Mercurial > hg > CbC > CbC_gcc
view gcc/tree-vect-loop.c @ 128:fe568345ddd5
fix CbC-example
| author | mir3636 |
|---|---|
| date | Wed, 11 Apr 2018 19:32:28 +0900 |
| parents | 04ced10e8804 |
| children | 84e7813d76e9 |
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/* Loop Vectorization Copyright (C) 2003-2017 Free Software Foundation, Inc. Contributed by Dorit Naishlos <dorit@il.ibm.com> and Ira Rosen <irar@il.ibm.com> This file is part of GCC. GCC is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version. GCC is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with GCC; see the file COPYING3. If not see <http://www.gnu.org/licenses/>. */ #include "config.h" #include "system.h" #include "coretypes.h" #include "backend.h" #include "target.h" #include "rtl.h" #include "tree.h" #include "gimple.h" #include "cfghooks.h" #include "tree-pass.h" #include "ssa.h" #include "optabs-tree.h" #include "diagnostic-core.h" #include "fold-const.h" #include "stor-layout.h" #include "cfganal.h" #include "gimplify.h" #include "gimple-iterator.h" #include "gimplify-me.h" #include "tree-ssa-loop-ivopts.h" #include "tree-ssa-loop-manip.h" #include "tree-ssa-loop-niter.h" #include "tree-ssa-loop.h" #include "cfgloop.h" #include "params.h" #include "tree-scalar-evolution.h" #include "tree-vectorizer.h" #include "gimple-fold.h" #include "cgraph.h" #include "tree-cfg.h" #include "tree-if-conv.h" /* Loop Vectorization Pass. This pass tries to vectorize loops. For example, the vectorizer transforms the following simple loop: short a[N]; short b[N]; short c[N]; int i; for (i=0; i<N; i++){ a[i] = b[i] + c[i]; } as if it was manually vectorized by rewriting the source code into: typedef int __attribute__((mode(V8HI))) v8hi; short a[N]; short b[N]; short c[N]; int i; v8hi *pa = (v8hi*)a, *pb = (v8hi*)b, *pc = (v8hi*)c; v8hi va, vb, vc; for (i=0; i<N/8; i++){ vb = pb[i]; vc = pc[i]; va = vb + vc; pa[i] = va; } The main entry to this pass is vectorize_loops(), in which the vectorizer applies a set of analyses on a given set of loops, followed by the actual vectorization transformation for the loops that had successfully passed the analysis phase. Throughout this pass we make a distinction between two types of data: scalars (which are represented by SSA_NAMES), and memory references ("data-refs"). These two types of data require different handling both during analysis and transformation. The types of data-refs that the vectorizer currently supports are ARRAY_REFS which base is an array DECL (not a pointer), and INDIRECT_REFS through pointers; both array and pointer accesses are required to have a simple (consecutive) access pattern. Analysis phase: =============== The driver for the analysis phase is vect_analyze_loop(). It applies a set of analyses, some of which rely on the scalar evolution analyzer (scev) developed by Sebastian Pop. During the analysis phase the vectorizer records some information per stmt in a "stmt_vec_info" struct which is attached to each stmt in the loop, as well as general information about the loop as a whole, which is recorded in a "loop_vec_info" struct attached to each loop. Transformation phase: ===================== The loop transformation phase scans all the stmts in the loop, and creates a vector stmt (or a sequence of stmts) for each scalar stmt S in the loop that needs to be vectorized. It inserts the vector code sequence just before the scalar stmt S, and records a pointer to the vector code in STMT_VINFO_VEC_STMT (stmt_info) (stmt_info is the stmt_vec_info struct attached to S). This pointer will be used for the vectorization of following stmts which use the def of stmt S. Stmt S is removed if it writes to memory; otherwise, we rely on dead code elimination for removing it. For example, say stmt S1 was vectorized into stmt VS1: VS1: vb = px[i]; S1: b = x[i]; STMT_VINFO_VEC_STMT (stmt_info (S1)) = VS1 S2: a = b; To vectorize stmt S2, the vectorizer first finds the stmt that defines the operand 'b' (S1), and gets the relevant vector def 'vb' from the vector stmt VS1 pointed to by STMT_VINFO_VEC_STMT (stmt_info (S1)). The resulting sequence would be: VS1: vb = px[i]; S1: b = x[i]; STMT_VINFO_VEC_STMT (stmt_info (S1)) = VS1 VS2: va = vb; S2: a = b; STMT_VINFO_VEC_STMT (stmt_info (S2)) = VS2 Operands that are not SSA_NAMEs, are data-refs that appear in load/store operations (like 'x[i]' in S1), and are handled differently. Target modeling: ================= Currently the only target specific information that is used is the size of the vector (in bytes) - "TARGET_VECTORIZE_UNITS_PER_SIMD_WORD". Targets that can support different sizes of vectors, for now will need to specify one value for "TARGET_VECTORIZE_UNITS_PER_SIMD_WORD". More flexibility will be added in the future. Since we only vectorize operations which vector form can be expressed using existing tree codes, to verify that an operation is supported, the vectorizer checks the relevant optab at the relevant machine_mode (e.g, optab_handler (add_optab, V8HImode)). If the value found is CODE_FOR_nothing, then there's no target support, and we can't vectorize the stmt. For additional information on this project see: http://gcc.gnu.org/projects/tree-ssa/vectorization.html */ static void vect_estimate_min_profitable_iters (loop_vec_info, int *, int *); /* Function vect_determine_vectorization_factor Determine the vectorization factor (VF). VF is the number of data elements that are operated upon in parallel in a single iteration of the vectorized loop. For example, when vectorizing a loop that operates on 4byte elements, on a target with vector size (VS) 16byte, the VF is set to 4, since 4 elements can fit in a single vector register. We currently support vectorization of loops in which all types operated upon are of the same size. Therefore this function currently sets VF according to the size of the types operated upon, and fails if there are multiple sizes in the loop. VF is also the factor by which the loop iterations are strip-mined, e.g.: original loop: for (i=0; i<N; i++){ a[i] = b[i] + c[i]; } vectorized loop: for (i=0; i<N; i+=VF){ a[i:VF] = b[i:VF] + c[i:VF]; } */ static bool vect_determine_vectorization_factor (loop_vec_info loop_vinfo) { struct loop *loop = LOOP_VINFO_LOOP (loop_vinfo); basic_block *bbs = LOOP_VINFO_BBS (loop_vinfo); unsigned nbbs = loop->num_nodes; unsigned int vectorization_factor = 0; tree scalar_type = NULL_TREE; gphi *phi; tree vectype; unsigned int nunits; stmt_vec_info stmt_info; unsigned i; HOST_WIDE_INT dummy; gimple *stmt, *pattern_stmt = NULL; gimple_seq pattern_def_seq = NULL; gimple_stmt_iterator pattern_def_si = gsi_none (); bool analyze_pattern_stmt = false; bool bool_result; auto_vec<stmt_vec_info> mask_producers; if (dump_enabled_p ()) dump_printf_loc (MSG_NOTE, vect_location, "=== vect_determine_vectorization_factor ===\n"); for (i = 0; i < nbbs; i++) { basic_block bb = bbs[i]; for (gphi_iterator si = gsi_start_phis (bb); !gsi_end_p (si); gsi_next (&si)) { phi = si.phi (); stmt_info = vinfo_for_stmt (phi); if (dump_enabled_p ()) { dump_printf_loc (MSG_NOTE, vect_location, "==> examining phi: "); dump_gimple_stmt (MSG_NOTE, TDF_SLIM, phi, 0); } gcc_assert (stmt_info); if (STMT_VINFO_RELEVANT_P (stmt_info) || STMT_VINFO_LIVE_P (stmt_info)) { gcc_assert (!STMT_VINFO_VECTYPE (stmt_info)); scalar_type = TREE_TYPE (PHI_RESULT (phi)); if (dump_enabled_p ()) { dump_printf_loc (MSG_NOTE, vect_location, "get vectype for scalar type: "); dump_generic_expr (MSG_NOTE, TDF_SLIM, scalar_type); dump_printf (MSG_NOTE, "\n"); } vectype = get_vectype_for_scalar_type (scalar_type); if (!vectype) { if (dump_enabled_p ()) { dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location, "not vectorized: unsupported " "data-type "); dump_generic_expr (MSG_MISSED_OPTIMIZATION, TDF_SLIM, scalar_type); dump_printf (MSG_MISSED_OPTIMIZATION, "\n"); } return false; } STMT_VINFO_VECTYPE (stmt_info) = vectype; if (dump_enabled_p ()) { dump_printf_loc (MSG_NOTE, vect_location, "vectype: "); dump_generic_expr (MSG_NOTE, TDF_SLIM, vectype); dump_printf (MSG_NOTE, "\n"); } nunits = TYPE_VECTOR_SUBPARTS (vectype); if (dump_enabled_p ()) dump_printf_loc (MSG_NOTE, vect_location, "nunits = %d\n", nunits); if (!vectorization_factor || (nunits > vectorization_factor)) vectorization_factor = nunits; } } for (gimple_stmt_iterator si = gsi_start_bb (bb); !gsi_end_p (si) || analyze_pattern_stmt;) { tree vf_vectype; if (analyze_pattern_stmt) stmt = pattern_stmt; else stmt = gsi_stmt (si); stmt_info = vinfo_for_stmt (stmt); if (dump_enabled_p ()) { dump_printf_loc (MSG_NOTE, vect_location, "==> examining statement: "); dump_gimple_stmt (MSG_NOTE, TDF_SLIM, stmt, 0); } gcc_assert (stmt_info); /* Skip stmts which do not need to be vectorized. */ if ((!STMT_VINFO_RELEVANT_P (stmt_info) && !STMT_VINFO_LIVE_P (stmt_info)) || gimple_clobber_p (stmt)) { if (STMT_VINFO_IN_PATTERN_P (stmt_info) && (pattern_stmt = STMT_VINFO_RELATED_STMT (stmt_info)) && (STMT_VINFO_RELEVANT_P (vinfo_for_stmt (pattern_stmt)) || STMT_VINFO_LIVE_P (vinfo_for_stmt (pattern_stmt)))) { stmt = pattern_stmt; stmt_info = vinfo_for_stmt (pattern_stmt); if (dump_enabled_p ()) { dump_printf_loc (MSG_NOTE, vect_location, "==> examining pattern statement: "); dump_gimple_stmt (MSG_NOTE, TDF_SLIM, stmt, 0); } } else { if (dump_enabled_p ()) dump_printf_loc (MSG_NOTE, vect_location, "skip.\n"); gsi_next (&si); continue; } } else if (STMT_VINFO_IN_PATTERN_P (stmt_info) && (pattern_stmt = STMT_VINFO_RELATED_STMT (stmt_info)) && (STMT_VINFO_RELEVANT_P (vinfo_for_stmt (pattern_stmt)) || STMT_VINFO_LIVE_P (vinfo_for_stmt (pattern_stmt)))) analyze_pattern_stmt = true; /* If a pattern statement has def stmts, analyze them too. */ if (is_pattern_stmt_p (stmt_info)) { if (pattern_def_seq == NULL) { pattern_def_seq = STMT_VINFO_PATTERN_DEF_SEQ (stmt_info); pattern_def_si = gsi_start (pattern_def_seq); } else if (!gsi_end_p (pattern_def_si)) gsi_next (&pattern_def_si); if (pattern_def_seq != NULL) { gimple *pattern_def_stmt = NULL; stmt_vec_info pattern_def_stmt_info = NULL; while (!gsi_end_p (pattern_def_si)) { pattern_def_stmt = gsi_stmt (pattern_def_si); pattern_def_stmt_info = vinfo_for_stmt (pattern_def_stmt); if (STMT_VINFO_RELEVANT_P (pattern_def_stmt_info) || STMT_VINFO_LIVE_P (pattern_def_stmt_info)) break; gsi_next (&pattern_def_si); } if (!gsi_end_p (pattern_def_si)) { if (dump_enabled_p ()) { dump_printf_loc (MSG_NOTE, vect_location, "==> examining pattern def stmt: "); dump_gimple_stmt (MSG_NOTE, TDF_SLIM, pattern_def_stmt, 0); } stmt = pattern_def_stmt; stmt_info = pattern_def_stmt_info; } else { pattern_def_si = gsi_none (); analyze_pattern_stmt = false; } } else analyze_pattern_stmt = false; } if (gimple_get_lhs (stmt) == NULL_TREE /* MASK_STORE has no lhs, but is ok. */ && (!is_gimple_call (stmt) || !gimple_call_internal_p (stmt) || gimple_call_internal_fn (stmt) != IFN_MASK_STORE)) { if (is_gimple_call (stmt)) { /* Ignore calls with no lhs. These must be calls to #pragma omp simd functions, and what vectorization factor it really needs can't be determined until vectorizable_simd_clone_call. */ if (!analyze_pattern_stmt && gsi_end_p (pattern_def_si)) { pattern_def_seq = NULL; gsi_next (&si); } continue; } if (dump_enabled_p ()) { dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location, "not vectorized: irregular stmt."); dump_gimple_stmt (MSG_MISSED_OPTIMIZATION, TDF_SLIM, stmt, 0); } return false; } if (VECTOR_MODE_P (TYPE_MODE (gimple_expr_type (stmt)))) { if (dump_enabled_p ()) { dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location, "not vectorized: vector stmt in loop:"); dump_gimple_stmt (MSG_MISSED_OPTIMIZATION, TDF_SLIM, stmt, 0); } return false; } bool_result = false; if (STMT_VINFO_VECTYPE (stmt_info)) { /* The only case when a vectype had been already set is for stmts that contain a dataref, or for "pattern-stmts" (stmts generated by the vectorizer to represent/replace a certain idiom). */ gcc_assert (STMT_VINFO_DATA_REF (stmt_info) || is_pattern_stmt_p (stmt_info) || !gsi_end_p (pattern_def_si)); vectype = STMT_VINFO_VECTYPE (stmt_info); } else { gcc_assert (!STMT_VINFO_DATA_REF (stmt_info)); if (gimple_call_internal_p (stmt, IFN_MASK_STORE)) scalar_type = TREE_TYPE (gimple_call_arg (stmt, 3)); else scalar_type = TREE_TYPE (gimple_get_lhs (stmt)); /* Bool ops don't participate in vectorization factor computation. For comparison use compared types to compute a factor. */ if (VECT_SCALAR_BOOLEAN_TYPE_P (scalar_type) && is_gimple_assign (stmt) && gimple_assign_rhs_code (stmt) != COND_EXPR) { if (STMT_VINFO_RELEVANT_P (stmt_info) || STMT_VINFO_LIVE_P (stmt_info)) mask_producers.safe_push (stmt_info); bool_result = true; if (TREE_CODE_CLASS (gimple_assign_rhs_code (stmt)) == tcc_comparison && !VECT_SCALAR_BOOLEAN_TYPE_P (TREE_TYPE (gimple_assign_rhs1 (stmt)))) scalar_type = TREE_TYPE (gimple_assign_rhs1 (stmt)); else { if (!analyze_pattern_stmt && gsi_end_p (pattern_def_si)) { pattern_def_seq = NULL; gsi_next (&si); } continue; } } if (dump_enabled_p ()) { dump_printf_loc (MSG_NOTE, vect_location, "get vectype for scalar type: "); dump_generic_expr (MSG_NOTE, TDF_SLIM, scalar_type); dump_printf (MSG_NOTE, "\n"); } vectype = get_vectype_for_scalar_type (scalar_type); if (!vectype) { if (dump_enabled_p ()) { dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location, "not vectorized: unsupported " "data-type "); dump_generic_expr (MSG_MISSED_OPTIMIZATION, TDF_SLIM, scalar_type); dump_printf (MSG_MISSED_OPTIMIZATION, "\n"); } return false; } if (!bool_result) STMT_VINFO_VECTYPE (stmt_info) = vectype; if (dump_enabled_p ()) { dump_printf_loc (MSG_NOTE, vect_location, "vectype: "); dump_generic_expr (MSG_NOTE, TDF_SLIM, vectype); dump_printf (MSG_NOTE, "\n"); } } /* Don't try to compute VF out scalar types if we stmt produces boolean vector. Use result vectype instead. */ if (VECTOR_BOOLEAN_TYPE_P (vectype)) vf_vectype = vectype; else { /* The vectorization factor is according to the smallest scalar type (or the largest vector size, but we only support one vector size per loop). */ if (!bool_result) scalar_type = vect_get_smallest_scalar_type (stmt, &dummy, &dummy); if (dump_enabled_p ()) { dump_printf_loc (MSG_NOTE, vect_location, "get vectype for scalar type: "); dump_generic_expr (MSG_NOTE, TDF_SLIM, scalar_type); dump_printf (MSG_NOTE, "\n"); } vf_vectype = get_vectype_for_scalar_type (scalar_type); } if (!vf_vectype) { if (dump_enabled_p ()) { dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location, "not vectorized: unsupported data-type "); dump_generic_expr (MSG_MISSED_OPTIMIZATION, TDF_SLIM, scalar_type); dump_printf (MSG_MISSED_OPTIMIZATION, "\n"); } return false; } if ((GET_MODE_SIZE (TYPE_MODE (vectype)) != GET_MODE_SIZE (TYPE_MODE (vf_vectype)))) { if (dump_enabled_p ()) { dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location, "not vectorized: different sized vector " "types in statement, "); dump_generic_expr (MSG_MISSED_OPTIMIZATION, TDF_SLIM, vectype); dump_printf (MSG_MISSED_OPTIMIZATION, " and "); dump_generic_expr (MSG_MISSED_OPTIMIZATION, TDF_SLIM, vf_vectype); dump_printf (MSG_MISSED_OPTIMIZATION, "\n"); } return false; } if (dump_enabled_p ()) { dump_printf_loc (MSG_NOTE, vect_location, "vectype: "); dump_generic_expr (MSG_NOTE, TDF_SLIM, vf_vectype); dump_printf (MSG_NOTE, "\n"); } nunits = TYPE_VECTOR_SUBPARTS (vf_vectype); if (dump_enabled_p ()) dump_printf_loc (MSG_NOTE, vect_location, "nunits = %d\n", nunits); if (!vectorization_factor || (nunits > vectorization_factor)) vectorization_factor = nunits; if (!analyze_pattern_stmt && gsi_end_p (pattern_def_si)) { pattern_def_seq = NULL; gsi_next (&si); } } } /* TODO: Analyze cost. Decide if worth while to vectorize. */ if (dump_enabled_p ()) dump_printf_loc (MSG_NOTE, vect_location, "vectorization factor = %d\n", vectorization_factor); if (vectorization_factor <= 1) { if (dump_enabled_p ()) dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location, "not vectorized: unsupported data-type\n"); return false; } LOOP_VINFO_VECT_FACTOR (loop_vinfo) = vectorization_factor; for (i = 0; i < mask_producers.length (); i++) { tree mask_type = NULL; stmt = STMT_VINFO_STMT (mask_producers[i]); if (is_gimple_assign (stmt) && TREE_CODE_CLASS (gimple_assign_rhs_code (stmt)) == tcc_comparison && !VECT_SCALAR_BOOLEAN_TYPE_P (TREE_TYPE (gimple_assign_rhs1 (stmt)))) { scalar_type = TREE_TYPE (gimple_assign_rhs1 (stmt)); mask_type = get_mask_type_for_scalar_type (scalar_type); if (!mask_type) { if (dump_enabled_p ()) dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location, "not vectorized: unsupported mask\n"); return false; } } else { tree rhs; ssa_op_iter iter; gimple *def_stmt; enum vect_def_type dt; FOR_EACH_SSA_TREE_OPERAND (rhs, stmt, iter, SSA_OP_USE) { if (!vect_is_simple_use (rhs, mask_producers[i]->vinfo, &def_stmt, &dt, &vectype)) { if (dump_enabled_p ()) { dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location, "not vectorized: can't compute mask type " "for statement, "); dump_gimple_stmt (MSG_MISSED_OPTIMIZATION, TDF_SLIM, stmt, 0); } return false; } /* No vectype probably means external definition. Allow it in case there is another operand which allows to determine mask type. */ if (!vectype) continue; if (!mask_type) mask_type = vectype; else if (TYPE_VECTOR_SUBPARTS (mask_type) != TYPE_VECTOR_SUBPARTS (vectype)) { if (dump_enabled_p ()) { dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location, "not vectorized: different sized masks " "types in statement, "); dump_generic_expr (MSG_MISSED_OPTIMIZATION, TDF_SLIM, mask_type); dump_printf (MSG_MISSED_OPTIMIZATION, " and "); dump_generic_expr (MSG_MISSED_OPTIMIZATION, TDF_SLIM, vectype); dump_printf (MSG_MISSED_OPTIMIZATION, "\n"); } return false; } else if (VECTOR_BOOLEAN_TYPE_P (mask_type) != VECTOR_BOOLEAN_TYPE_P (vectype)) { if (dump_enabled_p ()) { dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location, "not vectorized: mixed mask and " "nonmask vector types in statement, "); dump_generic_expr (MSG_MISSED_OPTIMIZATION, TDF_SLIM, mask_type); dump_printf (MSG_MISSED_OPTIMIZATION, " and "); dump_generic_expr (MSG_MISSED_OPTIMIZATION, TDF_SLIM, vectype); dump_printf (MSG_MISSED_OPTIMIZATION, "\n"); } return false; } } /* We may compare boolean value loaded as vector of integers. Fix mask_type in such case. */ if (mask_type && !VECTOR_BOOLEAN_TYPE_P (mask_type) && gimple_code (stmt) == GIMPLE_ASSIGN && TREE_CODE_CLASS (gimple_assign_rhs_code (stmt)) == tcc_comparison) mask_type = build_same_sized_truth_vector_type (mask_type); } /* No mask_type should mean loop invariant predicate. This is probably a subject for optimization in if-conversion. */ if (!mask_type) { if (dump_enabled_p ()) { dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location, "not vectorized: can't compute mask type " "for statement, "); dump_gimple_stmt (MSG_MISSED_OPTIMIZATION, TDF_SLIM, stmt, 0); } return false; } STMT_VINFO_VECTYPE (mask_producers[i]) = mask_type; } return true; } /* Function vect_is_simple_iv_evolution. FORNOW: A simple evolution of an induction variables in the loop is considered a polynomial evolution. */ static bool vect_is_simple_iv_evolution (unsigned loop_nb, tree access_fn, tree * init, tree * step) { tree init_expr; tree step_expr; tree evolution_part = evolution_part_in_loop_num (access_fn, loop_nb); basic_block bb; /* When there is no evolution in this loop, the evolution function is not "simple". */ if (evolution_part == NULL_TREE) return false; /* When the evolution is a polynomial of degree >= 2 the evolution function is not "simple". */ if (tree_is_chrec (evolution_part)) return false; step_expr = evolution_part; init_expr = unshare_expr (initial_condition_in_loop_num (access_fn, loop_nb)); if (dump_enabled_p ()) { dump_printf_loc (MSG_NOTE, vect_location, "step: "); dump_generic_expr (MSG_NOTE, TDF_SLIM, step_expr); dump_printf (MSG_NOTE, ", init: "); dump_generic_expr (MSG_NOTE, TDF_SLIM, init_expr); dump_printf (MSG_NOTE, "\n"); } *init = init_expr; *step = step_expr; if (TREE_CODE (step_expr) != INTEGER_CST && (TREE_CODE (step_expr) != SSA_NAME || ((bb = gimple_bb (SSA_NAME_DEF_STMT (step_expr))) && flow_bb_inside_loop_p (get_loop (cfun, loop_nb), bb)) || (!INTEGRAL_TYPE_P (TREE_TYPE (step_expr)) && (!SCALAR_FLOAT_TYPE_P (TREE_TYPE (step_expr)) || !flag_associative_math))) && (TREE_CODE (step_expr) != REAL_CST || !flag_associative_math)) { if (dump_enabled_p ()) dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location, "step unknown.\n"); return false; } return true; } /* Function vect_analyze_scalar_cycles_1. Examine the cross iteration def-use cycles of scalar variables in LOOP. LOOP_VINFO represents the loop that is now being considered for vectorization (can be LOOP, or an outer-loop enclosing LOOP). */ static void vect_analyze_scalar_cycles_1 (loop_vec_info loop_vinfo, struct loop *loop) { basic_block bb = loop->header; tree init, step; auto_vec<gimple *, 64> worklist; gphi_iterator gsi; bool double_reduc; if (dump_enabled_p ()) dump_printf_loc (MSG_NOTE, vect_location, "=== vect_analyze_scalar_cycles ===\n"); /* First - identify all inductions. Reduction detection assumes that all the inductions have been identified, therefore, this order must not be changed. */ for (gsi = gsi_start_phis (bb); !gsi_end_p (gsi); gsi_next (&gsi)) { gphi *phi = gsi.phi (); tree access_fn = NULL; tree def = PHI_RESULT (phi); stmt_vec_info stmt_vinfo = vinfo_for_stmt (phi); if (dump_enabled_p ()) { dump_printf_loc (MSG_NOTE, vect_location, "Analyze phi: "); dump_gimple_stmt (MSG_NOTE, TDF_SLIM, phi, 0); } /* Skip virtual phi's. The data dependences that are associated with virtual defs/uses (i.e., memory accesses) are analyzed elsewhere. */ if (virtual_operand_p (def)) continue; STMT_VINFO_DEF_TYPE (stmt_vinfo) = vect_unknown_def_type; /* Analyze the evolution function. */ access_fn = analyze_scalar_evolution (loop, def); if (access_fn) { STRIP_NOPS (access_fn); if (dump_enabled_p ()) { dump_printf_loc (MSG_NOTE, vect_location, "Access function of PHI: "); dump_generic_expr (MSG_NOTE, TDF_SLIM, access_fn); dump_printf (MSG_NOTE, "\n"); } STMT_VINFO_LOOP_PHI_EVOLUTION_BASE_UNCHANGED (stmt_vinfo) = initial_condition_in_loop_num (access_fn, loop->num); STMT_VINFO_LOOP_PHI_EVOLUTION_PART (stmt_vinfo) = evolution_part_in_loop_num (access_fn, loop->num); } if (!access_fn || !vect_is_simple_iv_evolution (loop->num, access_fn, &init, &step) || (LOOP_VINFO_LOOP (loop_vinfo) != loop && TREE_CODE (step) != INTEGER_CST)) { worklist.safe_push (phi); continue; } gcc_assert (STMT_VINFO_LOOP_PHI_EVOLUTION_BASE_UNCHANGED (stmt_vinfo) != NULL_TREE); gcc_assert (STMT_VINFO_LOOP_PHI_EVOLUTION_PART (stmt_vinfo) != NULL_TREE); if (dump_enabled_p ()) dump_printf_loc (MSG_NOTE, vect_location, "Detected induction.\n"); STMT_VINFO_DEF_TYPE (stmt_vinfo) = vect_induction_def; } /* Second - identify all reductions and nested cycles. */ while (worklist.length () > 0) { gimple *phi = worklist.pop (); tree def = PHI_RESULT (phi); stmt_vec_info stmt_vinfo = vinfo_for_stmt (phi); gimple *reduc_stmt; if (dump_enabled_p ()) { dump_printf_loc (MSG_NOTE, vect_location, "Analyze phi: "); dump_gimple_stmt (MSG_NOTE, TDF_SLIM, phi, 0); } gcc_assert (!virtual_operand_p (def) && STMT_VINFO_DEF_TYPE (stmt_vinfo) == vect_unknown_def_type); reduc_stmt = vect_force_simple_reduction (loop_vinfo, phi, &double_reduc, false); if (reduc_stmt) { if (double_reduc) { if (dump_enabled_p ()) dump_printf_loc (MSG_NOTE, vect_location, "Detected double reduction.\n"); STMT_VINFO_DEF_TYPE (stmt_vinfo) = vect_double_reduction_def; STMT_VINFO_DEF_TYPE (vinfo_for_stmt (reduc_stmt)) = vect_double_reduction_def; } else { if (loop != LOOP_VINFO_LOOP (loop_vinfo)) { if (dump_enabled_p ()) dump_printf_loc (MSG_NOTE, vect_location, "Detected vectorizable nested cycle.\n"); STMT_VINFO_DEF_TYPE (stmt_vinfo) = vect_nested_cycle; STMT_VINFO_DEF_TYPE (vinfo_for_stmt (reduc_stmt)) = vect_nested_cycle; } else { if (dump_enabled_p ()) dump_printf_loc (MSG_NOTE, vect_location, "Detected reduction.\n"); STMT_VINFO_DEF_TYPE (stmt_vinfo) = vect_reduction_def; STMT_VINFO_DEF_TYPE (vinfo_for_stmt (reduc_stmt)) = vect_reduction_def; /* Store the reduction cycles for possible vectorization in loop-aware SLP if it was not detected as reduction chain. */ if (! GROUP_FIRST_ELEMENT (vinfo_for_stmt (reduc_stmt))) LOOP_VINFO_REDUCTIONS (loop_vinfo).safe_push (reduc_stmt); } } } else if (dump_enabled_p ()) dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location, "Unknown def-use cycle pattern.\n"); } } /* Function vect_analyze_scalar_cycles. Examine the cross iteration def-use cycles of scalar variables, by analyzing the loop-header PHIs of scalar variables. Classify each cycle as one of the following: invariant, induction, reduction, unknown. We do that for the loop represented by LOOP_VINFO, and also to its inner-loop, if exists. Examples for scalar cycles: Example1: reduction: loop1: for (i=0; i<N; i++) sum += a[i]; Example2: induction: loop2: for (i=0; i<N; i++) a[i] = i; */ static void vect_analyze_scalar_cycles (loop_vec_info loop_vinfo) { struct loop *loop = LOOP_VINFO_LOOP (loop_vinfo); vect_analyze_scalar_cycles_1 (loop_vinfo, loop); /* When vectorizing an outer-loop, the inner-loop is executed sequentially. Reductions in such inner-loop therefore have different properties than the reductions in the nest that gets vectorized: 1. When vectorized, they are executed in the same order as in the original scalar loop, so we can't change the order of computation when vectorizing them. 2. FIXME: Inner-loop reductions can be used in the inner-loop, so the current checks are too strict. */ if (loop->inner) vect_analyze_scalar_cycles_1 (loop_vinfo, loop->inner); } /* Transfer group and reduction information from STMT to its pattern stmt. */ static void vect_fixup_reduc_chain (gimple *stmt) { gimple *firstp = STMT_VINFO_RELATED_STMT (vinfo_for_stmt (stmt)); gimple *stmtp; gcc_assert (!GROUP_FIRST_ELEMENT (vinfo_for_stmt (firstp)) && GROUP_FIRST_ELEMENT (vinfo_for_stmt (stmt))); GROUP_SIZE (vinfo_for_stmt (firstp)) = GROUP_SIZE (vinfo_for_stmt (stmt)); do { stmtp = STMT_VINFO_RELATED_STMT (vinfo_for_stmt (stmt)); GROUP_FIRST_ELEMENT (vinfo_for_stmt (stmtp)) = firstp; stmt = GROUP_NEXT_ELEMENT (vinfo_for_stmt (stmt)); if (stmt) GROUP_NEXT_ELEMENT (vinfo_for_stmt (stmtp)) = STMT_VINFO_RELATED_STMT (vinfo_for_stmt (stmt)); } while (stmt); STMT_VINFO_DEF_TYPE (vinfo_for_stmt (stmtp)) = vect_reduction_def; } /* Fixup scalar cycles that now have their stmts detected as patterns. */ static void vect_fixup_scalar_cycles_with_patterns (loop_vec_info loop_vinfo) { gimple *first; unsigned i; FOR_EACH_VEC_ELT (LOOP_VINFO_REDUCTION_CHAINS (loop_vinfo), i, first) if (STMT_VINFO_IN_PATTERN_P (vinfo_for_stmt (first))) { gimple *next = GROUP_NEXT_ELEMENT (vinfo_for_stmt (first)); while (next) { if (! STMT_VINFO_IN_PATTERN_P (vinfo_for_stmt (next))) break; next = GROUP_NEXT_ELEMENT (vinfo_for_stmt (next)); } /* If not all stmt in the chain are patterns try to handle the chain without patterns. */ if (! next) { vect_fixup_reduc_chain (first); LOOP_VINFO_REDUCTION_CHAINS (loop_vinfo)[i] = STMT_VINFO_RELATED_STMT (vinfo_for_stmt (first)); } } } /* Function vect_get_loop_niters. Determine how many iterations the loop is executed and place it in NUMBER_OF_ITERATIONS. Place the number of latch iterations in NUMBER_OF_ITERATIONSM1. Place the condition under which the niter information holds in ASSUMPTIONS. Return the loop exit condition. */ static gcond * vect_get_loop_niters (struct loop *loop, tree *assumptions, tree *number_of_iterations, tree *number_of_iterationsm1) { edge exit = single_exit (loop); struct tree_niter_desc niter_desc; tree niter_assumptions, niter, may_be_zero; gcond *cond = get_loop_exit_condition (loop); *assumptions = boolean_true_node; *number_of_iterationsm1 = chrec_dont_know; *number_of_iterations = chrec_dont_know; if (dump_enabled_p ()) dump_printf_loc (MSG_NOTE, vect_location, "=== get_loop_niters ===\n"); if (!exit) return cond; niter = chrec_dont_know; may_be_zero = NULL_TREE; niter_assumptions = boolean_true_node; if (!number_of_iterations_exit_assumptions (loop, exit, &niter_desc, NULL) || chrec_contains_undetermined (niter_desc.niter)) return cond; niter_assumptions = niter_desc.assumptions; may_be_zero = niter_desc.may_be_zero; niter = niter_desc.niter; if (may_be_zero && integer_zerop (may_be_zero)) may_be_zero = NULL_TREE; if (may_be_zero) { if (COMPARISON_CLASS_P (may_be_zero)) { /* Try to combine may_be_zero with assumptions, this can simplify computation of niter expression. */ if (niter_assumptions && !integer_nonzerop (niter_assumptions)) niter_assumptions = fold_build2 (TRUTH_AND_EXPR, boolean_type_node, niter_assumptions, fold_build1 (TRUTH_NOT_EXPR, boolean_type_node, may_be_zero)); else niter = fold_build3 (COND_EXPR, TREE_TYPE (niter), may_be_zero, build_int_cst (TREE_TYPE (niter), 0), niter); may_be_zero = NULL_TREE; } else if (integer_nonzerop (may_be_zero)) { *number_of_iterationsm1 = build_int_cst (TREE_TYPE (niter), 0); *number_of_iterations = build_int_cst (TREE_TYPE (niter), 1); return cond; } else return cond; } *assumptions = niter_assumptions; *number_of_iterationsm1 = niter; /* We want the number of loop header executions which is the number of latch executions plus one. ??? For UINT_MAX latch executions this number overflows to zero for loops like do { n++; } while (n != 0); */ if (niter && !chrec_contains_undetermined (niter)) niter = fold_build2 (PLUS_EXPR, TREE_TYPE (niter), unshare_expr (niter), build_int_cst (TREE_TYPE (niter), 1)); *number_of_iterations = niter; return cond; } /* Function bb_in_loop_p Used as predicate for dfs order traversal of the loop bbs. */ static bool bb_in_loop_p (const_basic_block bb, const void *data) { const struct loop *const loop = (const struct loop *)data; if (flow_bb_inside_loop_p (loop, bb)) return true; return false; } /* Create and initialize a new loop_vec_info struct for LOOP_IN, as well as stmt_vec_info structs for all the stmts in LOOP_IN. */ _loop_vec_info::_loop_vec_info (struct loop *loop_in) : vec_info (vec_info::loop, init_cost (loop_in)), loop (loop_in), bbs (XCNEWVEC (basic_block, loop->num_nodes)), num_itersm1 (NULL_TREE), num_iters (NULL_TREE), num_iters_unchanged (NULL_TREE), num_iters_assumptions (NULL_TREE), th (0), vectorization_factor (0), max_vectorization_factor (0), unaligned_dr (NULL), peeling_for_alignment (0), ptr_mask (0), slp_unrolling_factor (1), single_scalar_iteration_cost (0), vectorizable (false), peeling_for_gaps (false), peeling_for_niter (false), operands_swapped (false), no_data_dependencies (false), has_mask_store (false), scalar_loop (NULL), orig_loop_info (NULL) { /* Create/Update stmt_info for all stmts in the loop. */ basic_block *body = get_loop_body (loop); for (unsigned int i = 0; i < loop->num_nodes; i++) { basic_block bb = body[i]; gimple_stmt_iterator si; for (si = gsi_start_phis (bb); !gsi_end_p (si); gsi_next (&si)) { gimple *phi = gsi_stmt (si); gimple_set_uid (phi, 0); set_vinfo_for_stmt (phi, new_stmt_vec_info (phi, this)); } for (si = gsi_start_bb (bb); !gsi_end_p (si); gsi_next (&si)) { gimple *stmt = gsi_stmt (si); gimple_set_uid (stmt, 0); set_vinfo_for_stmt (stmt, new_stmt_vec_info (stmt, this)); } } free (body); /* CHECKME: We want to visit all BBs before their successors (except for latch blocks, for which this assertion wouldn't hold). In the simple case of the loop forms we allow, a dfs order of the BBs would the same as reversed postorder traversal, so we are safe. */ unsigned int nbbs = dfs_enumerate_from (loop->header, 0, bb_in_loop_p, bbs, loop->num_nodes, loop); gcc_assert (nbbs == loop->num_nodes); } /* Free all memory used by the _loop_vec_info, as well as all the stmt_vec_info structs of all the stmts in the loop. */ _loop_vec_info::~_loop_vec_info () { int nbbs; gimple_stmt_iterator si; int j; nbbs = loop->num_nodes; for (j = 0; j < nbbs; j++) { basic_block bb = bbs[j]; for (si = gsi_start_phis (bb); !gsi_end_p (si); gsi_next (&si)) free_stmt_vec_info (gsi_stmt (si)); for (si = gsi_start_bb (bb); !gsi_end_p (si); ) { gimple *stmt = gsi_stmt (si); /* We may have broken canonical form by moving a constant into RHS1 of a commutative op. Fix such occurrences. */ if (operands_swapped && is_gimple_assign (stmt)) { enum tree_code code = gimple_assign_rhs_code (stmt); if ((code == PLUS_EXPR || code == POINTER_PLUS_EXPR || code == MULT_EXPR) && CONSTANT_CLASS_P (gimple_assign_rhs1 (stmt))) swap_ssa_operands (stmt, gimple_assign_rhs1_ptr (stmt), gimple_assign_rhs2_ptr (stmt)); else if (code == COND_EXPR && CONSTANT_CLASS_P (gimple_assign_rhs2 (stmt))) { tree cond_expr = gimple_assign_rhs1 (stmt); enum tree_code cond_code = TREE_CODE (cond_expr); if (TREE_CODE_CLASS (cond_code) == tcc_comparison) { bool honor_nans = HONOR_NANS (TREE_OPERAND (cond_expr, 0)); cond_code = invert_tree_comparison (cond_code, honor_nans); if (cond_code != ERROR_MARK) { TREE_SET_CODE (cond_expr, cond_code); swap_ssa_operands (stmt, gimple_assign_rhs2_ptr (stmt), gimple_assign_rhs3_ptr (stmt)); } } } } /* Free stmt_vec_info. */ free_stmt_vec_info (stmt); gsi_next (&si); } } free (bbs); loop->aux = NULL; } /* Calculate the cost of one scalar iteration of the loop. */ static void vect_compute_single_scalar_iteration_cost (loop_vec_info loop_vinfo) { struct loop *loop = LOOP_VINFO_LOOP (loop_vinfo); basic_block *bbs = LOOP_VINFO_BBS (loop_vinfo); int nbbs = loop->num_nodes, factor, scalar_single_iter_cost = 0; int innerloop_iters, i; /* Count statements in scalar loop. Using this as scalar cost for a single iteration for now. TODO: Add outer loop support. TODO: Consider assigning different costs to different scalar statements. */ /* FORNOW. */ innerloop_iters = 1; if (loop->inner) innerloop_iters = 50; /* FIXME */ for (i = 0; i < nbbs; i++) { gimple_stmt_iterator si; basic_block bb = bbs[i]; if (bb->loop_father == loop->inner) factor = innerloop_iters; else factor = 1; for (si = gsi_start_bb (bb); !gsi_end_p (si); gsi_next (&si)) { gimple *stmt = gsi_stmt (si); stmt_vec_info stmt_info = vinfo_for_stmt (stmt); if (!is_gimple_assign (stmt) && !is_gimple_call (stmt)) continue; /* Skip stmts that are not vectorized inside the loop. */ if (stmt_info && !STMT_VINFO_RELEVANT_P (stmt_info) && (!STMT_VINFO_LIVE_P (stmt_info) || !VECTORIZABLE_CYCLE_DEF (STMT_VINFO_DEF_TYPE (stmt_info))) && !STMT_VINFO_IN_PATTERN_P (stmt_info)) continue; vect_cost_for_stmt kind; if (STMT_VINFO_DATA_REF (stmt_info)) { if (DR_IS_READ (STMT_VINFO_DATA_REF (stmt_info))) kind = scalar_load; else kind = scalar_store; } else kind = scalar_stmt; scalar_single_iter_cost += record_stmt_cost (&LOOP_VINFO_SCALAR_ITERATION_COST (loop_vinfo), factor, kind, stmt_info, 0, vect_prologue); } } LOOP_VINFO_SINGLE_SCALAR_ITERATION_COST (loop_vinfo) = scalar_single_iter_cost; } /* Function vect_analyze_loop_form_1. Verify that certain CFG restrictions hold, including: - the loop has a pre-header - the loop has a single entry and exit - the loop exit condition is simple enough - the number of iterations can be analyzed, i.e, a countable loop. The niter could be analyzed under some assumptions. */ bool vect_analyze_loop_form_1 (struct loop *loop, gcond **loop_cond, tree *assumptions, tree *number_of_iterationsm1, tree *number_of_iterations, gcond **inner_loop_cond) { if (dump_enabled_p ()) dump_printf_loc (MSG_NOTE, vect_location, "=== vect_analyze_loop_form ===\n"); /* Different restrictions apply when we are considering an inner-most loop, vs. an outer (nested) loop. (FORNOW. May want to relax some of these restrictions in the future). */ if (!loop->inner) { /* Inner-most loop. We currently require that the number of BBs is exactly 2 (the header and latch). Vectorizable inner-most loops look like this: (pre-header) | header <--------+ | | | | +--> latch --+ | (exit-bb) */ if (loop->num_nodes != 2) { if (dump_enabled_p ()) dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location, "not vectorized: control flow in loop.\n"); return false; } if (empty_block_p (loop->header)) { if (dump_enabled_p ()) dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location, "not vectorized: empty loop.\n"); return false; } } else { struct loop *innerloop = loop->inner; edge entryedge; /* Nested loop. We currently require that the loop is doubly-nested, contains a single inner loop, and the number of BBs is exactly 5. Vectorizable outer-loops look like this: (pre-header) | header <---+ | | inner-loop | | | tail ------+ | (exit-bb) The inner-loop has the properties expected of inner-most loops as described above. */ if ((loop->inner)->inner || (loop->inner)->next) { if (dump_enabled_p ()) dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location, "not vectorized: multiple nested loops.\n"); return false; } if (loop->num_nodes != 5) { if (dump_enabled_p ()) dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location, "not vectorized: control flow in loop.\n"); return false; } entryedge = loop_preheader_edge (innerloop); if (entryedge->src != loop->header || !single_exit (innerloop) || single_exit (innerloop)->dest != EDGE_PRED (loop->latch, 0)->src) { if (dump_enabled_p ()) dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location, "not vectorized: unsupported outerloop form.\n"); return false; } /* Analyze the inner-loop. */ tree inner_niterm1, inner_niter, inner_assumptions; if (! vect_analyze_loop_form_1 (loop->inner, inner_loop_cond, &inner_assumptions, &inner_niterm1, &inner_niter, NULL) /* Don't support analyzing niter under assumptions for inner loop. */ || !integer_onep (inner_assumptions)) { if (dump_enabled_p ()) dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location, "not vectorized: Bad inner loop.\n"); return false; } if (!expr_invariant_in_loop_p (loop, inner_niter)) { if (dump_enabled_p ()) dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location, "not vectorized: inner-loop count not" " invariant.\n"); return false; } if (dump_enabled_p ()) dump_printf_loc (MSG_NOTE, vect_location, "Considering outer-loop vectorization.\n"); } if (!single_exit (loop) || EDGE_COUNT (loop->header->preds) != 2) { if (dump_enabled_p ()) { if (!single_exit (loop)) dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location, "not vectorized: multiple exits.\n"); else if (EDGE_COUNT (loop->header->preds) != 2) dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location, "not vectorized: too many incoming edges.\n"); } return false; } /* We assume that the loop exit condition is at the end of the loop. i.e, that the loop is represented as a do-while (with a proper if-guard before the loop if needed), where the loop header contains all the executable statements, and the latch is empty. */ if (!empty_block_p (loop->latch) || !gimple_seq_empty_p (phi_nodes (loop->latch))) { if (dump_enabled_p ()) dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location, "not vectorized: latch block not empty.\n"); return false; } /* Make sure the exit is not abnormal. */ edge e = single_exit (loop); if (e->flags & EDGE_ABNORMAL) { if (dump_enabled_p ()) dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location, "not vectorized: abnormal loop exit edge.\n"); return false; } *loop_cond = vect_get_loop_niters (loop, assumptions, number_of_iterations, number_of_iterationsm1); if (!*loop_cond) { if (dump_enabled_p ()) dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location, "not vectorized: complicated exit condition.\n"); return false; } if (integer_zerop (*assumptions) || !*number_of_iterations || chrec_contains_undetermined (*number_of_iterations)) { if (dump_enabled_p ()) dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location, "not vectorized: number of iterations cannot be " "computed.\n"); return false; } if (integer_zerop (*number_of_iterations)) { if (dump_enabled_p ()) dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location, "not vectorized: number of iterations = 0.\n"); return false; } return true; } /* Analyze LOOP form and return a loop_vec_info if it is of suitable form. */ loop_vec_info vect_analyze_loop_form (struct loop *loop) { tree assumptions, number_of_iterations, number_of_iterationsm1; gcond *loop_cond, *inner_loop_cond = NULL; if (! vect_analyze_loop_form_1 (loop, &loop_cond, &assumptions, &number_of_iterationsm1, &number_of_iterations, &inner_loop_cond)) return NULL; loop_vec_info loop_vinfo = new _loop_vec_info (loop); LOOP_VINFO_NITERSM1 (loop_vinfo) = number_of_iterationsm1; LOOP_VINFO_NITERS (loop_vinfo) = number_of_iterations; LOOP_VINFO_NITERS_UNCHANGED (loop_vinfo) = number_of_iterations; if (!integer_onep (assumptions)) { /* We consider to vectorize this loop by versioning it under some assumptions. In order to do this, we need to clear existing information computed by scev and niter analyzer. */ scev_reset_htab (); free_numbers_of_iterations_estimates (loop); /* Also set flag for this loop so that following scev and niter analysis are done under the assumptions. */ loop_constraint_set (loop, LOOP_C_FINITE); /* Also record the assumptions for versioning. */ LOOP_VINFO_NITERS_ASSUMPTIONS (loop_vinfo) = assumptions; } if (!LOOP_VINFO_NITERS_KNOWN_P (loop_vinfo)) { if (dump_enabled_p ()) { dump_printf_loc (MSG_NOTE, vect_location, "Symbolic number of iterations is "); dump_generic_expr (MSG_NOTE, TDF_DETAILS, number_of_iterations); dump_printf (MSG_NOTE, "\n"); } } STMT_VINFO_TYPE (vinfo_for_stmt (loop_cond)) = loop_exit_ctrl_vec_info_type; if (inner_loop_cond) STMT_VINFO_TYPE (vinfo_for_stmt (inner_loop_cond)) = loop_exit_ctrl_vec_info_type; gcc_assert (!loop->aux); loop->aux = loop_vinfo; return loop_vinfo; } /* Scan the loop stmts and dependent on whether there are any (non-)SLP statements update the vectorization factor. */ static void vect_update_vf_for_slp (loop_vec_info loop_vinfo) { struct loop *loop = LOOP_VINFO_LOOP (loop_vinfo); basic_block *bbs = LOOP_VINFO_BBS (loop_vinfo); int nbbs = loop->num_nodes; unsigned int vectorization_factor; int i; if (dump_enabled_p ()) dump_printf_loc (MSG_NOTE, vect_location, "=== vect_update_vf_for_slp ===\n"); vectorization_factor = LOOP_VINFO_VECT_FACTOR (loop_vinfo); gcc_assert (vectorization_factor != 0); /* If all the stmts in the loop can be SLPed, we perform only SLP, and vectorization factor of the loop is the unrolling factor required by the SLP instances. If that unrolling factor is 1, we say, that we perform pure SLP on loop - cross iteration parallelism is not exploited. */ bool only_slp_in_loop = true; for (i = 0; i < nbbs; i++) { basic_block bb = bbs[i]; for (gimple_stmt_iterator si = gsi_start_bb (bb); !gsi_end_p (si); gsi_next (&si)) { gimple *stmt = gsi_stmt (si); stmt_vec_info stmt_info = vinfo_for_stmt (stmt); if (STMT_VINFO_IN_PATTERN_P (stmt_info) && STMT_VINFO_RELATED_STMT (stmt_info)) { stmt = STMT_VINFO_RELATED_STMT (stmt_info); stmt_info = vinfo_for_stmt (stmt); } if ((STMT_VINFO_RELEVANT_P (stmt_info) || VECTORIZABLE_CYCLE_DEF (STMT_VINFO_DEF_TYPE (stmt_info))) && !PURE_SLP_STMT (stmt_info)) /* STMT needs both SLP and loop-based vectorization. */ only_slp_in_loop = false; } } if (only_slp_in_loop) { dump_printf_loc (MSG_NOTE, vect_location, "Loop contains only SLP stmts\n"); vectorization_factor = LOOP_VINFO_SLP_UNROLLING_FACTOR (loop_vinfo); } else { dump_printf_loc (MSG_NOTE, vect_location, "Loop contains SLP and non-SLP stmts\n"); vectorization_factor = least_common_multiple (vectorization_factor, LOOP_VINFO_SLP_UNROLLING_FACTOR (loop_vinfo)); } LOOP_VINFO_VECT_FACTOR (loop_vinfo) = vectorization_factor; if (dump_enabled_p ()) dump_printf_loc (MSG_NOTE, vect_location, "Updating vectorization factor to %d\n", vectorization_factor); } /* Function vect_analyze_loop_operations. Scan the loop stmts and make sure they are all vectorizable. */ static bool vect_analyze_loop_operations (loop_vec_info loop_vinfo) { struct loop *loop = LOOP_VINFO_LOOP (loop_vinfo); basic_block *bbs = LOOP_VINFO_BBS (loop_vinfo); int nbbs = loop->num_nodes; int i; stmt_vec_info stmt_info; bool need_to_vectorize = false; bool ok; if (dump_enabled_p ()) dump_printf_loc (MSG_NOTE, vect_location, "=== vect_analyze_loop_operations ===\n"); for (i = 0; i < nbbs; i++) { basic_block bb = bbs[i]; for (gphi_iterator si = gsi_start_phis (bb); !gsi_end_p (si); gsi_next (&si)) { gphi *phi = si.phi (); ok = true; stmt_info = vinfo_for_stmt (phi); if (dump_enabled_p ()) { dump_printf_loc (MSG_NOTE, vect_location, "examining phi: "); dump_gimple_stmt (MSG_NOTE, TDF_SLIM, phi, 0); } if (virtual_operand_p (gimple_phi_result (phi))) continue; /* Inner-loop loop-closed exit phi in outer-loop vectorization (i.e., a phi in the tail of the outer-loop). */ if (! is_loop_header_bb_p (bb)) { /* FORNOW: we currently don't support the case that these phis are not used in the outerloop (unless it is double reduction, i.e., this phi is vect_reduction_def), cause this case requires to actually do something here. */ if (STMT_VINFO_LIVE_P (stmt_info) && STMT_VINFO_DEF_TYPE (stmt_info) != vect_double_reduction_def) { if (dump_enabled_p ()) dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location, "Unsupported loop-closed phi in " "outer-loop.\n"); return false; } /* If PHI is used in the outer loop, we check that its operand is defined in the inner loop. */ if (STMT_VINFO_RELEVANT_P (stmt_info)) { tree phi_op; gimple *op_def_stmt; if (gimple_phi_num_args (phi) != 1) return false; phi_op = PHI_ARG_DEF (phi, 0); if (TREE_CODE (phi_op) != SSA_NAME) return false; op_def_stmt = SSA_NAME_DEF_STMT (phi_op); if (gimple_nop_p (op_def_stmt) || !flow_bb_inside_loop_p (loop, gimple_bb (op_def_stmt)) || !vinfo_for_stmt (op_def_stmt)) return false; if (STMT_VINFO_RELEVANT (vinfo_for_stmt (op_def_stmt)) != vect_used_in_outer && STMT_VINFO_RELEVANT (vinfo_for_stmt (op_def_stmt)) != vect_used_in_outer_by_reduction) return false; } continue; } gcc_assert (stmt_info); if ((STMT_VINFO_RELEVANT (stmt_info) == vect_used_in_scope || STMT_VINFO_LIVE_P (stmt_info)) && STMT_VINFO_DEF_TYPE (stmt_info) != vect_induction_def) { /* A scalar-dependence cycle that we don't support. */ if (dump_enabled_p ()) dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location, "not vectorized: scalar dependence cycle.\n"); return false; } if (STMT_VINFO_RELEVANT_P (stmt_info)) { need_to_vectorize = true; if (STMT_VINFO_DEF_TYPE (stmt_info) == vect_induction_def && ! PURE_SLP_STMT (stmt_info)) ok = vectorizable_induction (phi, NULL, NULL, NULL); else if ((STMT_VINFO_DEF_TYPE (stmt_info) == vect_reduction_def || STMT_VINFO_DEF_TYPE (stmt_info) == vect_nested_cycle) && ! PURE_SLP_STMT (stmt_info)) ok = vectorizable_reduction (phi, NULL, NULL, NULL, NULL); } if (ok && STMT_VINFO_LIVE_P (stmt_info)) ok = vectorizable_live_operation (phi, NULL, NULL, -1, NULL); if (!ok) { if (dump_enabled_p ()) { dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location, "not vectorized: relevant phi not " "supported: "); dump_gimple_stmt (MSG_MISSED_OPTIMIZATION, TDF_SLIM, phi, 0); } return false; } } for (gimple_stmt_iterator si = gsi_start_bb (bb); !gsi_end_p (si); gsi_next (&si)) { gimple *stmt = gsi_stmt (si); if (!gimple_clobber_p (stmt) && !vect_analyze_stmt (stmt, &need_to_vectorize, NULL, NULL)) return false; } } /* bbs */ /* All operations in the loop are either irrelevant (deal with loop control, or dead), or only used outside the loop and can be moved out of the loop (e.g. invariants, inductions). The loop can be optimized away by scalar optimizations. We're better off not touching this loop. */ if (!need_to_vectorize) { if (dump_enabled_p ()) dump_printf_loc (MSG_NOTE, vect_location, "All the computation can be taken out of the loop.\n"); if (dump_enabled_p ()) dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location, "not vectorized: redundant loop. no profit to " "vectorize.\n"); return false; } return true; } /* Function vect_analyze_loop_2. Apply a set of analyses on LOOP, and create a loop_vec_info struct for it. The different analyses will record information in the loop_vec_info struct. */ static bool vect_analyze_loop_2 (loop_vec_info loop_vinfo, bool &fatal) { bool ok; int max_vf = MAX_VECTORIZATION_FACTOR; int min_vf = 2; unsigned int n_stmts = 0; /* The first group of checks is independent of the vector size. */ fatal = true; /* Find all data references in the loop (which correspond to vdefs/vuses) and analyze their evolution in the loop. */ basic_block *bbs = LOOP_VINFO_BBS (loop_vinfo); loop_p loop = LOOP_VINFO_LOOP (loop_vinfo); if (!find_loop_nest (loop, &LOOP_VINFO_LOOP_NEST (loop_vinfo))) { if (dump_enabled_p ()) dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location, "not vectorized: loop nest containing two " "or more consecutive inner loops cannot be " "vectorized\n"); return false; } for (unsigned i = 0; i < loop->num_nodes; i++) for (gimple_stmt_iterator gsi = gsi_start_bb (bbs[i]); !gsi_end_p (gsi); gsi_next (&gsi)) { gimple *stmt = gsi_stmt (gsi); if (is_gimple_debug (stmt)) continue; ++n_stmts; if (!find_data_references_in_stmt (loop, stmt, &LOOP_VINFO_DATAREFS (loop_vinfo))) { if (is_gimple_call (stmt) && loop->safelen) { tree fndecl = gimple_call_fndecl (stmt), op; if (fndecl != NULL_TREE) { cgraph_node *node = cgraph_node::get (fndecl); if (node != NULL && node->simd_clones != NULL) { unsigned int j, n = gimple_call_num_args (stmt); for (j = 0; j < n; j++) { op = gimple_call_arg (stmt, j); if (DECL_P (op) || (REFERENCE_CLASS_P (op) && get_base_address (op))) break; } op = gimple_call_lhs (stmt); /* Ignore #pragma omp declare simd functions if they don't have data references in the call stmt itself. */ if (j == n && !(op && (DECL_P (op) || (REFERENCE_CLASS_P (op) && get_base_address (op))))) continue; } } } if (dump_enabled_p ()) dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location, "not vectorized: loop contains function " "calls or data references that cannot " "be analyzed\n"); return false; } } /* Analyze the data references and also adjust the minimal vectorization factor according to the loads and stores. */ ok = vect_analyze_data_refs (loop_vinfo, &min_vf); if (!ok) { if (dump_enabled_p ()) dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location, "bad data references.\n"); return false; } /* Classify all cross-iteration scalar data-flow cycles. Cross-iteration cycles caused by virtual phis are analyzed separately. */ vect_analyze_scalar_cycles (loop_vinfo); vect_pattern_recog (loop_vinfo); vect_fixup_scalar_cycles_with_patterns (loop_vinfo); /* Analyze the access patterns of the data-refs in the loop (consecutive, complex, etc.). FORNOW: Only handle consecutive access pattern. */ ok = vect_analyze_data_ref_accesses (loop_vinfo); if (!ok) { if (dump_enabled_p ()) dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location, "bad data access.\n"); return false; } /* Data-flow analysis to detect stmts that do not need to be vectorized. */ ok = vect_mark_stmts_to_be_vectorized (loop_vinfo); if (!ok) { if (dump_enabled_p ()) dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location, "unexpected pattern.\n"); return false; } /* While the rest of the analysis below depends on it in some way. */ fatal = false; /* Analyze data dependences between the data-refs in the loop and adjust the maximum vectorization factor according to the dependences. FORNOW: fail at the first data dependence that we encounter. */ ok = vect_analyze_data_ref_dependences (loop_vinfo, &max_vf); if (!ok || max_vf < min_vf) { if (dump_enabled_p ()) dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location, "bad data dependence.\n"); return false; } LOOP_VINFO_MAX_VECT_FACTOR (loop_vinfo) = max_vf; ok = vect_determine_vectorization_factor (loop_vinfo); if (!ok) { if (dump_enabled_p ()) dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location, "can't determine vectorization factor.\n"); return false; } if (max_vf < LOOP_VINFO_VECT_FACTOR (loop_vinfo)) { if (dump_enabled_p ()) dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location, "bad data dependence.\n"); return false; } /* Compute the scalar iteration cost. */ vect_compute_single_scalar_iteration_cost (loop_vinfo); int saved_vectorization_factor = LOOP_VINFO_VECT_FACTOR (loop_vinfo); HOST_WIDE_INT estimated_niter; unsigned th; int min_scalar_loop_bound; /* Check the SLP opportunities in the loop, analyze and build SLP trees. */ ok = vect_analyze_slp (loop_vinfo, n_stmts); if (!ok) return false; /* If there are any SLP instances mark them as pure_slp. */ bool slp = vect_make_slp_decision (loop_vinfo); if (slp) { /* Find stmts that need to be both vectorized and SLPed. */ vect_detect_hybrid_slp (loop_vinfo); /* Update the vectorization factor based on the SLP decision. */ vect_update_vf_for_slp (loop_vinfo); } /* This is the point where we can re-start analysis with SLP forced off. */ start_over: /* Now the vectorization factor is final. */ unsigned vectorization_factor = LOOP_VINFO_VECT_FACTOR (loop_vinfo); gcc_assert (vectorization_factor != 0); if (LOOP_VINFO_NITERS_KNOWN_P (loop_vinfo) && dump_enabled_p ()) dump_printf_loc (MSG_NOTE, vect_location, "vectorization_factor = %d, niters = " HOST_WIDE_INT_PRINT_DEC "\n", vectorization_factor, LOOP_VINFO_INT_NITERS (loop_vinfo)); HOST_WIDE_INT max_niter = likely_max_stmt_executions_int (LOOP_VINFO_LOOP (loop_vinfo)); if ((LOOP_VINFO_NITERS_KNOWN_P (loop_vinfo) && (LOOP_VINFO_INT_NITERS (loop_vinfo) < vectorization_factor)) || (max_niter != -1 && (unsigned HOST_WIDE_INT) max_niter < vectorization_factor)) { if (dump_enabled_p ()) dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location, "not vectorized: iteration count smaller than " "vectorization factor.\n"); return false; } /* Analyze the alignment of the data-refs in the loop. Fail if a data reference is found that cannot be vectorized. */ ok = vect_analyze_data_refs_alignment (loop_vinfo); if (!ok) { if (dump_enabled_p ()) dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location, "bad data alignment.\n"); return false; } /* Prune the list of ddrs to be tested at run-time by versioning for alias. It is important to call pruning after vect_analyze_data_ref_accesses, since we use grouping information gathered by interleaving analysis. */ ok = vect_prune_runtime_alias_test_list (loop_vinfo); if (!ok) return false; /* Do not invoke vect_enhance_data_refs_alignment for eplilogue vectorization. */ if (!LOOP_VINFO_EPILOGUE_P (loop_vinfo)) { /* This pass will decide on using loop versioning and/or loop peeling in order to enhance the alignment of data references in the loop. */ ok = vect_enhance_data_refs_alignment (loop_vinfo); if (!ok) { if (dump_enabled_p ()) dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location, "bad data alignment.\n"); return false; } } if (slp) { /* Analyze operations in the SLP instances. Note this may remove unsupported SLP instances which makes the above SLP kind detection invalid. */ unsigned old_size = LOOP_VINFO_SLP_INSTANCES (loop_vinfo).length (); vect_slp_analyze_operations (loop_vinfo); if (LOOP_VINFO_SLP_INSTANCES (loop_vinfo).length () != old_size) goto again; } /* Scan all the remaining operations in the loop that are not subject to SLP and make sure they are vectorizable. */ ok = vect_analyze_loop_operations (loop_vinfo); if (!ok) { if (dump_enabled_p ()) dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location, "bad operation or unsupported loop bound.\n"); return false; } /* If epilog loop is required because of data accesses with gaps, one additional iteration needs to be peeled. Check if there is enough iterations for vectorization. */ if (LOOP_VINFO_PEELING_FOR_GAPS (loop_vinfo) && LOOP_VINFO_NITERS_KNOWN_P (loop_vinfo)) { int vf = LOOP_VINFO_VECT_FACTOR (loop_vinfo); tree scalar_niters = LOOP_VINFO_NITERSM1 (loop_vinfo); if (wi::to_widest (scalar_niters) < vf) { if (dump_enabled_p ()) dump_printf_loc (MSG_NOTE, vect_location, "loop has no enough iterations to support" " peeling for gaps.\n"); return false; } } /* Analyze cost. Decide if worth while to vectorize. */ int min_profitable_estimate, min_profitable_iters; vect_estimate_min_profitable_iters (loop_vinfo, &min_profitable_iters, &min_profitable_estimate); if (min_profitable_iters < 0) { if (dump_enabled_p ()) dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location, "not vectorized: vectorization not profitable.\n"); if (dump_enabled_p ()) dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location, "not vectorized: vector version will never be " "profitable.\n"); goto again; } min_scalar_loop_bound = (PARAM_VALUE (PARAM_MIN_VECT_LOOP_BOUND) * vectorization_factor); /* Use the cost model only if it is more conservative than user specified threshold. */ th = (unsigned) MAX (min_scalar_loop_bound, min_profitable_iters); LOOP_VINFO_COST_MODEL_THRESHOLD (loop_vinfo) = th; if (LOOP_VINFO_NITERS_KNOWN_P (loop_vinfo) && LOOP_VINFO_INT_NITERS (loop_vinfo) < th) { if (dump_enabled_p ()) dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location, "not vectorized: vectorization not profitable.\n"); if (dump_enabled_p ()) dump_printf_loc (MSG_NOTE, vect_location, "not vectorized: iteration count smaller than user " "specified loop bound parameter or minimum profitable " "iterations (whichever is more conservative).\n"); goto again; } estimated_niter = estimated_stmt_executions_int (LOOP_VINFO_LOOP (loop_vinfo)); if (estimated_niter == -1) estimated_niter = max_niter; if (estimated_niter != -1 && ((unsigned HOST_WIDE_INT) estimated_niter < MAX (th, (unsigned) min_profitable_estimate))) { if (dump_enabled_p ()) dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location, "not vectorized: estimated iteration count too " "small.\n"); if (dump_enabled_p ()) dump_printf_loc (MSG_NOTE, vect_location, "not vectorized: estimated iteration count smaller " "than specified loop bound parameter or minimum " "profitable iterations (whichever is more " "conservative).\n"); goto again; } /* Decide whether we need to create an epilogue loop to handle remaining scalar iterations. */ th = ((LOOP_VINFO_COST_MODEL_THRESHOLD (loop_vinfo) / LOOP_VINFO_VECT_FACTOR (loop_vinfo)) * LOOP_VINFO_VECT_FACTOR (loop_vinfo)); if (LOOP_VINFO_NITERS_KNOWN_P (loop_vinfo) && LOOP_VINFO_PEELING_FOR_ALIGNMENT (loop_vinfo) > 0) { if (ctz_hwi (LOOP_VINFO_INT_NITERS (loop_vinfo) - LOOP_VINFO_PEELING_FOR_ALIGNMENT (loop_vinfo)) < exact_log2 (LOOP_VINFO_VECT_FACTOR (loop_vinfo))) LOOP_VINFO_PEELING_FOR_NITER (loop_vinfo) = true; } else if (LOOP_VINFO_PEELING_FOR_ALIGNMENT (loop_vinfo) || (tree_ctz (LOOP_VINFO_NITERS (loop_vinfo)) < (unsigned)exact_log2 (LOOP_VINFO_VECT_FACTOR (loop_vinfo)) /* In case of versioning, check if the maximum number of iterations is greater than th. If they are identical, the epilogue is unnecessary. */ && (!LOOP_REQUIRES_VERSIONING (loop_vinfo) || (unsigned HOST_WIDE_INT) max_niter > th))) LOOP_VINFO_PEELING_FOR_NITER (loop_vinfo) = true; /* If an epilogue loop is required make sure we can create one. */ if (LOOP_VINFO_PEELING_FOR_GAPS (loop_vinfo) || LOOP_VINFO_PEELING_FOR_NITER (loop_vinfo)) { if (dump_enabled_p ()) dump_printf_loc (MSG_NOTE, vect_location, "epilog loop required\n"); if (!vect_can_advance_ivs_p (loop_vinfo) || !slpeel_can_duplicate_loop_p (LOOP_VINFO_LOOP (loop_vinfo), single_exit (LOOP_VINFO_LOOP (loop_vinfo)))) { if (dump_enabled_p ()) dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location, "not vectorized: can't create required " "epilog loop\n"); goto again; } } /* During peeling, we need to check if number of loop iterations is enough for both peeled prolog loop and vector loop. This check can be merged along with threshold check of loop versioning, so increase threshold for this case if necessary. */ if (LOOP_REQUIRES_VERSIONING (loop_vinfo) && (LOOP_VINFO_PEELING_FOR_GAPS (loop_vinfo) || LOOP_VINFO_PEELING_FOR_NITER (loop_vinfo))) { unsigned niters_th; /* Niters for peeled prolog loop. */ if (LOOP_VINFO_PEELING_FOR_ALIGNMENT (loop_vinfo) < 0) { struct data_reference *dr = LOOP_VINFO_UNALIGNED_DR (loop_vinfo); tree vectype = STMT_VINFO_VECTYPE (vinfo_for_stmt (DR_STMT (dr))); niters_th = TYPE_VECTOR_SUBPARTS (vectype) - 1; } else niters_th = LOOP_VINFO_PEELING_FOR_ALIGNMENT (loop_vinfo); /* Niters for at least one iteration of vectorized loop. */ niters_th += LOOP_VINFO_VECT_FACTOR (loop_vinfo); /* One additional iteration because of peeling for gap. */ if (LOOP_VINFO_PEELING_FOR_GAPS (loop_vinfo)) niters_th++; if (LOOP_VINFO_COST_MODEL_THRESHOLD (loop_vinfo) < niters_th) LOOP_VINFO_COST_MODEL_THRESHOLD (loop_vinfo) = niters_th; } gcc_assert (vectorization_factor == (unsigned)LOOP_VINFO_VECT_FACTOR (loop_vinfo)); /* Ok to vectorize! */ return true; again: /* Try again with SLP forced off but if we didn't do any SLP there is no point in re-trying. */ if (!slp) return false; /* If there are reduction chains re-trying will fail anyway. */ if (! LOOP_VINFO_REDUCTION_CHAINS (loop_vinfo).is_empty ()) return false; /* Likewise if the grouped loads or stores in the SLP cannot be handled via interleaving or lane instructions. */ slp_instance instance; slp_tree node; unsigned i, j; FOR_EACH_VEC_ELT (LOOP_VINFO_SLP_INSTANCES (loop_vinfo), i, instance) { stmt_vec_info vinfo; vinfo = vinfo_for_stmt (SLP_TREE_SCALAR_STMTS (SLP_INSTANCE_TREE (instance))[0]); if (! STMT_VINFO_GROUPED_ACCESS (vinfo)) continue; vinfo = vinfo_for_stmt (STMT_VINFO_GROUP_FIRST_ELEMENT (vinfo)); unsigned int size = STMT_VINFO_GROUP_SIZE (vinfo); tree vectype = STMT_VINFO_VECTYPE (vinfo); if (! vect_store_lanes_supported (vectype, size) && ! vect_grouped_store_supported (vectype, size)) return false; FOR_EACH_VEC_ELT (SLP_INSTANCE_LOADS (instance), j, node) { vinfo = vinfo_for_stmt (SLP_TREE_SCALAR_STMTS (node)[0]); vinfo = vinfo_for_stmt (STMT_VINFO_GROUP_FIRST_ELEMENT (vinfo)); bool single_element_p = !STMT_VINFO_GROUP_NEXT_ELEMENT (vinfo); size = STMT_VINFO_GROUP_SIZE (vinfo); vectype = STMT_VINFO_VECTYPE (vinfo); if (! vect_load_lanes_supported (vectype, size) && ! vect_grouped_load_supported (vectype, single_element_p, size)) return false; } } if (dump_enabled_p ()) dump_printf_loc (MSG_NOTE, vect_location, "re-trying with SLP disabled\n"); /* Roll back state appropriately. No SLP this time. */ slp = false; /* Restore vectorization factor as it were without SLP. */ LOOP_VINFO_VECT_FACTOR (loop_vinfo) = saved_vectorization_factor; /* Free the SLP instances. */ FOR_EACH_VEC_ELT (LOOP_VINFO_SLP_INSTANCES (loop_vinfo), j, instance) vect_free_slp_instance (instance); LOOP_VINFO_SLP_INSTANCES (loop_vinfo).release (); /* Reset SLP type to loop_vect on all stmts. */ for (i = 0; i < LOOP_VINFO_LOOP (loop_vinfo)->num_nodes; ++i) { basic_block bb = LOOP_VINFO_BBS (loop_vinfo)[i]; for (gimple_stmt_iterator si = gsi_start_phis (bb); !gsi_end_p (si); gsi_next (&si)) { stmt_vec_info stmt_info = vinfo_for_stmt (gsi_stmt (si)); STMT_SLP_TYPE (stmt_info) = loop_vect; } for (gimple_stmt_iterator si = gsi_start_bb (bb); !gsi_end_p (si); gsi_next (&si)) { stmt_vec_info stmt_info = vinfo_for_stmt (gsi_stmt (si)); STMT_SLP_TYPE (stmt_info) = loop_vect; if (STMT_VINFO_IN_PATTERN_P (stmt_info)) { stmt_info = vinfo_for_stmt (STMT_VINFO_RELATED_STMT (stmt_info)); STMT_SLP_TYPE (stmt_info) = loop_vect; for (gimple_stmt_iterator pi = gsi_start (STMT_VINFO_PATTERN_DEF_SEQ (stmt_info)); !gsi_end_p (pi); gsi_next (&pi)) { gimple *pstmt = gsi_stmt (pi); STMT_SLP_TYPE (vinfo_for_stmt (pstmt)) = loop_vect; } } } } /* Free optimized alias test DDRS. */ LOOP_VINFO_COMP_ALIAS_DDRS (loop_vinfo).release (); LOOP_VINFO_CHECK_UNEQUAL_ADDRS (loop_vinfo).release (); /* Reset target cost data. */ destroy_cost_data (LOOP_VINFO_TARGET_COST_DATA (loop_vinfo)); LOOP_VINFO_TARGET_COST_DATA (loop_vinfo) = init_cost (LOOP_VINFO_LOOP (loop_vinfo)); /* Reset assorted flags. */ LOOP_VINFO_PEELING_FOR_NITER (loop_vinfo) = false; LOOP_VINFO_PEELING_FOR_GAPS (loop_vinfo) = false; LOOP_VINFO_COST_MODEL_THRESHOLD (loop_vinfo) = 0; goto start_over; } /* Function vect_analyze_loop. Apply a set of analyses on LOOP, and create a loop_vec_info struct for it. The different analyses will record information in the loop_vec_info struct. If ORIG_LOOP_VINFO is not NULL epilogue must be vectorized. */ loop_vec_info vect_analyze_loop (struct loop *loop, loop_vec_info orig_loop_vinfo) { loop_vec_info loop_vinfo; unsigned int vector_sizes; /* Autodetect first vector size we try. */ current_vector_size = 0; vector_sizes = targetm.vectorize.autovectorize_vector_sizes (); if (dump_enabled_p ()) dump_printf_loc (MSG_NOTE, vect_location, "===== analyze_loop_nest =====\n"); if (loop_outer (loop) && loop_vec_info_for_loop (loop_outer (loop)) && LOOP_VINFO_VECTORIZABLE_P (loop_vec_info_for_loop (loop_outer (loop)))) { if (dump_enabled_p ()) dump_printf_loc (MSG_NOTE, vect_location, "outer-loop already vectorized.\n"); return NULL; } while (1) { /* Check the CFG characteristics of the loop (nesting, entry/exit). */ loop_vinfo = vect_analyze_loop_form (loop); if (!loop_vinfo) { if (dump_enabled_p ()) dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location, "bad loop form.\n"); return NULL; } bool fatal = false; if (orig_loop_vinfo) LOOP_VINFO_ORIG_LOOP_INFO (loop_vinfo) = orig_loop_vinfo; if (vect_analyze_loop_2 (loop_vinfo, fatal)) { LOOP_VINFO_VECTORIZABLE_P (loop_vinfo) = 1; return loop_vinfo; } delete loop_vinfo; vector_sizes &= ~current_vector_size; if (fatal || vector_sizes == 0 || current_vector_size == 0) return NULL; /* Try the next biggest vector size. */ current_vector_size = 1 << floor_log2 (vector_sizes); if (dump_enabled_p ()) dump_printf_loc (MSG_NOTE, vect_location, "***** Re-trying analysis with " "vector size %d\n", current_vector_size); } } /* Function reduction_code_for_scalar_code Input: CODE - tree_code of a reduction operations. Output: REDUC_CODE - the corresponding tree-code to be used to reduce the vector of partial results into a single scalar result, or ERROR_MARK if the operation is a supported reduction operation, but does not have such a tree-code. Return FALSE if CODE currently cannot be vectorized as reduction. */ static bool reduction_code_for_scalar_code (enum tree_code code, enum tree_code *reduc_code) { switch (code) { case MAX_EXPR: *reduc_code = REDUC_MAX_EXPR; return true; case MIN_EXPR: *reduc_code = REDUC_MIN_EXPR; return true; case PLUS_EXPR: *reduc_code = REDUC_PLUS_EXPR; return true; case MULT_EXPR: case MINUS_EXPR: case BIT_IOR_EXPR: case BIT_XOR_EXPR: case BIT_AND_EXPR: *reduc_code = ERROR_MARK; return true; default: return false; } } /* Error reporting helper for vect_is_simple_reduction below. GIMPLE statement STMT is printed with a message MSG. */ static void report_vect_op (dump_flags_t msg_type, gimple *stmt, const char *msg) { dump_printf_loc (msg_type, vect_location, "%s", msg); dump_gimple_stmt (msg_type, TDF_SLIM, stmt, 0); } /* Detect SLP reduction of the form: #a1 = phi <a5, a0> a2 = operation (a1) a3 = operation (a2) a4 = operation (a3) a5 = operation (a4) #a = phi <a5> PHI is the reduction phi node (#a1 = phi <a5, a0> above) FIRST_STMT is the first reduction stmt in the chain (a2 = operation (a1)). Return TRUE if a reduction chain was detected. */ static bool vect_is_slp_reduction (loop_vec_info loop_info, gimple *phi, gimple *first_stmt) { struct loop *loop = (gimple_bb (phi))->loop_father; struct loop *vect_loop = LOOP_VINFO_LOOP (loop_info); enum tree_code code; gimple *current_stmt = NULL, *loop_use_stmt = NULL, *first, *next_stmt; stmt_vec_info use_stmt_info, current_stmt_info; tree lhs; imm_use_iterator imm_iter; use_operand_p use_p; int nloop_uses, size = 0, n_out_of_loop_uses; bool found = false; if (loop != vect_loop) return false; lhs = PHI_RESULT (phi); code = gimple_assign_rhs_code (first_stmt); while (1) { nloop_uses = 0; n_out_of_loop_uses = 0; FOR_EACH_IMM_USE_FAST (use_p, imm_iter, lhs) { gimple *use_stmt = USE_STMT (use_p); if (is_gimple_debug (use_stmt)) continue; /* Check if we got back to the reduction phi. */ if (use_stmt == phi) { loop_use_stmt = use_stmt; found = true; break; } if (flow_bb_inside_loop_p (loop, gimple_bb (use_stmt))) { loop_use_stmt = use_stmt; nloop_uses++; } else n_out_of_loop_uses++; /* There are can be either a single use in the loop or two uses in phi nodes. */ if (nloop_uses > 1 || (n_out_of_loop_uses && nloop_uses)) return false; } if (found) break; /* We reached a statement with no loop uses. */ if (nloop_uses == 0) return false; /* This is a loop exit phi, and we haven't reached the reduction phi. */ if (gimple_code (loop_use_stmt) == GIMPLE_PHI) return false; if (!is_gimple_assign (loop_use_stmt) || code != gimple_assign_rhs_code (loop_use_stmt) || !flow_bb_inside_loop_p (loop, gimple_bb (loop_use_stmt))) return false; /* Insert USE_STMT into reduction chain. */ use_stmt_info = vinfo_for_stmt (loop_use_stmt); if (current_stmt) { current_stmt_info = vinfo_for_stmt (current_stmt); GROUP_NEXT_ELEMENT (current_stmt_info) = loop_use_stmt; GROUP_FIRST_ELEMENT (use_stmt_info) = GROUP_FIRST_ELEMENT (current_stmt_info); } else GROUP_FIRST_ELEMENT (use_stmt_info) = loop_use_stmt; lhs = gimple_assign_lhs (loop_use_stmt); current_stmt = loop_use_stmt; size++; } if (!found || loop_use_stmt != phi || size < 2) return false; /* Swap the operands, if needed, to make the reduction operand be the second operand. */ lhs = PHI_RESULT (phi); next_stmt = GROUP_FIRST_ELEMENT (vinfo_for_stmt (current_stmt)); while (next_stmt) { if (gimple_assign_rhs2 (next_stmt) == lhs) { tree op = gimple_assign_rhs1 (next_stmt); gimple *def_stmt = NULL; if (TREE_CODE (op) == SSA_NAME) def_stmt = SSA_NAME_DEF_STMT (op); /* Check that the other def is either defined in the loop ("vect_internal_def"), or it's an induction (defined by a loop-header phi-node). */ if (def_stmt && gimple_bb (def_stmt) && flow_bb_inside_loop_p (loop, gimple_bb (def_stmt)) && (is_gimple_assign (def_stmt) || is_gimple_call (def_stmt) || STMT_VINFO_DEF_TYPE (vinfo_for_stmt (def_stmt)) == vect_induction_def || (gimple_code (def_stmt) == GIMPLE_PHI && STMT_VINFO_DEF_TYPE (vinfo_for_stmt (def_stmt)) == vect_internal_def && !is_loop_header_bb_p (gimple_bb (def_stmt))))) { lhs = gimple_assign_lhs (next_stmt); next_stmt = GROUP_NEXT_ELEMENT (vinfo_for_stmt (next_stmt)); continue; } return false; } else { tree op = gimple_assign_rhs2 (next_stmt); gimple *def_stmt = NULL; if (TREE_CODE (op) == SSA_NAME) def_stmt = SSA_NAME_DEF_STMT (op); /* Check that the other def is either defined in the loop ("vect_internal_def"), or it's an induction (defined by a loop-header phi-node). */ if (def_stmt && gimple_bb (def_stmt) && flow_bb_inside_loop_p (loop, gimple_bb (def_stmt)) && (is_gimple_assign (def_stmt) || is_gimple_call (def_stmt) || STMT_VINFO_DEF_TYPE (vinfo_for_stmt (def_stmt)) == vect_induction_def || (gimple_code (def_stmt) == GIMPLE_PHI && STMT_VINFO_DEF_TYPE (vinfo_for_stmt (def_stmt)) == vect_internal_def && !is_loop_header_bb_p (gimple_bb (def_stmt))))) { if (dump_enabled_p ()) { dump_printf_loc (MSG_NOTE, vect_location, "swapping oprnds: "); dump_gimple_stmt (MSG_NOTE, TDF_SLIM, next_stmt, 0); } swap_ssa_operands (next_stmt, gimple_assign_rhs1_ptr (next_stmt), gimple_assign_rhs2_ptr (next_stmt)); update_stmt (next_stmt); if (CONSTANT_CLASS_P (gimple_assign_rhs1 (next_stmt))) LOOP_VINFO_OPERANDS_SWAPPED (loop_info) = true; } else return false; } lhs = gimple_assign_lhs (next_stmt); next_stmt = GROUP_NEXT_ELEMENT (vinfo_for_stmt (next_stmt)); } /* Save the chain for further analysis in SLP detection. */ first = GROUP_FIRST_ELEMENT (vinfo_for_stmt (current_stmt)); LOOP_VINFO_REDUCTION_CHAINS (loop_info).safe_push (first); GROUP_SIZE (vinfo_for_stmt (first)) = size; return true; } /* Function vect_is_simple_reduction (1) Detect a cross-iteration def-use cycle that represents a simple reduction computation. We look for the following pattern: loop_header: a1 = phi < a0, a2 > a3 = ... a2 = operation (a3, a1) or a3 = ... loop_header: a1 = phi < a0, a2 > a2 = operation (a3, a1) such that: 1. operation is commutative and associative and it is safe to change the order of the computation 2. no uses for a2 in the loop (a2 is used out of the loop) 3. no uses of a1 in the loop besides the reduction operation 4. no uses of a1 outside the loop. Conditions 1,4 are tested here. Conditions 2,3 are tested in vect_mark_stmts_to_be_vectorized. (2) Detect a cross-iteration def-use cycle in nested loops, i.e., nested cycles. (3) Detect cycles of phi nodes in outer-loop vectorization, i.e., double reductions: a1 = phi < a0, a2 > inner loop (def of a3) a2 = phi < a3 > (4) Detect condition expressions, ie: for (int i = 0; i < N; i++) if (a[i] < val) ret_val = a[i]; */ static gimple * vect_is_simple_reduction (loop_vec_info loop_info, gimple *phi, bool *double_reduc, bool need_wrapping_integral_overflow, enum vect_reduction_type *v_reduc_type) { struct loop *loop = (gimple_bb (phi))->loop_father; struct loop *vect_loop = LOOP_VINFO_LOOP (loop_info); gimple *def_stmt, *def1 = NULL, *def2 = NULL, *phi_use_stmt = NULL; enum tree_code orig_code, code; tree op1, op2, op3 = NULL_TREE, op4 = NULL_TREE; tree type; int nloop_uses; tree name; imm_use_iterator imm_iter; use_operand_p use_p; bool phi_def; *double_reduc = false; *v_reduc_type = TREE_CODE_REDUCTION; tree phi_name = PHI_RESULT (phi); /* ??? If there are no uses of the PHI result the inner loop reduction won't be detected as possibly double-reduction by vectorizable_reduction because that tries to walk the PHI arg from the preheader edge which can be constant. See PR60382. */ if (has_zero_uses (phi_name)) return NULL; nloop_uses = 0; FOR_EACH_IMM_USE_FAST (use_p, imm_iter, phi_name) { gimple *use_stmt = USE_STMT (use_p); if (is_gimple_debug (use_stmt)) continue; if (!flow_bb_inside_loop_p (loop, gimple_bb (use_stmt))) { if (dump_enabled_p ()) dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location, "intermediate value used outside loop.\n"); return NULL; } nloop_uses++; if (nloop_uses > 1) { if (dump_enabled_p ()) dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location, "reduction value used in loop.\n"); return NULL; } phi_use_stmt = use_stmt; } edge latch_e = loop_latch_edge (loop); tree loop_arg = PHI_ARG_DEF_FROM_EDGE (phi, latch_e); if (TREE_CODE (loop_arg) != SSA_NAME) { if (dump_enabled_p ()) { dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location, "reduction: not ssa_name: "); dump_generic_expr (MSG_MISSED_OPTIMIZATION, TDF_SLIM, loop_arg); dump_printf (MSG_MISSED_OPTIMIZATION, "\n"); } return NULL; } def_stmt = SSA_NAME_DEF_STMT (loop_arg); if (is_gimple_assign (def_stmt)) { name = gimple_assign_lhs (def_stmt); phi_def = false; } else if (gimple_code (def_stmt) == GIMPLE_PHI) { name = PHI_RESULT (def_stmt); phi_def = true; } else { if (dump_enabled_p ()) { dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location, "reduction: unhandled reduction operation: "); dump_gimple_stmt (MSG_MISSED_OPTIMIZATION, TDF_SLIM, def_stmt, 0); } return NULL; } if (! flow_bb_inside_loop_p (loop, gimple_bb (def_stmt))) return NULL; nloop_uses = 0; auto_vec<gphi *, 3> lcphis; FOR_EACH_IMM_USE_FAST (use_p, imm_iter, name) { gimple *use_stmt = USE_STMT (use_p); if (is_gimple_debug (use_stmt)) continue; if (flow_bb_inside_loop_p (loop, gimple_bb (use_stmt))) nloop_uses++; else /* We can have more than one loop-closed PHI. */ lcphis.safe_push (as_a <gphi *> (use_stmt)); if (nloop_uses > 1) { if (dump_enabled_p ()) dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location, "reduction used in loop.\n"); return NULL; } } /* If DEF_STMT is a phi node itself, we expect it to have a single argument defined in the inner loop. */ if (phi_def) { op1 = PHI_ARG_DEF (def_stmt, 0); if (gimple_phi_num_args (def_stmt) != 1 || TREE_CODE (op1) != SSA_NAME) { if (dump_enabled_p ()) dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location, "unsupported phi node definition.\n"); return NULL; } def1 = SSA_NAME_DEF_STMT (op1); if (gimple_bb (def1) && flow_bb_inside_loop_p (loop, gimple_bb (def_stmt)) && loop->inner && flow_bb_inside_loop_p (loop->inner, gimple_bb (def1)) && is_gimple_assign (def1) && flow_bb_inside_loop_p (loop->inner, gimple_bb (phi_use_stmt))) { if (dump_enabled_p ()) report_vect_op (MSG_NOTE, def_stmt, "detected double reduction: "); *double_reduc = true; return def_stmt; } return NULL; } /* If we are vectorizing an inner reduction we are executing that in the original order only in case we are not dealing with a double reduction. */ bool check_reduction = true; if (flow_loop_nested_p (vect_loop, loop)) { gphi *lcphi; unsigned i; check_reduction = false; FOR_EACH_VEC_ELT (lcphis, i, lcphi) FOR_EACH_IMM_USE_FAST (use_p, imm_iter, gimple_phi_result (lcphi)) { gimple *use_stmt = USE_STMT (use_p); if (is_gimple_debug (use_stmt)) continue; if (! flow_bb_inside_loop_p (vect_loop, gimple_bb (use_stmt))) check_reduction = true; } } bool nested_in_vect_loop = flow_loop_nested_p (vect_loop, loop); code = orig_code = gimple_assign_rhs_code (def_stmt); /* We can handle "res -= x[i]", which is non-associative by simply rewriting this into "res += -x[i]". Avoid changing gimple instruction for the first simple tests and only do this if we're allowed to change code at all. */ if (code == MINUS_EXPR && gimple_assign_rhs2 (def_stmt) != phi_name) code = PLUS_EXPR; if (code == COND_EXPR) { if (! nested_in_vect_loop) *v_reduc_type = COND_REDUCTION; op3 = gimple_assign_rhs1 (def_stmt); if (COMPARISON_CLASS_P (op3)) { op4 = TREE_OPERAND (op3, 1); op3 = TREE_OPERAND (op3, 0); } if (op3 == phi_name || op4 == phi_name) { if (dump_enabled_p ()) report_vect_op (MSG_MISSED_OPTIMIZATION, def_stmt, "reduction: condition depends on previous" " iteration: "); return NULL; } op1 = gimple_assign_rhs2 (def_stmt); op2 = gimple_assign_rhs3 (def_stmt); } else if (!commutative_tree_code (code) || !associative_tree_code (code)) { if (dump_enabled_p ()) report_vect_op (MSG_MISSED_OPTIMIZATION, def_stmt, "reduction: not commutative/associative: "); return NULL; } else if (get_gimple_rhs_class (code) == GIMPLE_BINARY_RHS) { op1 = gimple_assign_rhs1 (def_stmt); op2 = gimple_assign_rhs2 (def_stmt); } else { if (dump_enabled_p ()) report_vect_op (MSG_MISSED_OPTIMIZATION, def_stmt, "reduction: not handled operation: "); return NULL; } if (TREE_CODE (op1) != SSA_NAME && TREE_CODE (op2) != SSA_NAME) { if (dump_enabled_p ()) report_vect_op (MSG_MISSED_OPTIMIZATION, def_stmt, "reduction: both uses not ssa_names: "); return NULL; } type = TREE_TYPE (gimple_assign_lhs (def_stmt)); if ((TREE_CODE (op1) == SSA_NAME && !types_compatible_p (type,TREE_TYPE (op1))) || (TREE_CODE (op2) == SSA_NAME && !types_compatible_p (type, TREE_TYPE (op2))) || (op3 && TREE_CODE (op3) == SSA_NAME && !types_compatible_p (type, TREE_TYPE (op3))) || (op4 && TREE_CODE (op4) == SSA_NAME && !types_compatible_p (type, TREE_TYPE (op4)))) { if (dump_enabled_p ()) { dump_printf_loc (MSG_NOTE, vect_location, "reduction: multiple types: operation type: "); dump_generic_expr (MSG_NOTE, TDF_SLIM, type); dump_printf (MSG_NOTE, ", operands types: "); dump_generic_expr (MSG_NOTE, TDF_SLIM, TREE_TYPE (op1)); dump_printf (MSG_NOTE, ","); dump_generic_expr (MSG_NOTE, TDF_SLIM, TREE_TYPE (op2)); if (op3) { dump_printf (MSG_NOTE, ","); dump_generic_expr (MSG_NOTE, TDF_SLIM, TREE_TYPE (op3)); } if (op4) { dump_printf (MSG_NOTE, ","); dump_generic_expr (MSG_NOTE, TDF_SLIM, TREE_TYPE (op4)); } dump_printf (MSG_NOTE, "\n"); } return NULL; } /* Check that it's ok to change the order of the computation. Generally, when vectorizing a reduction we change the order of the computation. This may change the behavior of the program in some cases, so we need to check that this is ok. One exception is when vectorizing an outer-loop: the inner-loop is executed sequentially, and therefore vectorizing reductions in the inner-loop during outer-loop vectorization is safe. */ if (*v_reduc_type != COND_REDUCTION && check_reduction) { /* CHECKME: check for !flag_finite_math_only too? */ if (SCALAR_FLOAT_TYPE_P (type) && !flag_associative_math) { /* Changing the order of operations changes the semantics. */ if (dump_enabled_p ()) report_vect_op (MSG_MISSED_OPTIMIZATION, def_stmt, "reduction: unsafe fp math optimization: "); return NULL; } else if (INTEGRAL_TYPE_P (type)) { if (!operation_no_trapping_overflow (type, code)) { /* Changing the order of operations changes the semantics. */ if (dump_enabled_p ()) report_vect_op (MSG_MISSED_OPTIMIZATION, def_stmt, "reduction: unsafe int math optimization" " (overflow traps): "); return NULL; } if (need_wrapping_integral_overflow && !TYPE_OVERFLOW_WRAPS (type) && operation_can_overflow (code)) { /* Changing the order of operations changes the semantics. */ if (dump_enabled_p ()) report_vect_op (MSG_MISSED_OPTIMIZATION, def_stmt, "reduction: unsafe int math optimization" " (overflow doesn't wrap): "); return NULL; } } else if (SAT_FIXED_POINT_TYPE_P (type)) { /* Changing the order of operations changes the semantics. */ if (dump_enabled_p ()) report_vect_op (MSG_MISSED_OPTIMIZATION, def_stmt, "reduction: unsafe fixed-point math optimization: "); return NULL; } } /* Reduction is safe. We're dealing with one of the following: 1) integer arithmetic and no trapv 2) floating point arithmetic, and special flags permit this optimization 3) nested cycle (i.e., outer loop vectorization). */ if (TREE_CODE (op1) == SSA_NAME) def1 = SSA_NAME_DEF_STMT (op1); if (TREE_CODE (op2) == SSA_NAME) def2 = SSA_NAME_DEF_STMT (op2); if (code != COND_EXPR && ((!def1 || gimple_nop_p (def1)) && (!def2 || gimple_nop_p (def2)))) { if (dump_enabled_p ()) report_vect_op (MSG_NOTE, def_stmt, "reduction: no defs for operands: "); return NULL; } /* Check that one def is the reduction def, defined by PHI, the other def is either defined in the loop ("vect_internal_def"), or it's an induction (defined by a loop-header phi-node). */ if (def2 && def2 == phi && (code == COND_EXPR || !def1 || gimple_nop_p (def1) || !flow_bb_inside_loop_p (loop, gimple_bb (def1)) || (def1 && flow_bb_inside_loop_p (loop, gimple_bb (def1)) && (is_gimple_assign (def1) || is_gimple_call (def1) || STMT_VINFO_DEF_TYPE (vinfo_for_stmt (def1)) == vect_induction_def || (gimple_code (def1) == GIMPLE_PHI && STMT_VINFO_DEF_TYPE (vinfo_for_stmt (def1)) == vect_internal_def && !is_loop_header_bb_p (gimple_bb (def1))))))) { if (dump_enabled_p ()) report_vect_op (MSG_NOTE, def_stmt, "detected reduction: "); return def_stmt; } if (def1 && def1 == phi && (code == COND_EXPR || !def2 || gimple_nop_p (def2) || !flow_bb_inside_loop_p (loop, gimple_bb (def2)) || (def2 && flow_bb_inside_loop_p (loop, gimple_bb (def2)) && (is_gimple_assign (def2) || is_gimple_call (def2) || STMT_VINFO_DEF_TYPE (vinfo_for_stmt (def2)) == vect_induction_def || (gimple_code (def2) == GIMPLE_PHI && STMT_VINFO_DEF_TYPE (vinfo_for_stmt (def2)) == vect_internal_def && !is_loop_header_bb_p (gimple_bb (def2))))))) { if (! nested_in_vect_loop && orig_code != MINUS_EXPR) { /* Check if we can swap operands (just for simplicity - so that the rest of the code can assume that the reduction variable is always the last (second) argument). */ if (code == COND_EXPR) { /* Swap cond_expr by inverting the condition. */ tree cond_expr = gimple_assign_rhs1 (def_stmt); enum tree_code invert_code = ERROR_MARK; enum tree_code cond_code = TREE_CODE (cond_expr); if (TREE_CODE_CLASS (cond_code) == tcc_comparison) { bool honor_nans = HONOR_NANS (TREE_OPERAND (cond_expr, 0)); invert_code = invert_tree_comparison (cond_code, honor_nans); } if (invert_code != ERROR_MARK) { TREE_SET_CODE (cond_expr, invert_code); swap_ssa_operands (def_stmt, gimple_assign_rhs2_ptr (def_stmt), gimple_assign_rhs3_ptr (def_stmt)); } else { if (dump_enabled_p ()) report_vect_op (MSG_NOTE, def_stmt, "detected reduction: cannot swap operands " "for cond_expr"); return NULL; } } else swap_ssa_operands (def_stmt, gimple_assign_rhs1_ptr (def_stmt), gimple_assign_rhs2_ptr (def_stmt)); if (dump_enabled_p ()) report_vect_op (MSG_NOTE, def_stmt, "detected reduction: need to swap operands: "); if (CONSTANT_CLASS_P (gimple_assign_rhs1 (def_stmt))) LOOP_VINFO_OPERANDS_SWAPPED (loop_info) = true; } else { if (dump_enabled_p ()) report_vect_op (MSG_NOTE, def_stmt, "detected reduction: "); } return def_stmt; } /* Try to find SLP reduction chain. */ if (! nested_in_vect_loop && code != COND_EXPR && orig_code != MINUS_EXPR && vect_is_slp_reduction (loop_info, phi, def_stmt)) { if (dump_enabled_p ()) report_vect_op (MSG_NOTE, def_stmt, "reduction: detected reduction chain: "); return def_stmt; } /* Dissolve group eventually half-built by vect_is_slp_reduction. */ gimple *first = GROUP_FIRST_ELEMENT (vinfo_for_stmt (def_stmt)); while (first) { gimple *next = GROUP_NEXT_ELEMENT (vinfo_for_stmt (first)); GROUP_FIRST_ELEMENT (vinfo_for_stmt (first)) = NULL; GROUP_NEXT_ELEMENT (vinfo_for_stmt (first)) = NULL; first = next; } /* Look for the expression computing loop_arg from loop PHI result. */ auto_vec<std::pair<ssa_op_iter, use_operand_p> > path; auto_bitmap visited; tree lookfor = PHI_RESULT (phi); ssa_op_iter curri; use_operand_p curr = op_iter_init_phiuse (&curri, as_a <gphi *>(phi), SSA_OP_USE); while (USE_FROM_PTR (curr) != loop_arg) curr = op_iter_next_use (&curri); curri.i = curri.numops; do { path.safe_push (std::make_pair (curri, curr)); tree use = USE_FROM_PTR (curr); if (use == lookfor) break; gimple *def = SSA_NAME_DEF_STMT (use); if (gimple_nop_p (def) || ! flow_bb_inside_loop_p (loop, gimple_bb (def))) { pop: do { std::pair<ssa_op_iter, use_operand_p> x = path.pop (); curri = x.first; curr = x.second; do curr = op_iter_next_use (&curri); /* Skip already visited or non-SSA operands (from iterating over PHI args). */ while (curr != NULL_USE_OPERAND_P && (TREE_CODE (USE_FROM_PTR (curr)) != SSA_NAME || ! bitmap_set_bit (visited, SSA_NAME_VERSION (USE_FROM_PTR (curr))))); } while (curr == NULL_USE_OPERAND_P && ! path.is_empty ()); if (curr == NULL_USE_OPERAND_P) break; } else { if (gimple_code (def) == GIMPLE_PHI) curr = op_iter_init_phiuse (&curri, as_a <gphi *>(def), SSA_OP_USE); else curr = op_iter_init_use (&curri, def, SSA_OP_USE); while (curr != NULL_USE_OPERAND_P && (TREE_CODE (USE_FROM_PTR (curr)) != SSA_NAME || ! bitmap_set_bit (visited, SSA_NAME_VERSION (USE_FROM_PTR (curr))))) curr = op_iter_next_use (&curri); if (curr == NULL_USE_OPERAND_P) goto pop; } } while (1); if (dump_file && (dump_flags & TDF_DETAILS)) { dump_printf_loc (MSG_NOTE, vect_location, "reduction path: "); unsigned i; std::pair<ssa_op_iter, use_operand_p> *x; FOR_EACH_VEC_ELT (path, i, x) { dump_generic_expr (MSG_NOTE, TDF_SLIM, USE_FROM_PTR (x->second)); dump_printf (MSG_NOTE, " "); } dump_printf (MSG_NOTE, "\n"); } /* Check whether the reduction path detected is valid. */ bool fail = path.length () == 0; bool neg = false; for (unsigned i = 1; i < path.length (); ++i) { gimple *use_stmt = USE_STMT (path[i].second); tree op = USE_FROM_PTR (path[i].second); if (! has_single_use (op) || ! is_gimple_assign (use_stmt)) { fail = true; break; } if (gimple_assign_rhs_code (use_stmt) != code) { if (code == PLUS_EXPR && gimple_assign_rhs_code (use_stmt) == MINUS_EXPR) { /* Track whether we negate the reduction value each iteration. */ if (gimple_assign_rhs2 (use_stmt) == op) neg = ! neg; } else { fail = true; break; } } } if (! fail && ! neg) return def_stmt; if (dump_enabled_p ()) { report_vect_op (MSG_MISSED_OPTIMIZATION, def_stmt, "reduction: unknown pattern: "); } return NULL; } /* Wrapper around vect_is_simple_reduction, which will modify code in-place if it enables detection of more reductions. Arguments as there. */ gimple * vect_force_simple_reduction (loop_vec_info loop_info, gimple *phi, bool *double_reduc, bool need_wrapping_integral_overflow) { enum vect_reduction_type v_reduc_type; gimple *def = vect_is_simple_reduction (loop_info, phi, double_reduc, need_wrapping_integral_overflow, &v_reduc_type); if (def) { stmt_vec_info reduc_def_info = vinfo_for_stmt (phi); STMT_VINFO_REDUC_TYPE (reduc_def_info) = v_reduc_type; STMT_VINFO_REDUC_DEF (reduc_def_info) = def; reduc_def_info = vinfo_for_stmt (def); STMT_VINFO_REDUC_DEF (reduc_def_info) = phi; } return def; } /* Calculate cost of peeling the loop PEEL_ITERS_PROLOGUE times. */ int vect_get_known_peeling_cost (loop_vec_info loop_vinfo, int peel_iters_prologue, int *peel_iters_epilogue, stmt_vector_for_cost *scalar_cost_vec, stmt_vector_for_cost *prologue_cost_vec, stmt_vector_for_cost *epilogue_cost_vec) { int retval = 0; int vf = LOOP_VINFO_VECT_FACTOR (loop_vinfo); if (!LOOP_VINFO_NITERS_KNOWN_P (loop_vinfo)) { *peel_iters_epilogue = vf/2; if (dump_enabled_p ()) dump_printf_loc (MSG_NOTE, vect_location, "cost model: epilogue peel iters set to vf/2 " "because loop iterations are unknown .\n"); /* If peeled iterations are known but number of scalar loop iterations are unknown, count a taken branch per peeled loop. */ retval = record_stmt_cost (prologue_cost_vec, 1, cond_branch_taken, NULL, 0, vect_prologue); retval = record_stmt_cost (prologue_cost_vec, 1, cond_branch_taken, NULL, 0, vect_epilogue); } else { int niters = LOOP_VINFO_INT_NITERS (loop_vinfo); peel_iters_prologue = niters < peel_iters_prologue ? niters : peel_iters_prologue; *peel_iters_epilogue = (niters - peel_iters_prologue) % vf; /* If we need to peel for gaps, but no peeling is required, we have to peel VF iterations. */ if (LOOP_VINFO_PEELING_FOR_GAPS (loop_vinfo) && !*peel_iters_epilogue) *peel_iters_epilogue = vf; } stmt_info_for_cost *si; int j; if (peel_iters_prologue) FOR_EACH_VEC_ELT (*scalar_cost_vec, j, si) { stmt_vec_info stmt_info = si->stmt ? vinfo_for_stmt (si->stmt) : NULL; retval += record_stmt_cost (prologue_cost_vec, si->count * peel_iters_prologue, si->kind, stmt_info, si->misalign, vect_prologue); } if (*peel_iters_epilogue) FOR_EACH_VEC_ELT (*scalar_cost_vec, j, si) { stmt_vec_info stmt_info = si->stmt ? vinfo_for_stmt (si->stmt) : NULL; retval += record_stmt_cost (epilogue_cost_vec, si->count * *peel_iters_epilogue, si->kind, stmt_info, si->misalign, vect_epilogue); } return retval; } /* Function vect_estimate_min_profitable_iters Return the number of iterations required for the vector version of the loop to be profitable relative to the cost of the scalar version of the loop. *RET_MIN_PROFITABLE_NITERS is a cost model profitability threshold of iterations for vectorization. -1 value means loop vectorization is not profitable. This returned value may be used for dynamic profitability check. *RET_MIN_PROFITABLE_ESTIMATE is a profitability threshold to be used for static check against estimated number of iterations. */ static void vect_estimate_min_profitable_iters (loop_vec_info loop_vinfo, int *ret_min_profitable_niters, int *ret_min_profitable_estimate) { int min_profitable_iters; int min_profitable_estimate; int peel_iters_prologue; int peel_iters_epilogue; unsigned vec_inside_cost = 0; int vec_outside_cost = 0; unsigned vec_prologue_cost = 0; unsigned vec_epilogue_cost = 0; int scalar_single_iter_cost = 0; int scalar_outside_cost = 0; int vf = LOOP_VINFO_VECT_FACTOR (loop_vinfo); int npeel = LOOP_VINFO_PEELING_FOR_ALIGNMENT (loop_vinfo); void *target_cost_data = LOOP_VINFO_TARGET_COST_DATA (loop_vinfo); /* Cost model disabled. */ if (unlimited_cost_model (LOOP_VINFO_LOOP (loop_vinfo))) { dump_printf_loc (MSG_NOTE, vect_location, "cost model disabled.\n"); *ret_min_profitable_niters = 0; *ret_min_profitable_estimate = 0; return; } /* Requires loop versioning tests to handle misalignment. */ if (LOOP_REQUIRES_VERSIONING_FOR_ALIGNMENT (loop_vinfo)) { /* FIXME: Make cost depend on complexity of individual check. */ unsigned len = LOOP_VINFO_MAY_MISALIGN_STMTS (loop_vinfo).length (); (void) add_stmt_cost (target_cost_data, len, vector_stmt, NULL, 0, vect_prologue); dump_printf (MSG_NOTE, "cost model: Adding cost of checks for loop " "versioning to treat misalignment.\n"); } /* Requires loop versioning with alias checks. */ if (LOOP_REQUIRES_VERSIONING_FOR_ALIAS (loop_vinfo)) { /* FIXME: Make cost depend on complexity of individual check. */ unsigned len = LOOP_VINFO_COMP_ALIAS_DDRS (loop_vinfo).length (); (void) add_stmt_cost (target_cost_data, len, vector_stmt, NULL, 0, vect_prologue); len = LOOP_VINFO_CHECK_UNEQUAL_ADDRS (loop_vinfo).length (); if (len) /* Count LEN - 1 ANDs and LEN comparisons. */ (void) add_stmt_cost (target_cost_data, len * 2 - 1, scalar_stmt, NULL, 0, vect_prologue); dump_printf (MSG_NOTE, "cost model: Adding cost of checks for loop " "versioning aliasing.\n"); } /* Requires loop versioning with niter checks. */ if (LOOP_REQUIRES_VERSIONING_FOR_NITERS (loop_vinfo)) { /* FIXME: Make cost depend on complexity of individual check. */ (void) add_stmt_cost (target_cost_data, 1, vector_stmt, NULL, 0, vect_prologue); dump_printf (MSG_NOTE, "cost model: Adding cost of checks for loop " "versioning niters.\n"); } if (LOOP_REQUIRES_VERSIONING (loop_vinfo)) (void) add_stmt_cost (target_cost_data, 1, cond_branch_taken, NULL, 0, vect_prologue); /* Count statements in scalar loop. Using this as scalar cost for a single iteration for now. TODO: Add outer loop support. TODO: Consider assigning different costs to different scalar statements. */ scalar_single_iter_cost = LOOP_VINFO_SINGLE_SCALAR_ITERATION_COST (loop_vinfo); /* Add additional cost for the peeled instructions in prologue and epilogue loop. FORNOW: If we don't know the value of peel_iters for prologue or epilogue at compile-time - we assume it's vf/2 (the worst would be vf-1). TODO: Build an expression that represents peel_iters for prologue and epilogue to be used in a run-time test. */ if (npeel < 0) { peel_iters_prologue = vf/2; dump_printf (MSG_NOTE, "cost model: " "prologue peel iters set to vf/2.\n"); /* If peeling for alignment is unknown, loop bound of main loop becomes unknown. */ peel_iters_epilogue = vf/2; dump_printf (MSG_NOTE, "cost model: " "epilogue peel iters set to vf/2 because " "peeling for alignment is unknown.\n"); /* If peeled iterations are unknown, count a taken branch and a not taken branch per peeled loop. Even if scalar loop iterations are known, vector iterations are not known since peeled prologue iterations are not known. Hence guards remain the same. */ (void) add_stmt_cost (target_cost_data, 1, cond_branch_taken, NULL, 0, vect_prologue); (void) add_stmt_cost (target_cost_data, 1, cond_branch_not_taken, NULL, 0, vect_prologue); (void) add_stmt_cost (target_cost_data, 1, cond_branch_taken, NULL, 0, vect_epilogue); (void) add_stmt_cost (target_cost_data, 1, cond_branch_not_taken, NULL, 0, vect_epilogue); stmt_info_for_cost *si; int j; FOR_EACH_VEC_ELT (LOOP_VINFO_SCALAR_ITERATION_COST (loop_vinfo), j, si) { struct _stmt_vec_info *stmt_info = si->stmt ? vinfo_for_stmt (si->stmt) : NULL; (void) add_stmt_cost (target_cost_data, si->count * peel_iters_prologue, si->kind, stmt_info, si->misalign, vect_prologue); (void) add_stmt_cost (target_cost_data, si->count * peel_iters_epilogue, si->kind, stmt_info, si->misalign, vect_epilogue); } } else { stmt_vector_for_cost prologue_cost_vec, epilogue_cost_vec; stmt_info_for_cost *si; int j; void *data = LOOP_VINFO_TARGET_COST_DATA (loop_vinfo); prologue_cost_vec.create (2); epilogue_cost_vec.create (2); peel_iters_prologue = npeel; (void) vect_get_known_peeling_cost (loop_vinfo, peel_iters_prologue, &peel_iters_epilogue, &LOOP_VINFO_SCALAR_ITERATION_COST (loop_vinfo), &prologue_cost_vec, &epilogue_cost_vec); FOR_EACH_VEC_ELT (prologue_cost_vec, j, si) { struct _stmt_vec_info *stmt_info = si->stmt ? vinfo_for_stmt (si->stmt) : NULL; (void) add_stmt_cost (data, si->count, si->kind, stmt_info, si->misalign, vect_prologue); } FOR_EACH_VEC_ELT (epilogue_cost_vec, j, si) { struct _stmt_vec_info *stmt_info = si->stmt ? vinfo_for_stmt (si->stmt) : NULL; (void) add_stmt_cost (data, si->count, si->kind, stmt_info, si->misalign, vect_epilogue); } prologue_cost_vec.release (); epilogue_cost_vec.release (); } /* FORNOW: The scalar outside cost is incremented in one of the following ways: 1. The vectorizer checks for alignment and aliasing and generates a condition that allows dynamic vectorization. A cost model check is ANDED with the versioning condition. Hence scalar code path now has the added cost of the versioning check. if (cost > th & versioning_check) jmp to vector code Hence run-time scalar is incremented by not-taken branch cost. 2. The vectorizer then checks if a prologue is required. If the cost model check was not done before during versioning, it has to be done before the prologue check. if (cost <= th) prologue = scalar_iters if (prologue == 0) jmp to vector code else execute prologue if (prologue == num_iters) go to exit Hence the run-time scalar cost is incremented by a taken branch, plus a not-taken branch, plus a taken branch cost. 3. The vectorizer then checks if an epilogue is required. If the cost model check was not done before during prologue check, it has to be done with the epilogue check. if (prologue == 0) jmp to vector code else execute prologue if (prologue == num_iters) go to exit vector code: if ((cost <= th) | (scalar_iters-prologue-epilogue == 0)) jmp to epilogue Hence the run-time scalar cost should be incremented by 2 taken branches. TODO: The back end may reorder the BBS's differently and reverse conditions/branch directions. Change the estimates below to something more reasonable. */ /* If the number of iterations is known and we do not do versioning, we can decide whether to vectorize at compile time. Hence the scalar version do not carry cost model guard costs. */ if (!LOOP_VINFO_NITERS_KNOWN_P (loop_vinfo) || LOOP_REQUIRES_VERSIONING (loop_vinfo)) { /* Cost model check occurs at versioning. */ if (LOOP_REQUIRES_VERSIONING (loop_vinfo)) scalar_outside_cost += vect_get_stmt_cost (cond_branch_not_taken); else { /* Cost model check occurs at prologue generation. */ if (LOOP_VINFO_PEELING_FOR_ALIGNMENT (loop_vinfo) < 0) scalar_outside_cost += 2 * vect_get_stmt_cost (cond_branch_taken) + vect_get_stmt_cost (cond_branch_not_taken); /* Cost model check occurs at epilogue generation. */ else scalar_outside_cost += 2 * vect_get_stmt_cost (cond_branch_taken); } } /* Complete the target-specific cost calculations. */ finish_cost (LOOP_VINFO_TARGET_COST_DATA (loop_vinfo), &vec_prologue_cost, &vec_inside_cost, &vec_epilogue_cost); vec_outside_cost = (int)(vec_prologue_cost + vec_epilogue_cost); if (dump_enabled_p ()) { dump_printf_loc (MSG_NOTE, vect_location, "Cost model analysis: \n"); dump_printf (MSG_NOTE, " Vector inside of loop cost: %d\n", vec_inside_cost); dump_printf (MSG_NOTE, " Vector prologue cost: %d\n", vec_prologue_cost); dump_printf (MSG_NOTE, " Vector epilogue cost: %d\n", vec_epilogue_cost); dump_printf (MSG_NOTE, " Scalar iteration cost: %d\n", scalar_single_iter_cost); dump_printf (MSG_NOTE, " Scalar outside cost: %d\n", scalar_outside_cost); dump_printf (MSG_NOTE, " Vector outside cost: %d\n", vec_outside_cost); dump_printf (MSG_NOTE, " prologue iterations: %d\n", peel_iters_prologue); dump_printf (MSG_NOTE, " epilogue iterations: %d\n", peel_iters_epilogue); } /* Calculate number of iterations required to make the vector version profitable, relative to the loop bodies only. The following condition must hold true: SIC * niters + SOC > VIC * ((niters-PL_ITERS-EP_ITERS)/VF) + VOC where SIC = scalar iteration cost, VIC = vector iteration cost, VOC = vector outside cost, VF = vectorization factor, PL_ITERS = prologue iterations, EP_ITERS= epilogue iterations SOC = scalar outside cost for run time cost model check. */ if ((scalar_single_iter_cost * vf) > (int) vec_inside_cost) { if (vec_outside_cost <= 0) min_profitable_iters = 0; else { min_profitable_iters = ((vec_outside_cost - scalar_outside_cost) * vf - vec_inside_cost * peel_iters_prologue - vec_inside_cost * peel_iters_epilogue) / ((scalar_single_iter_cost * vf) - vec_inside_cost); if ((scalar_single_iter_cost * vf * min_profitable_iters) <= (((int) vec_inside_cost * min_profitable_iters) + (((int) vec_outside_cost - scalar_outside_cost) * vf))) min_profitable_iters++; } } /* vector version will never be profitable. */ else { if (LOOP_VINFO_LOOP (loop_vinfo)->force_vectorize) warning_at (vect_location, OPT_Wopenmp_simd, "vectorization " "did not happen for a simd loop"); if (dump_enabled_p ()) dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location, "cost model: the vector iteration cost = %d " "divided by the scalar iteration cost = %d " "is greater or equal to the vectorization factor = %d" ".\n", vec_inside_cost, scalar_single_iter_cost, vf); *ret_min_profitable_niters = -1; *ret_min_profitable_estimate = -1; return; } dump_printf (MSG_NOTE, " Calculated minimum iters for profitability: %d\n", min_profitable_iters); /* We want the vectorized loop to execute at least once. */ if (min_profitable_iters < (vf + peel_iters_prologue)) min_profitable_iters = vf + peel_iters_prologue; if (dump_enabled_p ()) dump_printf_loc (MSG_NOTE, vect_location, " Runtime profitability threshold = %d\n", min_profitable_iters); *ret_min_profitable_niters = min_profitable_iters; /* Calculate number of iterations required to make the vector version profitable, relative to the loop bodies only. Non-vectorized variant is SIC * niters and it must win over vector variant on the expected loop trip count. The following condition must hold true: SIC * niters > VIC * ((niters-PL_ITERS-EP_ITERS)/VF) + VOC + SOC */ if (vec_outside_cost <= 0) min_profitable_estimate = 0; else { min_profitable_estimate = ((vec_outside_cost + scalar_outside_cost) * vf - vec_inside_cost * peel_iters_prologue - vec_inside_cost * peel_iters_epilogue) / ((scalar_single_iter_cost * vf) - vec_inside_cost); } min_profitable_estimate = MAX (min_profitable_estimate, min_profitable_iters); if (dump_enabled_p ()) dump_printf_loc (MSG_NOTE, vect_location, " Static estimate profitability threshold = %d\n", min_profitable_estimate); *ret_min_profitable_estimate = min_profitable_estimate; } /* Writes into SEL a mask for a vec_perm, equivalent to a vec_shr by OFFSET vector elements (not bits) for a vector with NELT elements. */ static void calc_vec_perm_mask_for_shift (unsigned int offset, unsigned int nelt, vec_perm_indices *sel) { unsigned int i; for (i = 0; i < nelt; i++) sel->quick_push ((i + offset) & (2 * nelt - 1)); } /* Checks whether the target supports whole-vector shifts for vectors of mode MODE. This is the case if _either_ the platform handles vec_shr_optab, _or_ it supports vec_perm_const with masks for all necessary shift amounts. */ static bool have_whole_vector_shift (machine_mode mode) { if (optab_handler (vec_shr_optab, mode) != CODE_FOR_nothing) return true; if (direct_optab_handler (vec_perm_const_optab, mode) == CODE_FOR_nothing) return false; unsigned int i, nelt = GET_MODE_NUNITS (mode); auto_vec_perm_indices sel (nelt); for (i = nelt/2; i >= 1; i/=2) { sel.truncate (0); calc_vec_perm_mask_for_shift (i, nelt, &sel); if (!can_vec_perm_p (mode, false, &sel)) return false; } return true; } /* TODO: Close dependency between vect_model_*_cost and vectorizable_* functions. Design better to avoid maintenance issues. */ /* Function vect_model_reduction_cost. Models cost for a reduction operation, including the vector ops generated within the strip-mine loop, the initial definition before the loop, and the epilogue code that must be generated. */ static void vect_model_reduction_cost (stmt_vec_info stmt_info, enum tree_code reduc_code, int ncopies) { int prologue_cost = 0, epilogue_cost = 0; enum tree_code code; optab optab; tree vectype; gimple *orig_stmt; machine_mode mode; loop_vec_info loop_vinfo = STMT_VINFO_LOOP_VINFO (stmt_info); struct loop *loop = NULL; void *target_cost_data; if (loop_vinfo) { loop = LOOP_VINFO_LOOP (loop_vinfo); target_cost_data = LOOP_VINFO_TARGET_COST_DATA (loop_vinfo); } else target_cost_data = BB_VINFO_TARGET_COST_DATA (STMT_VINFO_BB_VINFO (stmt_info)); /* Condition reductions generate two reductions in the loop. */ if (STMT_VINFO_VEC_REDUCTION_TYPE (stmt_info) == COND_REDUCTION) ncopies *= 2; /* Cost of reduction op inside loop. */ unsigned inside_cost = add_stmt_cost (target_cost_data, ncopies, vector_stmt, stmt_info, 0, vect_body); vectype = STMT_VINFO_VECTYPE (stmt_info); mode = TYPE_MODE (vectype); orig_stmt = STMT_VINFO_RELATED_STMT (stmt_info); if (!orig_stmt) orig_stmt = STMT_VINFO_STMT (stmt_info); code = gimple_assign_rhs_code (orig_stmt); /* Add in cost for initial definition. For cond reduction we have four vectors: initial index, step, initial result of the data reduction, initial value of the index reduction. */ int prologue_stmts = STMT_VINFO_VEC_REDUCTION_TYPE (stmt_info) == COND_REDUCTION ? 4 : 1; prologue_cost += add_stmt_cost (target_cost_data, prologue_stmts, scalar_to_vec, stmt_info, 0, vect_prologue); /* Determine cost of epilogue code. We have a reduction operator that will reduce the vector in one statement. Also requires scalar extract. */ if (!loop || !nested_in_vect_loop_p (loop, orig_stmt)) { if (reduc_code != ERROR_MARK) { if (STMT_VINFO_VEC_REDUCTION_TYPE (stmt_info) == COND_REDUCTION) { /* An EQ stmt and an COND_EXPR stmt. */ epilogue_cost += add_stmt_cost (target_cost_data, 2, vector_stmt, stmt_info, 0, vect_epilogue); /* Reduction of the max index and a reduction of the found values. */ epilogue_cost += add_stmt_cost (target_cost_data, 2, vec_to_scalar, stmt_info, 0, vect_epilogue); /* A broadcast of the max value. */ epilogue_cost += add_stmt_cost (target_cost_data, 1, scalar_to_vec, stmt_info, 0, vect_epilogue); } else { epilogue_cost += add_stmt_cost (target_cost_data, 1, vector_stmt, stmt_info, 0, vect_epilogue); epilogue_cost += add_stmt_cost (target_cost_data, 1, vec_to_scalar, stmt_info, 0, vect_epilogue); } } else if (STMT_VINFO_VEC_REDUCTION_TYPE (stmt_info) == COND_REDUCTION) { unsigned nunits = TYPE_VECTOR_SUBPARTS (vectype); /* Extraction of scalar elements. */ epilogue_cost += add_stmt_cost (target_cost_data, 2 * nunits, vec_to_scalar, stmt_info, 0, vect_epilogue); /* Scalar max reductions via COND_EXPR / MAX_EXPR. */ epilogue_cost += add_stmt_cost (target_cost_data, 2 * nunits - 3, scalar_stmt, stmt_info, 0, vect_epilogue); } else { int vec_size_in_bits = tree_to_uhwi (TYPE_SIZE (vectype)); tree bitsize = TYPE_SIZE (TREE_TYPE (gimple_assign_lhs (orig_stmt))); int element_bitsize = tree_to_uhwi (bitsize); int nelements = vec_size_in_bits / element_bitsize; if (code == COND_EXPR) code = MAX_EXPR; optab = optab_for_tree_code (code, vectype, optab_default); /* We have a whole vector shift available. */ if (optab != unknown_optab && VECTOR_MODE_P (mode) && optab_handler (optab, mode) != CODE_FOR_nothing && have_whole_vector_shift (mode)) { /* Final reduction via vector shifts and the reduction operator. Also requires scalar extract. */ epilogue_cost += add_stmt_cost (target_cost_data, exact_log2 (nelements) * 2, vector_stmt, stmt_info, 0, vect_epilogue); epilogue_cost += add_stmt_cost (target_cost_data, 1, vec_to_scalar, stmt_info, 0, vect_epilogue); } else /* Use extracts and reduction op for final reduction. For N elements, we have N extracts and N-1 reduction ops. */ epilogue_cost += add_stmt_cost (target_cost_data, nelements + nelements - 1, vector_stmt, stmt_info, 0, vect_epilogue); } } if (dump_enabled_p ()) dump_printf (MSG_NOTE, "vect_model_reduction_cost: inside_cost = %d, " "prologue_cost = %d, epilogue_cost = %d .\n", inside_cost, prologue_cost, epilogue_cost); } /* Function vect_model_induction_cost. Models cost for induction operations. */ static void vect_model_induction_cost (stmt_vec_info stmt_info, int ncopies) { loop_vec_info loop_vinfo = STMT_VINFO_LOOP_VINFO (stmt_info); void *target_cost_data = LOOP_VINFO_TARGET_COST_DATA (loop_vinfo); unsigned inside_cost, prologue_cost; if (PURE_SLP_STMT (stmt_info)) return; /* loop cost for vec_loop. */ inside_cost = add_stmt_cost (target_cost_data, ncopies, vector_stmt, stmt_info, 0, vect_body); /* prologue cost for vec_init and vec_step. */ prologue_cost = add_stmt_cost (target_cost_data, 2, scalar_to_vec, stmt_info, 0, vect_prologue); if (dump_enabled_p ()) dump_printf_loc (MSG_NOTE, vect_location, "vect_model_induction_cost: inside_cost = %d, " "prologue_cost = %d .\n", inside_cost, prologue_cost); } /* Function get_initial_def_for_reduction Input: STMT - a stmt that performs a reduction operation in the loop. INIT_VAL - the initial value of the reduction variable Output: ADJUSTMENT_DEF - a tree that holds a value to be added to the final result of the reduction (used for adjusting the epilog - see below). Return a vector variable, initialized according to the operation that STMT performs. This vector will be used as the initial value of the vector of partial results. Option1 (adjust in epilog): Initialize the vector as follows: add/bit or/xor: [0,0,...,0,0] mult/bit and: [1,1,...,1,1] min/max/cond_expr: [init_val,init_val,..,init_val,init_val] and when necessary (e.g. add/mult case) let the caller know that it needs to adjust the result by init_val. Option2: Initialize the vector as follows: add/bit or/xor: [init_val,0,0,...,0] mult/bit and: [init_val,1,1,...,1] min/max/cond_expr: [init_val,init_val,...,init_val] and no adjustments are needed. For example, for the following code: s = init_val; for (i=0;i<n;i++) s = s + a[i]; STMT is 's = s + a[i]', and the reduction variable is 's'. For a vector of 4 units, we want to return either [0,0,0,init_val], or [0,0,0,0] and let the caller know that it needs to adjust the result at the end by 'init_val'. FORNOW, we are using the 'adjust in epilog' scheme, because this way the initialization vector is simpler (same element in all entries), if ADJUSTMENT_DEF is not NULL, and Option2 otherwise. A cost model should help decide between these two schemes. */ tree get_initial_def_for_reduction (gimple *stmt, tree init_val, tree *adjustment_def) { stmt_vec_info stmt_vinfo = vinfo_for_stmt (stmt); loop_vec_info loop_vinfo = STMT_VINFO_LOOP_VINFO (stmt_vinfo); struct loop *loop = LOOP_VINFO_LOOP (loop_vinfo); tree scalar_type = TREE_TYPE (init_val); tree vectype = get_vectype_for_scalar_type (scalar_type); int nunits; enum tree_code code = gimple_assign_rhs_code (stmt); tree def_for_init; tree init_def; int i; bool nested_in_vect_loop = false; REAL_VALUE_TYPE real_init_val = dconst0; int int_init_val = 0; gimple *def_stmt = NULL; gimple_seq stmts = NULL; gcc_assert (vectype); nunits = TYPE_VECTOR_SUBPARTS (vectype); gcc_assert (POINTER_TYPE_P (scalar_type) || INTEGRAL_TYPE_P (scalar_type) || SCALAR_FLOAT_TYPE_P (scalar_type)); if (nested_in_vect_loop_p (loop, stmt)) nested_in_vect_loop = true; else gcc_assert (loop == (gimple_bb (stmt))->loop_father); /* In case of double reduction we only create a vector variable to be put in the reduction phi node. The actual statement creation is done in vect_create_epilog_for_reduction. */ if (adjustment_def && nested_in_vect_loop && TREE_CODE (init_val) == SSA_NAME && (def_stmt = SSA_NAME_DEF_STMT (init_val)) && gimple_code (def_stmt) == GIMPLE_PHI && flow_bb_inside_loop_p (loop, gimple_bb (def_stmt)) && vinfo_for_stmt (def_stmt) && STMT_VINFO_DEF_TYPE (vinfo_for_stmt (def_stmt)) == vect_double_reduction_def) { *adjustment_def = NULL; return vect_create_destination_var (init_val, vectype); } /* In case of a nested reduction do not use an adjustment def as that case is not supported by the epilogue generation correctly if ncopies is not one. */ if (adjustment_def && nested_in_vect_loop) { *adjustment_def = NULL; return vect_get_vec_def_for_operand (init_val, stmt); } switch (code) { case WIDEN_SUM_EXPR: case DOT_PROD_EXPR: case SAD_EXPR: case PLUS_EXPR: case MINUS_EXPR: case BIT_IOR_EXPR: case BIT_XOR_EXPR: case MULT_EXPR: case BIT_AND_EXPR: { /* ADJUSMENT_DEF is NULL when called from vect_create_epilog_for_reduction to vectorize double reduction. */ if (adjustment_def) *adjustment_def = init_val; if (code == MULT_EXPR) { real_init_val = dconst1; int_init_val = 1; } if (code == BIT_AND_EXPR) int_init_val = -1; if (SCALAR_FLOAT_TYPE_P (scalar_type)) def_for_init = build_real (scalar_type, real_init_val); else def_for_init = build_int_cst (scalar_type, int_init_val); if (adjustment_def) /* Option1: the first element is '0' or '1' as well. */ init_def = gimple_build_vector_from_val (&stmts, vectype, def_for_init); else { /* Option2: the first element is INIT_VAL. */ auto_vec<tree, 32> elts (nunits); elts.quick_push (init_val); for (i = 1; i < nunits; ++i) elts.quick_push (def_for_init); init_def = gimple_build_vector (&stmts, vectype, elts); } } break; case MIN_EXPR: case MAX_EXPR: case COND_EXPR: { if (adjustment_def) { *adjustment_def = NULL_TREE; if (STMT_VINFO_VEC_REDUCTION_TYPE (stmt_vinfo) != COND_REDUCTION) { init_def = vect_get_vec_def_for_operand (init_val, stmt); break; } } init_val = gimple_convert (&stmts, TREE_TYPE (vectype), init_val); init_def = gimple_build_vector_from_val (&stmts, vectype, init_val); } break; default: gcc_unreachable (); } if (stmts) gsi_insert_seq_on_edge_immediate (loop_preheader_edge (loop), stmts); return init_def; } /* Get at the initial defs for the reduction PHIs in SLP_NODE. NUMBER_OF_VECTORS is the number of vector defs to create. */ static void get_initial_defs_for_reduction (slp_tree slp_node, vec<tree> *vec_oprnds, unsigned int number_of_vectors, enum tree_code code, bool reduc_chain) { vec<gimple *> stmts = SLP_TREE_SCALAR_STMTS (slp_node); gimple *stmt = stmts[0]; stmt_vec_info stmt_vinfo = vinfo_for_stmt (stmt); unsigned nunits; unsigned j, number_of_places_left_in_vector; tree vector_type, scalar_type; tree vop; int group_size = stmts.length (); unsigned int vec_num, i; unsigned number_of_copies = 1; vec<tree> voprnds; voprnds.create (number_of_vectors); tree neutral_op = NULL; struct loop *loop; vector_type = STMT_VINFO_VECTYPE (stmt_vinfo); scalar_type = TREE_TYPE (vector_type); nunits = TYPE_VECTOR_SUBPARTS (vector_type); gcc_assert (STMT_VINFO_DEF_TYPE (stmt_vinfo) == vect_reduction_def); loop = (gimple_bb (stmt))->loop_father; gcc_assert (loop); edge pe = loop_preheader_edge (loop); /* op is the reduction operand of the first stmt already. */ /* For additional copies (see the explanation of NUMBER_OF_COPIES below) we need either neutral operands or the original operands. See get_initial_def_for_reduction() for details. */ switch (code) { case WIDEN_SUM_EXPR: case DOT_PROD_EXPR: case SAD_EXPR: case PLUS_EXPR: case MINUS_EXPR: case BIT_IOR_EXPR: case BIT_XOR_EXPR: neutral_op = build_zero_cst (scalar_type); break; case MULT_EXPR: neutral_op = build_one_cst (scalar_type); break; case BIT_AND_EXPR: neutral_op = build_all_ones_cst (scalar_type); break; /* For MIN/MAX we don't have an easy neutral operand but the initial values can be used fine here. Only for a reduction chain we have to force a neutral element. */ case MAX_EXPR: case MIN_EXPR: if (! reduc_chain) neutral_op = NULL; else neutral_op = PHI_ARG_DEF_FROM_EDGE (stmt, pe); break; default: gcc_assert (! reduc_chain); neutral_op = NULL; } /* NUMBER_OF_COPIES is the number of times we need to use the same values in created vectors. It is greater than 1 if unrolling is performed. For example, we have two scalar operands, s1 and s2 (e.g., group of strided accesses of size two), while NUNITS is four (i.e., four scalars of this type can be packed in a vector). The output vector will contain two copies of each scalar operand: {s1, s2, s1, s2}. (NUMBER_OF_COPIES will be 2). If GROUP_SIZE > NUNITS, the scalars will be split into several vectors containing the operands. For example, NUNITS is four as before, and the group size is 8 (s1, s2, ..., s8). We will create two vectors {s1, s2, s3, s4} and {s5, s6, s7, s8}. */ number_of_copies = nunits * number_of_vectors / group_size; number_of_places_left_in_vector = nunits; auto_vec<tree, 32> elts (nunits); elts.quick_grow (nunits); for (j = 0; j < number_of_copies; j++) { for (i = group_size - 1; stmts.iterate (i, &stmt); i--) { tree op; /* Get the def before the loop. In reduction chain we have only one initial value. */ if ((j != (number_of_copies - 1) || (reduc_chain && i != 0)) && neutral_op) op = neutral_op; else op = PHI_ARG_DEF_FROM_EDGE (stmt, pe); /* Create 'vect_ = {op0,op1,...,opn}'. */ number_of_places_left_in_vector--; elts[number_of_places_left_in_vector] = op; if (number_of_places_left_in_vector == 0) { gimple_seq ctor_seq = NULL; tree init = gimple_build_vector (&ctor_seq, vector_type, elts); if (ctor_seq != NULL) gsi_insert_seq_on_edge_immediate (pe, ctor_seq); voprnds.quick_push (init); number_of_places_left_in_vector = nunits; } } } /* Since the vectors are created in the reverse order, we should invert them. */ vec_num = voprnds.length (); for (j = vec_num; j != 0; j--) { vop = voprnds[j - 1]; vec_oprnds->quick_push (vop); } voprnds.release (); /* In case that VF is greater than the unrolling factor needed for the SLP group of stmts, NUMBER_OF_VECTORS to be created is greater than NUMBER_OF_SCALARS/NUNITS or NUNITS/NUMBER_OF_SCALARS, and hence we have to replicate the vectors. */ tree neutral_vec = NULL; while (number_of_vectors > vec_oprnds->length ()) { if (neutral_op) { if (!neutral_vec) { gimple_seq ctor_seq = NULL; neutral_vec = gimple_build_vector_from_val (&ctor_seq, vector_type, neutral_op); if (ctor_seq != NULL) gsi_insert_seq_on_edge_immediate (pe, ctor_seq); } vec_oprnds->quick_push (neutral_vec); } else { for (i = 0; vec_oprnds->iterate (i, &vop) && i < vec_num; i++) vec_oprnds->quick_push (vop); } } } /* Function vect_create_epilog_for_reduction Create code at the loop-epilog to finalize the result of a reduction computation. VECT_DEFS is list of vector of partial results, i.e., the lhs's of vector reduction statements. STMT is the scalar reduction stmt that is being vectorized. NCOPIES is > 1 in case the vectorization factor (VF) is bigger than the number of elements that we can fit in a vectype (nunits). In this case we have to generate more than one vector stmt - i.e - we need to "unroll" the vector stmt by a factor VF/nunits. For more details see documentation in vectorizable_operation. REDUC_CODE is the tree-code for the epilog reduction. REDUCTION_PHIS is a list of the phi-nodes that carry the reduction computation. REDUC_INDEX is the index of the operand in the right hand side of the statement that is defined by REDUCTION_PHI. DOUBLE_REDUC is TRUE if double reduction phi nodes should be handled. SLP_NODE is an SLP node containing a group of reduction statements. The first one in this group is STMT. This function: 1. Creates the reduction def-use cycles: sets the arguments for REDUCTION_PHIS: The loop-entry argument is the vectorized initial-value of the reduction. The loop-latch argument is taken from VECT_DEFS - the vector of partial sums. 2. "Reduces" each vector of partial results VECT_DEFS into a single result, by applying the operation specified by REDUC_CODE if available, or by other means (whole-vector shifts or a scalar loop). The function also creates a new phi node at the loop exit to preserve loop-closed form, as illustrated below. The flow at the entry to this function: loop: vec_def = phi <null, null> # REDUCTION_PHI VECT_DEF = vector_stmt # vectorized form of STMT s_loop = scalar_stmt # (scalar) STMT loop_exit: s_out0 = phi <s_loop> # (scalar) EXIT_PHI use <s_out0> use <s_out0> The above is transformed by this function into: loop: vec_def = phi <vec_init, VECT_DEF> # REDUCTION_PHI VECT_DEF = vector_stmt # vectorized form of STMT s_loop = scalar_stmt # (scalar) STMT loop_exit: s_out0 = phi <s_loop> # (scalar) EXIT_PHI v_out1 = phi <VECT_DEF> # NEW_EXIT_PHI v_out2 = reduce <v_out1> s_out3 = extract_field <v_out2, 0> s_out4 = adjust_result <s_out3> use <s_out4> use <s_out4> */ static void vect_create_epilog_for_reduction (vec<tree> vect_defs, gimple *stmt, gimple *reduc_def_stmt, int ncopies, enum tree_code reduc_code, vec<gimple *> reduction_phis, bool double_reduc, slp_tree slp_node, slp_instance slp_node_instance) { stmt_vec_info stmt_info = vinfo_for_stmt (stmt); stmt_vec_info prev_phi_info; tree vectype; machine_mode mode; loop_vec_info loop_vinfo = STMT_VINFO_LOOP_VINFO (stmt_info); struct loop *loop = LOOP_VINFO_LOOP (loop_vinfo), *outer_loop = NULL; basic_block exit_bb; tree scalar_dest; tree scalar_type; gimple *new_phi = NULL, *phi; gimple_stmt_iterator exit_gsi; tree vec_dest; tree new_temp = NULL_TREE, new_dest, new_name, new_scalar_dest; gimple *epilog_stmt = NULL; enum tree_code code = gimple_assign_rhs_code (stmt); gimple *exit_phi; tree bitsize; tree adjustment_def = NULL; tree vec_initial_def = NULL; tree expr, def, initial_def = NULL; tree orig_name, scalar_result; imm_use_iterator imm_iter, phi_imm_iter; use_operand_p use_p, phi_use_p; gimple *use_stmt, *orig_stmt, *reduction_phi = NULL; bool nested_in_vect_loop = false; auto_vec<gimple *> new_phis; auto_vec<gimple *> inner_phis; enum vect_def_type dt = vect_unknown_def_type; int j, i; auto_vec<tree> scalar_results; unsigned int group_size = 1, k, ratio; auto_vec<tree> vec_initial_defs; auto_vec<gimple *> phis; bool slp_reduc = false; tree new_phi_result; gimple *inner_phi = NULL; tree induction_index = NULL_TREE; if (slp_node) group_size = SLP_TREE_SCALAR_STMTS (slp_node).length (); if (nested_in_vect_loop_p (loop, stmt)) { outer_loop = loop; loop = loop->inner; nested_in_vect_loop = true; gcc_assert (!slp_node); } vectype = STMT_VINFO_VECTYPE (stmt_info); gcc_assert (vectype); mode = TYPE_MODE (vectype); /* 1. Create the reduction def-use cycle: Set the arguments of REDUCTION_PHIS, i.e., transform loop: vec_def = phi <null, null> # REDUCTION_PHI VECT_DEF = vector_stmt # vectorized form of STMT ... into: loop: vec_def = phi <vec_init, VECT_DEF> # REDUCTION_PHI VECT_DEF = vector_stmt # vectorized form of STMT ... (in case of SLP, do it for all the phis). */ /* Get the loop-entry arguments. */ enum vect_def_type initial_def_dt = vect_unknown_def_type; if (slp_node) { unsigned vec_num = SLP_TREE_NUMBER_OF_VEC_STMTS (slp_node); vec_initial_defs.reserve (vec_num); get_initial_defs_for_reduction (slp_node_instance->reduc_phis, &vec_initial_defs, vec_num, code, GROUP_FIRST_ELEMENT (stmt_info)); } else { /* Get at the scalar def before the loop, that defines the initial value of the reduction variable. */ gimple *def_stmt; initial_def = PHI_ARG_DEF_FROM_EDGE (reduc_def_stmt, loop_preheader_edge (loop)); vect_is_simple_use (initial_def, loop_vinfo, &def_stmt, &initial_def_dt); vec_initial_def = get_initial_def_for_reduction (stmt, initial_def, &adjustment_def); vec_initial_defs.create (1); vec_initial_defs.quick_push (vec_initial_def); } /* Set phi nodes arguments. */ FOR_EACH_VEC_ELT (reduction_phis, i, phi) { tree vec_init_def = vec_initial_defs[i]; tree def = vect_defs[i]; for (j = 0; j < ncopies; j++) { if (j != 0) { phi = STMT_VINFO_RELATED_STMT (vinfo_for_stmt (phi)); if (nested_in_vect_loop) vec_init_def = vect_get_vec_def_for_stmt_copy (initial_def_dt, vec_init_def); } /* Set the loop-entry arg of the reduction-phi. */ if (STMT_VINFO_VEC_REDUCTION_TYPE (stmt_info) == INTEGER_INDUC_COND_REDUCTION) { /* Initialise the reduction phi to zero. This prevents initial values of non-zero interferring with the reduction op. */ gcc_assert (ncopies == 1); gcc_assert (i == 0); tree vec_init_def_type = TREE_TYPE (vec_init_def); tree zero_vec = build_zero_cst (vec_init_def_type); add_phi_arg (as_a <gphi *> (phi), zero_vec, loop_preheader_edge (loop), UNKNOWN_LOCATION); } else add_phi_arg (as_a <gphi *> (phi), vec_init_def, loop_preheader_edge (loop), UNKNOWN_LOCATION); /* Set the loop-latch arg for the reduction-phi. */ if (j > 0) def = vect_get_vec_def_for_stmt_copy (vect_unknown_def_type, def); add_phi_arg (as_a <gphi *> (phi), def, loop_latch_edge (loop), UNKNOWN_LOCATION); if (dump_enabled_p ()) { dump_printf_loc (MSG_NOTE, vect_location, "transform reduction: created def-use cycle: "); dump_gimple_stmt (MSG_NOTE, TDF_SLIM, phi, 0); dump_gimple_stmt (MSG_NOTE, TDF_SLIM, SSA_NAME_DEF_STMT (def), 0); } } } /* For cond reductions we want to create a new vector (INDEX_COND_EXPR) which is updated with the current index of the loop for every match of the original loop's cond_expr (VEC_STMT). This results in a vector containing the last time the condition passed for that vector lane. The first match will be a 1 to allow 0 to be used for non-matching indexes. If there are no matches at all then the vector will be all zeroes. */ if (STMT_VINFO_VEC_REDUCTION_TYPE (stmt_info) == COND_REDUCTION) { tree indx_before_incr, indx_after_incr; int nunits_out = TYPE_VECTOR_SUBPARTS (vectype); int k; gimple *vec_stmt = STMT_VINFO_VEC_STMT (stmt_info); gcc_assert (gimple_assign_rhs_code (vec_stmt) == VEC_COND_EXPR); int scalar_precision = GET_MODE_PRECISION (SCALAR_TYPE_MODE (TREE_TYPE (vectype))); tree cr_index_scalar_type = make_unsigned_type (scalar_precision); tree cr_index_vector_type = build_vector_type (cr_index_scalar_type, TYPE_VECTOR_SUBPARTS (vectype)); /* First we create a simple vector induction variable which starts with the values {1,2,3,...} (SERIES_VECT) and increments by the vector size (STEP). */ /* Create a {1,2,3,...} vector. */ auto_vec<tree, 32> vtemp (nunits_out); for (k = 0; k < nunits_out; ++k) vtemp.quick_push (build_int_cst (cr_index_scalar_type, k + 1)); tree series_vect = build_vector (cr_index_vector_type, vtemp); /* Create a vector of the step value. */ tree step = build_int_cst (cr_index_scalar_type, nunits_out); tree vec_step = build_vector_from_val (cr_index_vector_type, step); /* Create an induction variable. */ gimple_stmt_iterator incr_gsi; bool insert_after; standard_iv_increment_position (loop, &incr_gsi, &insert_after); create_iv (series_vect, vec_step, NULL_TREE, loop, &incr_gsi, insert_after, &indx_before_incr, &indx_after_incr); /* Next create a new phi node vector (NEW_PHI_TREE) which starts filled with zeros (VEC_ZERO). */ /* Create a vector of 0s. */ tree zero = build_zero_cst (cr_index_scalar_type); tree vec_zero = build_vector_from_val (cr_index_vector_type, zero); /* Create a vector phi node. */ tree new_phi_tree = make_ssa_name (cr_index_vector_type); new_phi = create_phi_node (new_phi_tree, loop->header); set_vinfo_for_stmt (new_phi, new_stmt_vec_info (new_phi, loop_vinfo)); add_phi_arg (as_a <gphi *> (new_phi), vec_zero, loop_preheader_edge (loop), UNKNOWN_LOCATION); /* Now take the condition from the loops original cond_expr (VEC_STMT) and produce a new cond_expr (INDEX_COND_EXPR) which for every match uses values from the induction variable (INDEX_BEFORE_INCR) otherwise uses values from the phi node (NEW_PHI_TREE). Finally, we update the phi (NEW_PHI_TREE) to take the value of the new cond_expr (INDEX_COND_EXPR). */ /* Duplicate the condition from vec_stmt. */ tree ccompare = unshare_expr (gimple_assign_rhs1 (vec_stmt)); /* Create a conditional, where the condition is taken from vec_stmt (CCOMPARE), then is the induction index (INDEX_BEFORE_INCR) and else is the phi (NEW_PHI_TREE). */ tree index_cond_expr = build3 (VEC_COND_EXPR, cr_index_vector_type, ccompare, indx_before_incr, new_phi_tree); induction_index = make_ssa_name (cr_index_vector_type); gimple *index_condition = gimple_build_assign (induction_index, index_cond_expr); gsi_insert_before (&incr_gsi, index_condition, GSI_SAME_STMT); stmt_vec_info index_vec_info = new_stmt_vec_info (index_condition, loop_vinfo); STMT_VINFO_VECTYPE (index_vec_info) = cr_index_vector_type; set_vinfo_for_stmt (index_condition, index_vec_info); /* Update the phi with the vec cond. */ add_phi_arg (as_a <gphi *> (new_phi), induction_index, loop_latch_edge (loop), UNKNOWN_LOCATION); } /* 2. Create epilog code. The reduction epilog code operates across the elements of the vector of partial results computed by the vectorized loop. The reduction epilog code consists of: step 1: compute the scalar result in a vector (v_out2) step 2: extract the scalar result (s_out3) from the vector (v_out2) step 3: adjust the scalar result (s_out3) if needed. Step 1 can be accomplished using one the following three schemes: (scheme 1) using reduc_code, if available. (scheme 2) using whole-vector shifts, if available. (scheme 3) using a scalar loop. In this case steps 1+2 above are combined. The overall epilog code looks like this: s_out0 = phi <s_loop> # original EXIT_PHI v_out1 = phi <VECT_DEF> # NEW_EXIT_PHI v_out2 = reduce <v_out1> # step 1 s_out3 = extract_field <v_out2, 0> # step 2 s_out4 = adjust_result <s_out3> # step 3 (step 3 is optional, and steps 1 and 2 may be combined). Lastly, the uses of s_out0 are replaced by s_out4. */ /* 2.1 Create new loop-exit-phis to preserve loop-closed form: v_out1 = phi <VECT_DEF> Store them in NEW_PHIS. */ exit_bb = single_exit (loop)->dest; prev_phi_info = NULL; new_phis.create (vect_defs.length ()); FOR_EACH_VEC_ELT (vect_defs, i, def) { for (j = 0; j < ncopies; j++) { tree new_def = copy_ssa_name (def); phi = create_phi_node (new_def, exit_bb); set_vinfo_for_stmt (phi, new_stmt_vec_info (phi, loop_vinfo)); if (j == 0) new_phis.quick_push (phi); else { def = vect_get_vec_def_for_stmt_copy (dt, def); STMT_VINFO_RELATED_STMT (prev_phi_info) = phi; } SET_PHI_ARG_DEF (phi, single_exit (loop)->dest_idx, def); prev_phi_info = vinfo_for_stmt (phi); } } /* The epilogue is created for the outer-loop, i.e., for the loop being vectorized. Create exit phis for the outer loop. */ if (double_reduc) { loop = outer_loop; exit_bb = single_exit (loop)->dest; inner_phis.create (vect_defs.length ()); FOR_EACH_VEC_ELT (new_phis, i, phi) { tree new_result = copy_ssa_name (PHI_RESULT (phi)); gphi *outer_phi = create_phi_node (new_result, exit_bb); SET_PHI_ARG_DEF (outer_phi, single_exit (loop)->dest_idx, PHI_RESULT (phi)); set_vinfo_for_stmt (outer_phi, new_stmt_vec_info (outer_phi, loop_vinfo)); inner_phis.quick_push (phi); new_phis[i] = outer_phi; prev_phi_info = vinfo_for_stmt (outer_phi); while (STMT_VINFO_RELATED_STMT (vinfo_for_stmt (phi))) { phi = STMT_VINFO_RELATED_STMT (vinfo_for_stmt (phi)); new_result = copy_ssa_name (PHI_RESULT (phi)); outer_phi = create_phi_node (new_result, exit_bb); SET_PHI_ARG_DEF (outer_phi, single_exit (loop)->dest_idx, PHI_RESULT (phi)); set_vinfo_for_stmt (outer_phi, new_stmt_vec_info (outer_phi, loop_vinfo)); STMT_VINFO_RELATED_STMT (prev_phi_info) = outer_phi; prev_phi_info = vinfo_for_stmt (outer_phi); } } } exit_gsi = gsi_after_labels (exit_bb); /* 2.2 Get the relevant tree-code to use in the epilog for schemes 2,3 (i.e. when reduc_code is not available) and in the final adjustment code (if needed). Also get the original scalar reduction variable as defined in the loop. In case STMT is a "pattern-stmt" (i.e. - it represents a reduction pattern), the tree-code and scalar-def are taken from the original stmt that the pattern-stmt (STMT) replaces. Otherwise (it is a regular reduction) - the tree-code and scalar-def are taken from STMT. */ orig_stmt = STMT_VINFO_RELATED_STMT (stmt_info); if (!orig_stmt) { /* Regular reduction */ orig_stmt = stmt; } else { /* Reduction pattern */ stmt_vec_info stmt_vinfo = vinfo_for_stmt (orig_stmt); gcc_assert (STMT_VINFO_IN_PATTERN_P (stmt_vinfo)); gcc_assert (STMT_VINFO_RELATED_STMT (stmt_vinfo) == stmt); } code = gimple_assign_rhs_code (orig_stmt); /* For MINUS_EXPR the initial vector is [init_val,0,...,0], therefore, partial results are added and not subtracted. */ if (code == MINUS_EXPR) code = PLUS_EXPR; scalar_dest = gimple_assign_lhs (orig_stmt); scalar_type = TREE_TYPE (scalar_dest); scalar_results.create (group_size); new_scalar_dest = vect_create_destination_var (scalar_dest, NULL); bitsize = TYPE_SIZE (scalar_type); /* In case this is a reduction in an inner-loop while vectorizing an outer loop - we don't need to extract a single scalar result at the end of the inner-loop (unless it is double reduction, i.e., the use of reduction is outside the outer-loop). The final vector of partial results will be used in the vectorized outer-loop, or reduced to a scalar result at the end of the outer-loop. */ if (nested_in_vect_loop && !double_reduc) goto vect_finalize_reduction; /* SLP reduction without reduction chain, e.g., # a1 = phi <a2, a0> # b1 = phi <b2, b0> a2 = operation (a1) b2 = operation (b1) */ slp_reduc = (slp_node && !GROUP_FIRST_ELEMENT (vinfo_for_stmt (stmt))); /* In case of reduction chain, e.g., # a1 = phi <a3, a0> a2 = operation (a1) a3 = operation (a2), we may end up with more than one vector result. Here we reduce them to one vector. */ if (GROUP_FIRST_ELEMENT (vinfo_for_stmt (stmt))) { tree first_vect = PHI_RESULT (new_phis[0]); gassign *new_vec_stmt = NULL; vec_dest = vect_create_destination_var (scalar_dest, vectype); for (k = 1; k < new_phis.length (); k++) { gimple *next_phi = new_phis[k]; tree second_vect = PHI_RESULT (next_phi); tree tem = make_ssa_name (vec_dest, new_vec_stmt); new_vec_stmt = gimple_build_assign (tem, code, first_vect, second_vect); gsi_insert_before (&exit_gsi, new_vec_stmt, GSI_SAME_STMT); first_vect = tem; } new_phi_result = first_vect; if (new_vec_stmt) { new_phis.truncate (0); new_phis.safe_push (new_vec_stmt); } } /* Likewise if we couldn't use a single defuse cycle. */ else if (ncopies > 1) { gcc_assert (new_phis.length () == 1); tree first_vect = PHI_RESULT (new_phis[0]); gassign *new_vec_stmt = NULL; vec_dest = vect_create_destination_var (scalar_dest, vectype); gimple *next_phi = new_phis[0]; for (int k = 1; k < ncopies; ++k) { next_phi = STMT_VINFO_RELATED_STMT (vinfo_for_stmt (next_phi)); tree second_vect = PHI_RESULT (next_phi); tree tem = make_ssa_name (vec_dest, new_vec_stmt); new_vec_stmt = gimple_build_assign (tem, code, first_vect, second_vect); gsi_insert_before (&exit_gsi, new_vec_stmt, GSI_SAME_STMT); first_vect = tem; } new_phi_result = first_vect; new_phis.truncate (0); new_phis.safe_push (new_vec_stmt); } else new_phi_result = PHI_RESULT (new_phis[0]); if (STMT_VINFO_VEC_REDUCTION_TYPE (stmt_info) == COND_REDUCTION && reduc_code != ERROR_MARK) { /* For condition reductions, we have a vector (NEW_PHI_RESULT) containing various data values where the condition matched and another vector (INDUCTION_INDEX) containing all the indexes of those matches. We need to extract the last matching index (which will be the index with highest value) and use this to index into the data vector. For the case where there were no matches, the data vector will contain all default values and the index vector will be all zeros. */ /* Get various versions of the type of the vector of indexes. */ tree index_vec_type = TREE_TYPE (induction_index); gcc_checking_assert (TYPE_UNSIGNED (index_vec_type)); tree index_scalar_type = TREE_TYPE (index_vec_type); tree index_vec_cmp_type = build_same_sized_truth_vector_type (index_vec_type); /* Get an unsigned integer version of the type of the data vector. */ int scalar_precision = GET_MODE_PRECISION (SCALAR_TYPE_MODE (scalar_type)); tree scalar_type_unsigned = make_unsigned_type (scalar_precision); tree vectype_unsigned = build_vector_type (scalar_type_unsigned, TYPE_VECTOR_SUBPARTS (vectype)); /* First we need to create a vector (ZERO_VEC) of zeros and another vector (MAX_INDEX_VEC) filled with the last matching index, which we can create using a MAX reduction and then expanding. In the case where the loop never made any matches, the max index will be zero. */ /* Vector of {0, 0, 0,...}. */ tree zero_vec = make_ssa_name (vectype); tree zero_vec_rhs = build_zero_cst (vectype); gimple *zero_vec_stmt = gimple_build_assign (zero_vec, zero_vec_rhs); gsi_insert_before (&exit_gsi, zero_vec_stmt, GSI_SAME_STMT); /* Find maximum value from the vector of found indexes. */ tree max_index = make_ssa_name (index_scalar_type); gimple *max_index_stmt = gimple_build_assign (max_index, REDUC_MAX_EXPR, induction_index); gsi_insert_before (&exit_gsi, max_index_stmt, GSI_SAME_STMT); /* Vector of {max_index, max_index, max_index,...}. */ tree max_index_vec = make_ssa_name (index_vec_type); tree max_index_vec_rhs = build_vector_from_val (index_vec_type, max_index); gimple *max_index_vec_stmt = gimple_build_assign (max_index_vec, max_index_vec_rhs); gsi_insert_before (&exit_gsi, max_index_vec_stmt, GSI_SAME_STMT); /* Next we compare the new vector (MAX_INDEX_VEC) full of max indexes with the vector (INDUCTION_INDEX) of found indexes, choosing values from the data vector (NEW_PHI_RESULT) for matches, 0 (ZERO_VEC) otherwise. Only one value should match, resulting in a vector (VEC_COND) with one data value and the rest zeros. In the case where the loop never made any matches, every index will match, resulting in a vector with all data values (which will all be the default value). */ /* Compare the max index vector to the vector of found indexes to find the position of the max value. */ tree vec_compare = make_ssa_name (index_vec_cmp_type); gimple *vec_compare_stmt = gimple_build_assign (vec_compare, EQ_EXPR, induction_index, max_index_vec); gsi_insert_before (&exit_gsi, vec_compare_stmt, GSI_SAME_STMT); /* Use the compare to choose either values from the data vector or zero. */ tree vec_cond = make_ssa_name (vectype); gimple *vec_cond_stmt = gimple_build_assign (vec_cond, VEC_COND_EXPR, vec_compare, new_phi_result, zero_vec); gsi_insert_before (&exit_gsi, vec_cond_stmt, GSI_SAME_STMT); /* Finally we need to extract the data value from the vector (VEC_COND) into a scalar (MATCHED_DATA_REDUC). Logically we want to do a OR reduction, but because this doesn't exist, we can use a MAX reduction instead. The data value might be signed or a float so we need to cast it first. In the case where the loop never made any matches, the data values are all identical, and so will reduce down correctly. */ /* Make the matched data values unsigned. */ tree vec_cond_cast = make_ssa_name (vectype_unsigned); tree vec_cond_cast_rhs = build1 (VIEW_CONVERT_EXPR, vectype_unsigned, vec_cond); gimple *vec_cond_cast_stmt = gimple_build_assign (vec_cond_cast, VIEW_CONVERT_EXPR, vec_cond_cast_rhs); gsi_insert_before (&exit_gsi, vec_cond_cast_stmt, GSI_SAME_STMT); /* Reduce down to a scalar value. */ tree data_reduc = make_ssa_name (scalar_type_unsigned); optab ot = optab_for_tree_code (REDUC_MAX_EXPR, vectype_unsigned, optab_default); gcc_assert (optab_handler (ot, TYPE_MODE (vectype_unsigned)) != CODE_FOR_nothing); gimple *data_reduc_stmt = gimple_build_assign (data_reduc, REDUC_MAX_EXPR, vec_cond_cast); gsi_insert_before (&exit_gsi, data_reduc_stmt, GSI_SAME_STMT); /* Convert the reduced value back to the result type and set as the result. */ gimple_seq stmts = NULL; new_temp = gimple_build (&stmts, VIEW_CONVERT_EXPR, scalar_type, data_reduc); gsi_insert_seq_before (&exit_gsi, stmts, GSI_SAME_STMT); scalar_results.safe_push (new_temp); } else if (STMT_VINFO_VEC_REDUCTION_TYPE (stmt_info) == COND_REDUCTION && reduc_code == ERROR_MARK) { /* Condition redution without supported REDUC_MAX_EXPR. Generate idx = 0; idx_val = induction_index[0]; val = data_reduc[0]; for (idx = 0, val = init, i = 0; i < nelts; ++i) if (induction_index[i] > idx_val) val = data_reduc[i], idx_val = induction_index[i]; return val; */ tree data_eltype = TREE_TYPE (TREE_TYPE (new_phi_result)); tree idx_eltype = TREE_TYPE (TREE_TYPE (induction_index)); unsigned HOST_WIDE_INT el_size = tree_to_uhwi (TYPE_SIZE (idx_eltype)); unsigned HOST_WIDE_INT v_size = el_size * TYPE_VECTOR_SUBPARTS (TREE_TYPE (induction_index)); tree idx_val = NULL_TREE, val = NULL_TREE; for (unsigned HOST_WIDE_INT off = 0; off < v_size; off += el_size) { tree old_idx_val = idx_val; tree old_val = val; idx_val = make_ssa_name (idx_eltype); epilog_stmt = gimple_build_assign (idx_val, BIT_FIELD_REF, build3 (BIT_FIELD_REF, idx_eltype, induction_index, bitsize_int (el_size), bitsize_int (off))); gsi_insert_before (&exit_gsi, epilog_stmt, GSI_SAME_STMT); val = make_ssa_name (data_eltype); epilog_stmt = gimple_build_assign (val, BIT_FIELD_REF, build3 (BIT_FIELD_REF, data_eltype, new_phi_result, bitsize_int (el_size), bitsize_int (off))); gsi_insert_before (&exit_gsi, epilog_stmt, GSI_SAME_STMT); if (off != 0) { tree new_idx_val = idx_val; tree new_val = val; if (off != v_size - el_size) { new_idx_val = make_ssa_name (idx_eltype); epilog_stmt = gimple_build_assign (new_idx_val, MAX_EXPR, idx_val, old_idx_val); gsi_insert_before (&exit_gsi, epilog_stmt, GSI_SAME_STMT); } new_val = make_ssa_name (data_eltype); epilog_stmt = gimple_build_assign (new_val, COND_EXPR, build2 (GT_EXPR, boolean_type_node, idx_val, old_idx_val), val, old_val); gsi_insert_before (&exit_gsi, epilog_stmt, GSI_SAME_STMT); idx_val = new_idx_val; val = new_val; } } /* Convert the reduced value back to the result type and set as the result. */ gimple_seq stmts = NULL; val = gimple_convert (&stmts, scalar_type, val); gsi_insert_seq_before (&exit_gsi, stmts, GSI_SAME_STMT); scalar_results.safe_push (val); } /* 2.3 Create the reduction code, using one of the three schemes described above. In SLP we simply need to extract all the elements from the vector (without reducing them), so we use scalar shifts. */ else if (reduc_code != ERROR_MARK && !slp_reduc) { tree tmp; tree vec_elem_type; /* Case 1: Create: v_out2 = reduc_expr <v_out1> */ if (dump_enabled_p ()) dump_printf_loc (MSG_NOTE, vect_location, "Reduce using direct vector reduction.\n"); vec_elem_type = TREE_TYPE (TREE_TYPE (new_phi_result)); if (!useless_type_conversion_p (scalar_type, vec_elem_type)) { tree tmp_dest = vect_create_destination_var (scalar_dest, vec_elem_type); tmp = build1 (reduc_code, vec_elem_type, new_phi_result); epilog_stmt = gimple_build_assign (tmp_dest, tmp); new_temp = make_ssa_name (tmp_dest, epilog_stmt); gimple_assign_set_lhs (epilog_stmt, new_temp); gsi_insert_before (&exit_gsi, epilog_stmt, GSI_SAME_STMT); tmp = build1 (NOP_EXPR, scalar_type, new_temp); } else tmp = build1 (reduc_code, scalar_type, new_phi_result); epilog_stmt = gimple_build_assign (new_scalar_dest, tmp); new_temp = make_ssa_name (new_scalar_dest, epilog_stmt); gimple_assign_set_lhs (epilog_stmt, new_temp); gsi_insert_before (&exit_gsi, epilog_stmt, GSI_SAME_STMT); if (STMT_VINFO_VEC_REDUCTION_TYPE (stmt_info) == INTEGER_INDUC_COND_REDUCTION) { /* Earlier we set the initial value to be zero. Check the result and if it is zero then replace with the original initial value. */ tree zero = build_zero_cst (scalar_type); tree zcompare = build2 (EQ_EXPR, boolean_type_node, new_temp, zero); tmp = make_ssa_name (new_scalar_dest); epilog_stmt = gimple_build_assign (tmp, COND_EXPR, zcompare, initial_def, new_temp); gsi_insert_before (&exit_gsi, epilog_stmt, GSI_SAME_STMT); new_temp = tmp; } scalar_results.safe_push (new_temp); } else { bool reduce_with_shift = have_whole_vector_shift (mode); int element_bitsize = tree_to_uhwi (bitsize); int vec_size_in_bits = tree_to_uhwi (TYPE_SIZE (vectype)); tree vec_temp; /* COND reductions all do the final reduction with MAX_EXPR. */ if (code == COND_EXPR) code = MAX_EXPR; /* Regardless of whether we have a whole vector shift, if we're emulating the operation via tree-vect-generic, we don't want to use it. Only the first round of the reduction is likely to still be profitable via emulation. */ /* ??? It might be better to emit a reduction tree code here, so that tree-vect-generic can expand the first round via bit tricks. */ if (!VECTOR_MODE_P (mode)) reduce_with_shift = false; else { optab optab = optab_for_tree_code (code, vectype, optab_default); if (optab_handler (optab, mode) == CODE_FOR_nothing) reduce_with_shift = false; } if (reduce_with_shift && !slp_reduc) { int nelements = vec_size_in_bits / element_bitsize; auto_vec_perm_indices sel (nelements); int elt_offset; tree zero_vec = build_zero_cst (vectype); /* Case 2: Create: for (offset = nelements/2; offset >= 1; offset/=2) { Create: va' = vec_shift <va, offset> Create: va = vop <va, va'> } */ tree rhs; if (dump_enabled_p ()) dump_printf_loc (MSG_NOTE, vect_location, "Reduce using vector shifts\n"); vec_dest = vect_create_destination_var (scalar_dest, vectype); new_temp = new_phi_result; for (elt_offset = nelements / 2; elt_offset >= 1; elt_offset /= 2) { sel.truncate (0); calc_vec_perm_mask_for_shift (elt_offset, nelements, &sel); tree mask = vect_gen_perm_mask_any (vectype, sel); epilog_stmt = gimple_build_assign (vec_dest, VEC_PERM_EXPR, new_temp, zero_vec, mask); new_name = make_ssa_name (vec_dest, epilog_stmt); gimple_assign_set_lhs (epilog_stmt, new_name); gsi_insert_before (&exit_gsi, epilog_stmt, GSI_SAME_STMT); epilog_stmt = gimple_build_assign (vec_dest, code, new_name, new_temp); new_temp = make_ssa_name (vec_dest, epilog_stmt); gimple_assign_set_lhs (epilog_stmt, new_temp); gsi_insert_before (&exit_gsi, epilog_stmt, GSI_SAME_STMT); } /* 2.4 Extract the final scalar result. Create: s_out3 = extract_field <v_out2, bitpos> */ if (dump_enabled_p ()) dump_printf_loc (MSG_NOTE, vect_location, "extract scalar result\n"); rhs = build3 (BIT_FIELD_REF, scalar_type, new_temp, bitsize, bitsize_zero_node); epilog_stmt = gimple_build_assign (new_scalar_dest, rhs); new_temp = make_ssa_name (new_scalar_dest, epilog_stmt); gimple_assign_set_lhs (epilog_stmt, new_temp); gsi_insert_before (&exit_gsi, epilog_stmt, GSI_SAME_STMT); scalar_results.safe_push (new_temp); } else { /* Case 3: Create: s = extract_field <v_out2, 0> for (offset = element_size; offset < vector_size; offset += element_size;) { Create: s' = extract_field <v_out2, offset> Create: s = op <s, s'> // For non SLP cases } */ if (dump_enabled_p ()) dump_printf_loc (MSG_NOTE, vect_location, "Reduce using scalar code.\n"); vec_size_in_bits = tree_to_uhwi (TYPE_SIZE (vectype)); FOR_EACH_VEC_ELT (new_phis, i, new_phi) { int bit_offset; if (gimple_code (new_phi) == GIMPLE_PHI) vec_temp = PHI_RESULT (new_phi); else vec_temp = gimple_assign_lhs (new_phi); tree rhs = build3 (BIT_FIELD_REF, scalar_type, vec_temp, bitsize, bitsize_zero_node); epilog_stmt = gimple_build_assign (new_scalar_dest, rhs); new_temp = make_ssa_name (new_scalar_dest, epilog_stmt); gimple_assign_set_lhs (epilog_stmt, new_temp); gsi_insert_before (&exit_gsi, epilog_stmt, GSI_SAME_STMT); /* In SLP we don't need to apply reduction operation, so we just collect s' values in SCALAR_RESULTS. */ if (slp_reduc) scalar_results.safe_push (new_temp); for (bit_offset = element_bitsize; bit_offset < vec_size_in_bits; bit_offset += element_bitsize) { tree bitpos = bitsize_int (bit_offset); tree rhs = build3 (BIT_FIELD_REF, scalar_type, vec_temp, bitsize, bitpos); epilog_stmt = gimple_build_assign (new_scalar_dest, rhs); new_name = make_ssa_name (new_scalar_dest, epilog_stmt); gimple_assign_set_lhs (epilog_stmt, new_name); gsi_insert_before (&exit_gsi, epilog_stmt, GSI_SAME_STMT); if (slp_reduc) { /* In SLP we don't need to apply reduction operation, so we just collect s' values in SCALAR_RESULTS. */ new_temp = new_name; scalar_results.safe_push (new_name); } else { epilog_stmt = gimple_build_assign (new_scalar_dest, code, new_name, new_temp); new_temp = make_ssa_name (new_scalar_dest, epilog_stmt); gimple_assign_set_lhs (epilog_stmt, new_temp); gsi_insert_before (&exit_gsi, epilog_stmt, GSI_SAME_STMT); } } } /* The only case where we need to reduce scalar results in SLP, is unrolling. If the size of SCALAR_RESULTS is greater than GROUP_SIZE, we reduce them combining elements modulo GROUP_SIZE. */ if (slp_reduc) { tree res, first_res, new_res; gimple *new_stmt; /* Reduce multiple scalar results in case of SLP unrolling. */ for (j = group_size; scalar_results.iterate (j, &res); j++) { first_res = scalar_results[j % group_size]; new_stmt = gimple_build_assign (new_scalar_dest, code, first_res, res); new_res = make_ssa_name (new_scalar_dest, new_stmt); gimple_assign_set_lhs (new_stmt, new_res); gsi_insert_before (&exit_gsi, new_stmt, GSI_SAME_STMT); scalar_results[j % group_size] = new_res; } } else /* Not SLP - we have one scalar to keep in SCALAR_RESULTS. */ scalar_results.safe_push (new_temp); } if (STMT_VINFO_VEC_REDUCTION_TYPE (stmt_info) == INTEGER_INDUC_COND_REDUCTION) { /* Earlier we set the initial value to be zero. Check the result and if it is zero then replace with the original initial value. */ tree zero = build_zero_cst (scalar_type); tree zcompare = build2 (EQ_EXPR, boolean_type_node, new_temp, zero); tree tmp = make_ssa_name (new_scalar_dest); epilog_stmt = gimple_build_assign (tmp, COND_EXPR, zcompare, initial_def, new_temp); gsi_insert_before (&exit_gsi, epilog_stmt, GSI_SAME_STMT); scalar_results[0] = tmp; } } vect_finalize_reduction: if (double_reduc) loop = loop->inner; /* 2.5 Adjust the final result by the initial value of the reduction variable. (When such adjustment is not needed, then 'adjustment_def' is zero). For example, if code is PLUS we create: new_temp = loop_exit_def + adjustment_def */ if (adjustment_def) { gcc_assert (!slp_reduc); if (nested_in_vect_loop) { new_phi = new_phis[0]; gcc_assert (TREE_CODE (TREE_TYPE (adjustment_def)) == VECTOR_TYPE); expr = build2 (code, vectype, PHI_RESULT (new_phi), adjustment_def); new_dest = vect_create_destination_var (scalar_dest, vectype); } else { new_temp = scalar_results[0]; gcc_assert (TREE_CODE (TREE_TYPE (adjustment_def)) != VECTOR_TYPE); expr = build2 (code, scalar_type, new_temp, adjustment_def); new_dest = vect_create_destination_var (scalar_dest, scalar_type); } epilog_stmt = gimple_build_assign (new_dest, expr); new_temp = make_ssa_name (new_dest, epilog_stmt); gimple_assign_set_lhs (epilog_stmt, new_temp); gsi_insert_before (&exit_gsi, epilog_stmt, GSI_SAME_STMT); if (nested_in_vect_loop) { set_vinfo_for_stmt (epilog_stmt, new_stmt_vec_info (epilog_stmt, loop_vinfo)); STMT_VINFO_RELATED_STMT (vinfo_for_stmt (epilog_stmt)) = STMT_VINFO_RELATED_STMT (vinfo_for_stmt (new_phi)); if (!double_reduc) scalar_results.quick_push (new_temp); else scalar_results[0] = new_temp; } else scalar_results[0] = new_temp; new_phis[0] = epilog_stmt; } /* 2.6 Handle the loop-exit phis. Replace the uses of scalar loop-exit phis with new adjusted scalar results, i.e., replace use <s_out0> with use <s_out4>. Transform: loop_exit: s_out0 = phi <s_loop> # (scalar) EXIT_PHI v_out1 = phi <VECT_DEF> # NEW_EXIT_PHI v_out2 = reduce <v_out1> s_out3 = extract_field <v_out2, 0> s_out4 = adjust_result <s_out3> use <s_out0> use <s_out0> into: loop_exit: s_out0 = phi <s_loop> # (scalar) EXIT_PHI v_out1 = phi <VECT_DEF> # NEW_EXIT_PHI v_out2 = reduce <v_out1> s_out3 = extract_field <v_out2, 0> s_out4 = adjust_result <s_out3> use <s_out4> use <s_out4> */ /* In SLP reduction chain we reduce vector results into one vector if necessary, hence we set here GROUP_SIZE to 1. SCALAR_DEST is the LHS of the last stmt in the reduction chain, since we are looking for the loop exit phi node. */ if (GROUP_FIRST_ELEMENT (vinfo_for_stmt (stmt))) { gimple *dest_stmt = SLP_TREE_SCALAR_STMTS (slp_node)[group_size - 1]; /* Handle reduction patterns. */ if (STMT_VINFO_RELATED_STMT (vinfo_for_stmt (dest_stmt))) dest_stmt = STMT_VINFO_RELATED_STMT (vinfo_for_stmt (dest_stmt)); scalar_dest = gimple_assign_lhs (dest_stmt); group_size = 1; } /* In SLP we may have several statements in NEW_PHIS and REDUCTION_PHIS (in case that GROUP_SIZE is greater than vectorization factor). Therefore, we need to match SCALAR_RESULTS with corresponding statements. The first (GROUP_SIZE / number of new vector stmts) scalar results correspond to the first vector stmt, etc. (RATIO is equal to (GROUP_SIZE / number of new vector stmts)). */ if (group_size > new_phis.length ()) { ratio = group_size / new_phis.length (); gcc_assert (!(group_size % new_phis.length ())); } else ratio = 1; for (k = 0; k < group_size; k++) { if (k % ratio == 0) { epilog_stmt = new_phis[k / ratio]; reduction_phi = reduction_phis[k / ratio]; if (double_reduc) inner_phi = inner_phis[k / ratio]; } if (slp_reduc) { gimple *current_stmt = SLP_TREE_SCALAR_STMTS (slp_node)[k]; orig_stmt = STMT_VINFO_RELATED_STMT (vinfo_for_stmt (current_stmt)); /* SLP statements can't participate in patterns. */ gcc_assert (!orig_stmt); scalar_dest = gimple_assign_lhs (current_stmt); } phis.create (3); /* Find the loop-closed-use at the loop exit of the original scalar result. (The reduction result is expected to have two immediate uses - one at the latch block, and one at the loop exit). */ FOR_EACH_IMM_USE_FAST (use_p, imm_iter, scalar_dest) if (!flow_bb_inside_loop_p (loop, gimple_bb (USE_STMT (use_p))) && !is_gimple_debug (USE_STMT (use_p))) phis.safe_push (USE_STMT (use_p)); /* While we expect to have found an exit_phi because of loop-closed-ssa form we can end up without one if the scalar cycle is dead. */ FOR_EACH_VEC_ELT (phis, i, exit_phi) { if (outer_loop) { stmt_vec_info exit_phi_vinfo = vinfo_for_stmt (exit_phi); gphi *vect_phi; /* FORNOW. Currently not supporting the case that an inner-loop reduction is not used in the outer-loop (but only outside the outer-loop), unless it is double reduction. */ gcc_assert ((STMT_VINFO_RELEVANT_P (exit_phi_vinfo) && !STMT_VINFO_LIVE_P (exit_phi_vinfo)) || double_reduc); if (double_reduc) STMT_VINFO_VEC_STMT (exit_phi_vinfo) = inner_phi; else STMT_VINFO_VEC_STMT (exit_phi_vinfo) = epilog_stmt; if (!double_reduc || STMT_VINFO_DEF_TYPE (exit_phi_vinfo) != vect_double_reduction_def) continue; /* Handle double reduction: stmt1: s1 = phi <s0, s2> - double reduction phi (outer loop) stmt2: s3 = phi <s1, s4> - (regular) reduc phi (inner loop) stmt3: s4 = use (s3) - (regular) reduc stmt (inner loop) stmt4: s2 = phi <s4> - double reduction stmt (outer loop) At that point the regular reduction (stmt2 and stmt3) is already vectorized, as well as the exit phi node, stmt4. Here we vectorize the phi node of double reduction, stmt1, and update all relevant statements. */ /* Go through all the uses of s2 to find double reduction phi node, i.e., stmt1 above. */ orig_name = PHI_RESULT (exit_phi); FOR_EACH_IMM_USE_STMT (use_stmt, imm_iter, orig_name) { stmt_vec_info use_stmt_vinfo; stmt_vec_info new_phi_vinfo; tree vect_phi_init, preheader_arg, vect_phi_res; basic_block bb = gimple_bb (use_stmt); gimple *use; /* Check that USE_STMT is really double reduction phi node. */ if (gimple_code (use_stmt) != GIMPLE_PHI || gimple_phi_num_args (use_stmt) != 2 || bb->loop_father != outer_loop) continue; use_stmt_vinfo = vinfo_for_stmt (use_stmt); if (!use_stmt_vinfo || STMT_VINFO_DEF_TYPE (use_stmt_vinfo) != vect_double_reduction_def) continue; /* Create vector phi node for double reduction: vs1 = phi <vs0, vs2> vs1 was created previously in this function by a call to vect_get_vec_def_for_operand and is stored in vec_initial_def; vs2 is defined by INNER_PHI, the vectorized EXIT_PHI; vs0 is created here. */ /* Create vector phi node. */ vect_phi = create_phi_node (vec_initial_def, bb); new_phi_vinfo = new_stmt_vec_info (vect_phi, loop_vec_info_for_loop (outer_loop)); set_vinfo_for_stmt (vect_phi, new_phi_vinfo); /* Create vs0 - initial def of the double reduction phi. */ preheader_arg = PHI_ARG_DEF_FROM_EDGE (use_stmt, loop_preheader_edge (outer_loop)); vect_phi_init = get_initial_def_for_reduction (stmt, preheader_arg, NULL); /* Update phi node arguments with vs0 and vs2. */ add_phi_arg (vect_phi, vect_phi_init, loop_preheader_edge (outer_loop), UNKNOWN_LOCATION); add_phi_arg (vect_phi, PHI_RESULT (inner_phi), loop_latch_edge (outer_loop), UNKNOWN_LOCATION); if (dump_enabled_p ()) { dump_printf_loc (MSG_NOTE, vect_location, "created double reduction phi node: "); dump_gimple_stmt (MSG_NOTE, TDF_SLIM, vect_phi, 0); } vect_phi_res = PHI_RESULT (vect_phi); /* Replace the use, i.e., set the correct vs1 in the regular reduction phi node. FORNOW, NCOPIES is always 1, so the loop is redundant. */ use = reduction_phi; for (j = 0; j < ncopies; j++) { edge pr_edge = loop_preheader_edge (loop); SET_PHI_ARG_DEF (use, pr_edge->dest_idx, vect_phi_res); use = STMT_VINFO_RELATED_STMT (vinfo_for_stmt (use)); } } } } phis.release (); if (nested_in_vect_loop) { if (double_reduc) loop = outer_loop; else continue; } phis.create (3); /* Find the loop-closed-use at the loop exit of the original scalar result. (The reduction result is expected to have two immediate uses, one at the latch block, and one at the loop exit). For double reductions we are looking for exit phis of the outer loop. */ FOR_EACH_IMM_USE_FAST (use_p, imm_iter, scalar_dest) { if (!flow_bb_inside_loop_p (loop, gimple_bb (USE_STMT (use_p)))) { if (!is_gimple_debug (USE_STMT (use_p))) phis.safe_push (USE_STMT (use_p)); } else { if (double_reduc && gimple_code (USE_STMT (use_p)) == GIMPLE_PHI) { tree phi_res = PHI_RESULT (USE_STMT (use_p)); FOR_EACH_IMM_USE_FAST (phi_use_p, phi_imm_iter, phi_res) { if (!flow_bb_inside_loop_p (loop, gimple_bb (USE_STMT (phi_use_p))) && !is_gimple_debug (USE_STMT (phi_use_p))) phis.safe_push (USE_STMT (phi_use_p)); } } } } FOR_EACH_VEC_ELT (phis, i, exit_phi) { /* Replace the uses: */ orig_name = PHI_RESULT (exit_phi); scalar_result = scalar_results[k]; FOR_EACH_IMM_USE_STMT (use_stmt, imm_iter, orig_name) FOR_EACH_IMM_USE_ON_STMT (use_p, imm_iter) SET_USE (use_p, scalar_result); } phis.release (); } } /* Function is_nonwrapping_integer_induction. Check if STMT (which is part of loop LOOP) both increments and does not cause overflow. */ static bool is_nonwrapping_integer_induction (gimple *stmt, struct loop *loop) { stmt_vec_info stmt_vinfo = vinfo_for_stmt (stmt); tree base = STMT_VINFO_LOOP_PHI_EVOLUTION_BASE_UNCHANGED (stmt_vinfo); tree step = STMT_VINFO_LOOP_PHI_EVOLUTION_PART (stmt_vinfo); tree lhs_type = TREE_TYPE (gimple_phi_result (stmt)); widest_int ni, max_loop_value, lhs_max; bool overflow = false; /* Make sure the loop is integer based. */ if (TREE_CODE (base) != INTEGER_CST || TREE_CODE (step) != INTEGER_CST) return false; /* Check that the induction increments. */ if (tree_int_cst_sgn (step) == -1) return false; /* Check that the max size of the loop will not wrap. */ if (TYPE_OVERFLOW_UNDEFINED (lhs_type)) return true; if (! max_stmt_executions (loop, &ni)) return false; max_loop_value = wi::mul (wi::to_widest (step), ni, TYPE_SIGN (lhs_type), &overflow); if (overflow) return false; max_loop_value = wi::add (wi::to_widest (base), max_loop_value, TYPE_SIGN (lhs_type), &overflow); if (overflow) return false; return (wi::min_precision (max_loop_value, TYPE_SIGN (lhs_type)) <= TYPE_PRECISION (lhs_type)); } /* Function vectorizable_reduction. Check if STMT performs a reduction operation that can be vectorized. If VEC_STMT is also passed, vectorize the STMT: create a vectorized stmt to replace it, put it in VEC_STMT, and insert it at GSI. Return FALSE if not a vectorizable STMT, TRUE otherwise. This function also handles reduction idioms (patterns) that have been recognized in advance during vect_pattern_recog. In this case, STMT may be of this form: X = pattern_expr (arg0, arg1, ..., X) and it's STMT_VINFO_RELATED_STMT points to the last stmt in the original sequence that had been detected and replaced by the pattern-stmt (STMT). This function also handles reduction of condition expressions, for example: for (int i = 0; i < N; i++) if (a[i] < value) last = a[i]; This is handled by vectorising the loop and creating an additional vector containing the loop indexes for which "a[i] < value" was true. In the function epilogue this is reduced to a single max value and then used to index into the vector of results. In some cases of reduction patterns, the type of the reduction variable X is different than the type of the other arguments of STMT. In such cases, the vectype that is used when transforming STMT into a vector stmt is different than the vectype that is used to determine the vectorization factor, because it consists of a different number of elements than the actual number of elements that are being operated upon in parallel. For example, consider an accumulation of shorts into an int accumulator. On some targets it's possible to vectorize this pattern operating on 8 shorts at a time (hence, the vectype for purposes of determining the vectorization factor should be V8HI); on the other hand, the vectype that is used to create the vector form is actually V4SI (the type of the result). Upon entry to this function, STMT_VINFO_VECTYPE records the vectype that indicates what is the actual level of parallelism (V8HI in the example), so that the right vectorization factor would be derived. This vectype corresponds to the type of arguments to the reduction stmt, and should *NOT* be used to create the vectorized stmt. The right vectype for the vectorized stmt is obtained from the type of the result X: get_vectype_for_scalar_type (TREE_TYPE (X)) This means that, contrary to "regular" reductions (or "regular" stmts in general), the following equation: STMT_VINFO_VECTYPE == get_vectype_for_scalar_type (TREE_TYPE (X)) does *NOT* necessarily hold for reduction patterns. */ bool vectorizable_reduction (gimple *stmt, gimple_stmt_iterator *gsi, gimple **vec_stmt, slp_tree slp_node, slp_instance slp_node_instance) { tree vec_dest; tree scalar_dest; stmt_vec_info stmt_info = vinfo_for_stmt (stmt); tree vectype_out = STMT_VINFO_VECTYPE (stmt_info); tree vectype_in = NULL_TREE; loop_vec_info loop_vinfo = STMT_VINFO_LOOP_VINFO (stmt_info); struct loop *loop = LOOP_VINFO_LOOP (loop_vinfo); enum tree_code code, orig_code, epilog_reduc_code; machine_mode vec_mode; int op_type; optab optab, reduc_optab; tree new_temp = NULL_TREE; gimple *def_stmt; enum vect_def_type dt, cond_reduc_dt = vect_unknown_def_type; tree scalar_type; bool is_simple_use; gimple *orig_stmt; stmt_vec_info orig_stmt_info = NULL; int i; int ncopies; int epilog_copies; stmt_vec_info prev_stmt_info, prev_phi_info; bool single_defuse_cycle = false; gimple *new_stmt = NULL; int j; tree ops[3]; enum vect_def_type dts[3]; bool nested_cycle = false, found_nested_cycle_def = false; bool double_reduc = false; basic_block def_bb; struct loop * def_stmt_loop, *outer_loop = NULL; tree def_arg; gimple *def_arg_stmt; auto_vec<tree> vec_oprnds0; auto_vec<tree> vec_oprnds1; auto_vec<tree> vec_oprnds2; auto_vec<tree> vect_defs; auto_vec<gimple *> phis; int vec_num; tree def0, tem; bool first_p = true; tree cr_index_scalar_type = NULL_TREE, cr_index_vector_type = NULL_TREE; tree cond_reduc_val = NULL_TREE; /* Make sure it was already recognized as a reduction computation. */ if (STMT_VINFO_DEF_TYPE (vinfo_for_stmt (stmt)) != vect_reduction_def && STMT_VINFO_DEF_TYPE (vinfo_for_stmt (stmt)) != vect_nested_cycle) return false; if (nested_in_vect_loop_p (loop, stmt)) { outer_loop = loop; loop = loop->inner; nested_cycle = true; } /* In case of reduction chain we switch to the first stmt in the chain, but we don't update STMT_INFO, since only the last stmt is marked as reduction and has reduction properties. */ if (GROUP_FIRST_ELEMENT (stmt_info) && GROUP_FIRST_ELEMENT (stmt_info) != stmt) { stmt = GROUP_FIRST_ELEMENT (stmt_info); first_p = false; } if (gimple_code (stmt) == GIMPLE_PHI) { /* Analysis is fully done on the reduction stmt invocation. */ if (! vec_stmt) { if (slp_node) slp_node_instance->reduc_phis = slp_node; STMT_VINFO_TYPE (stmt_info) = reduc_vec_info_type; return true; } gimple *reduc_stmt = STMT_VINFO_REDUC_DEF (stmt_info); if (STMT_VINFO_IN_PATTERN_P (vinfo_for_stmt (reduc_stmt))) reduc_stmt = STMT_VINFO_RELATED_STMT (vinfo_for_stmt (reduc_stmt)); gcc_assert (is_gimple_assign (reduc_stmt)); for (unsigned k = 1; k < gimple_num_ops (reduc_stmt); ++k) { tree op = gimple_op (reduc_stmt, k); if (op == gimple_phi_result (stmt)) continue; if (k == 1 && gimple_assign_rhs_code (reduc_stmt) == COND_EXPR) continue; tem = get_vectype_for_scalar_type (TREE_TYPE (op)); if (! vectype_in || TYPE_VECTOR_SUBPARTS (tem) < TYPE_VECTOR_SUBPARTS (vectype_in)) vectype_in = tem; break; } gcc_assert (vectype_in); if (slp_node) ncopies = 1; else ncopies = vect_get_num_copies (loop_vinfo, vectype_in); use_operand_p use_p; gimple *use_stmt; if (ncopies > 1 && (STMT_VINFO_RELEVANT (vinfo_for_stmt (reduc_stmt)) <= vect_used_only_live) && single_imm_use (gimple_phi_result (stmt), &use_p, &use_stmt) && (use_stmt == reduc_stmt || (STMT_VINFO_RELATED_STMT (vinfo_for_stmt (use_stmt)) == reduc_stmt))) single_defuse_cycle = true; /* Create the destination vector */ scalar_dest = gimple_assign_lhs (reduc_stmt); vec_dest = vect_create_destination_var (scalar_dest, vectype_out); if (slp_node) /* The size vect_schedule_slp_instance computes is off for us. */ vec_num = ((LOOP_VINFO_VECT_FACTOR (loop_vinfo) * SLP_TREE_SCALAR_STMTS (slp_node).length ()) / TYPE_VECTOR_SUBPARTS (vectype_in)); else vec_num = 1; /* Generate the reduction PHIs upfront. */ prev_phi_info = NULL; for (j = 0; j < ncopies; j++) { if (j == 0 || !single_defuse_cycle) { for (i = 0; i < vec_num; i++) { /* Create the reduction-phi that defines the reduction operand. */ gimple *new_phi = create_phi_node (vec_dest, loop->header); set_vinfo_for_stmt (new_phi, new_stmt_vec_info (new_phi, loop_vinfo)); if (slp_node) SLP_TREE_VEC_STMTS (slp_node).quick_push (new_phi); else { if (j == 0) STMT_VINFO_VEC_STMT (stmt_info) = *vec_stmt = new_phi; else STMT_VINFO_RELATED_STMT (prev_phi_info) = new_phi; prev_phi_info = vinfo_for_stmt (new_phi); } } } } return true; } /* 1. Is vectorizable reduction? */ /* Not supportable if the reduction variable is used in the loop, unless it's a reduction chain. */ if (STMT_VINFO_RELEVANT (stmt_info) > vect_used_in_outer && !GROUP_FIRST_ELEMENT (stmt_info)) return false; /* Reductions that are not used even in an enclosing outer-loop, are expected to be "live" (used out of the loop). */ if (STMT_VINFO_RELEVANT (stmt_info) == vect_unused_in_scope && !STMT_VINFO_LIVE_P (stmt_info)) return false; /* 2. Has this been recognized as a reduction pattern? Check if STMT represents a pattern that has been recognized in earlier analysis stages. For stmts that represent a pattern, the STMT_VINFO_RELATED_STMT field records the last stmt in the original sequence that constitutes the pattern. */ orig_stmt = STMT_VINFO_RELATED_STMT (vinfo_for_stmt (stmt)); if (orig_stmt) { orig_stmt_info = vinfo_for_stmt (orig_stmt); gcc_assert (STMT_VINFO_IN_PATTERN_P (orig_stmt_info)); gcc_assert (!STMT_VINFO_IN_PATTERN_P (stmt_info)); } /* 3. Check the operands of the operation. The first operands are defined inside the loop body. The last operand is the reduction variable, which is defined by the loop-header-phi. */ gcc_assert (is_gimple_assign (stmt)); /* Flatten RHS. */ switch (get_gimple_rhs_class (gimple_assign_rhs_code (stmt))) { case GIMPLE_BINARY_RHS: code = gimple_assign_rhs_code (stmt); op_type = TREE_CODE_LENGTH (code); gcc_assert (op_type == binary_op); ops[0] = gimple_assign_rhs1 (stmt); ops[1] = gimple_assign_rhs2 (stmt); break; case GIMPLE_TERNARY_RHS: code = gimple_assign_rhs_code (stmt); op_type = TREE_CODE_LENGTH (code); gcc_assert (op_type == ternary_op); ops[0] = gimple_assign_rhs1 (stmt); ops[1] = gimple_assign_rhs2 (stmt); ops[2] = gimple_assign_rhs3 (stmt); break; case GIMPLE_UNARY_RHS: return false; default: gcc_unreachable (); } if (code == COND_EXPR && slp_node) return false; scalar_dest = gimple_assign_lhs (stmt); scalar_type = TREE_TYPE (scalar_dest); if (!POINTER_TYPE_P (scalar_type) && !INTEGRAL_TYPE_P (scalar_type) && !SCALAR_FLOAT_TYPE_P (scalar_type)) return false; /* Do not try to vectorize bit-precision reductions. */ if (!type_has_mode_precision_p (scalar_type)) return false; /* All uses but the last are expected to be defined in the loop. The last use is the reduction variable. In case of nested cycle this assumption is not true: we use reduc_index to record the index of the reduction variable. */ gimple *reduc_def_stmt = NULL; int reduc_index = -1; for (i = 0; i < op_type; i++) { /* The condition of COND_EXPR is checked in vectorizable_condition(). */ if (i == 0 && code == COND_EXPR) continue; is_simple_use = vect_is_simple_use (ops[i], loop_vinfo, &def_stmt, &dts[i], &tem); dt = dts[i]; gcc_assert (is_simple_use); if (dt == vect_reduction_def) { reduc_def_stmt = def_stmt; reduc_index = i; continue; } else if (tem) { /* To properly compute ncopies we are interested in the widest input type in case we're looking at a widening accumulation. */ if (!vectype_in || TYPE_VECTOR_SUBPARTS (vectype_in) > TYPE_VECTOR_SUBPARTS (tem)) vectype_in = tem; } if (dt != vect_internal_def && dt != vect_external_def && dt != vect_constant_def && dt != vect_induction_def && !(dt == vect_nested_cycle && nested_cycle)) return false; if (dt == vect_nested_cycle) { found_nested_cycle_def = true; reduc_def_stmt = def_stmt; reduc_index = i; } if (i == 1 && code == COND_EXPR) { /* Record how value of COND_EXPR is defined. */ if (dt == vect_constant_def) { cond_reduc_dt = dt; cond_reduc_val = ops[i]; } if (dt == vect_induction_def && def_stmt != NULL && is_nonwrapping_integer_induction (def_stmt, loop)) cond_reduc_dt = dt; } } if (!vectype_in) vectype_in = vectype_out; /* When vectorizing a reduction chain w/o SLP the reduction PHI is not directy used in stmt. */ if (reduc_index == -1) { if (orig_stmt) reduc_def_stmt = STMT_VINFO_REDUC_DEF (orig_stmt_info); else reduc_def_stmt = STMT_VINFO_REDUC_DEF (stmt_info); } if (! reduc_def_stmt || gimple_code (reduc_def_stmt) != GIMPLE_PHI) return false; if (!(reduc_index == -1 || dts[reduc_index] == vect_reduction_def || dts[reduc_index] == vect_nested_cycle || ((dts[reduc_index] == vect_internal_def || dts[reduc_index] == vect_external_def || dts[reduc_index] == vect_constant_def || dts[reduc_index] == vect_induction_def) && nested_cycle && found_nested_cycle_def))) { /* For pattern recognized stmts, orig_stmt might be a reduction, but some helper statements for the pattern might not, or might be COND_EXPRs with reduction uses in the condition. */ gcc_assert (orig_stmt); return false; } stmt_vec_info reduc_def_info = vinfo_for_stmt (reduc_def_stmt); enum vect_reduction_type v_reduc_type = STMT_VINFO_REDUC_TYPE (reduc_def_info); gimple *tmp = STMT_VINFO_REDUC_DEF (reduc_def_info); STMT_VINFO_VEC_REDUCTION_TYPE (stmt_info) = v_reduc_type; /* If we have a condition reduction, see if we can simplify it further. */ if (v_reduc_type == COND_REDUCTION) { if (cond_reduc_dt == vect_induction_def) { if (dump_enabled_p ()) dump_printf_loc (MSG_NOTE, vect_location, "condition expression based on " "integer induction.\n"); STMT_VINFO_VEC_REDUCTION_TYPE (stmt_info) = INTEGER_INDUC_COND_REDUCTION; } /* Loop peeling modifies initial value of reduction PHI, which makes the reduction stmt to be transformed different to the original stmt analyzed. We need to record reduction code for CONST_COND_REDUCTION type reduction at analyzing stage, thus it can be used directly at transform stage. */ if (STMT_VINFO_VEC_CONST_COND_REDUC_CODE (stmt_info) == MAX_EXPR || STMT_VINFO_VEC_CONST_COND_REDUC_CODE (stmt_info) == MIN_EXPR) { /* Also set the reduction type to CONST_COND_REDUCTION. */ gcc_assert (cond_reduc_dt == vect_constant_def); STMT_VINFO_VEC_REDUCTION_TYPE (stmt_info) = CONST_COND_REDUCTION; } else if (cond_reduc_dt == vect_constant_def) { enum vect_def_type cond_initial_dt; gimple *def_stmt = SSA_NAME_DEF_STMT (ops[reduc_index]); tree cond_initial_val = PHI_ARG_DEF_FROM_EDGE (def_stmt, loop_preheader_edge (loop)); gcc_assert (cond_reduc_val != NULL_TREE); vect_is_simple_use (cond_initial_val, loop_vinfo, &def_stmt, &cond_initial_dt); if (cond_initial_dt == vect_constant_def && types_compatible_p (TREE_TYPE (cond_initial_val), TREE_TYPE (cond_reduc_val))) { tree e = fold_binary (LE_EXPR, boolean_type_node, cond_initial_val, cond_reduc_val); if (e && (integer_onep (e) || integer_zerop (e))) { if (dump_enabled_p ()) dump_printf_loc (MSG_NOTE, vect_location, "condition expression based on " "compile time constant.\n"); /* Record reduction code at analysis stage. */ STMT_VINFO_VEC_CONST_COND_REDUC_CODE (stmt_info) = integer_onep (e) ? MAX_EXPR : MIN_EXPR; STMT_VINFO_VEC_REDUCTION_TYPE (stmt_info) = CONST_COND_REDUCTION; } } } } if (orig_stmt) gcc_assert (tmp == orig_stmt || GROUP_FIRST_ELEMENT (vinfo_for_stmt (tmp)) == orig_stmt); else /* We changed STMT to be the first stmt in reduction chain, hence we check that in this case the first element in the chain is STMT. */ gcc_assert (stmt == tmp || GROUP_FIRST_ELEMENT (vinfo_for_stmt (tmp)) == stmt); if (STMT_VINFO_LIVE_P (vinfo_for_stmt (reduc_def_stmt))) return false; if (slp_node) ncopies = 1; else ncopies = vect_get_num_copies (loop_vinfo, vectype_in); gcc_assert (ncopies >= 1); vec_mode = TYPE_MODE (vectype_in); if (code == COND_EXPR) { /* Only call during the analysis stage, otherwise we'll lose STMT_VINFO_TYPE. */ if (!vec_stmt && !vectorizable_condition (stmt, gsi, NULL, ops[reduc_index], 0, NULL)) { if (dump_enabled_p ()) dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location, "unsupported condition in reduction\n"); return false; } } else { /* 4. Supportable by target? */ if (code == LSHIFT_EXPR || code == RSHIFT_EXPR || code == LROTATE_EXPR || code == RROTATE_EXPR) { /* Shifts and rotates are only supported by vectorizable_shifts, not vectorizable_reduction. */ if (dump_enabled_p ()) dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location, "unsupported shift or rotation.\n"); return false; } /* 4.1. check support for the operation in the loop */ optab = optab_for_tree_code (code, vectype_in, optab_default); if (!optab) { if (dump_enabled_p ()) dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location, "no optab.\n"); return false; } if (optab_handler (optab, vec_mode) == CODE_FOR_nothing) { if (dump_enabled_p ()) dump_printf (MSG_NOTE, "op not supported by target.\n"); if (GET_MODE_SIZE (vec_mode) != UNITS_PER_WORD || !vect_worthwhile_without_simd_p (loop_vinfo, code)) return false; if (dump_enabled_p ()) dump_printf (MSG_NOTE, "proceeding using word mode.\n"); } /* Worthwhile without SIMD support? */ if (!VECTOR_MODE_P (TYPE_MODE (vectype_in)) && !vect_worthwhile_without_simd_p (loop_vinfo, code)) { if (dump_enabled_p ()) dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location, "not worthwhile without SIMD support.\n"); return false; } } /* 4.2. Check support for the epilog operation. If STMT represents a reduction pattern, then the type of the reduction variable may be different than the type of the rest of the arguments. For example, consider the case of accumulation of shorts into an int accumulator; The original code: S1: int_a = (int) short_a; orig_stmt-> S2: int_acc = plus <int_a ,int_acc>; was replaced with: STMT: int_acc = widen_sum <short_a, int_acc> This means that: 1. The tree-code that is used to create the vector operation in the epilog code (that reduces the partial results) is not the tree-code of STMT, but is rather the tree-code of the original stmt from the pattern that STMT is replacing. I.e, in the example above we want to use 'widen_sum' in the loop, but 'plus' in the epilog. 2. The type (mode) we use to check available target support for the vector operation to be created in the *epilog*, is determined by the type of the reduction variable (in the example above we'd check this: optab_handler (plus_optab, vect_int_mode])). However the type (mode) we use to check available target support for the vector operation to be created *inside the loop*, is determined by the type of the other arguments to STMT (in the example we'd check this: optab_handler (widen_sum_optab, vect_short_mode)). This is contrary to "regular" reductions, in which the types of all the arguments are the same as the type of the reduction variable. For "regular" reductions we can therefore use the same vector type (and also the same tree-code) when generating the epilog code and when generating the code inside the loop. */ if (orig_stmt) { /* This is a reduction pattern: get the vectype from the type of the reduction variable, and get the tree-code from orig_stmt. */ gcc_assert (STMT_VINFO_VEC_REDUCTION_TYPE (stmt_info) == TREE_CODE_REDUCTION); orig_code = gimple_assign_rhs_code (orig_stmt); gcc_assert (vectype_out); vec_mode = TYPE_MODE (vectype_out); } else { /* Regular reduction: use the same vectype and tree-code as used for the vector code inside the loop can be used for the epilog code. */ orig_code = code; if (code == MINUS_EXPR) orig_code = PLUS_EXPR; /* For simple condition reductions, replace with the actual expression we want to base our reduction around. */ if (STMT_VINFO_VEC_REDUCTION_TYPE (stmt_info) == CONST_COND_REDUCTION) { orig_code = STMT_VINFO_VEC_CONST_COND_REDUC_CODE (stmt_info); gcc_assert (orig_code == MAX_EXPR || orig_code == MIN_EXPR); } else if (STMT_VINFO_VEC_REDUCTION_TYPE (stmt_info) == INTEGER_INDUC_COND_REDUCTION) orig_code = MAX_EXPR; } if (nested_cycle) { def_bb = gimple_bb (reduc_def_stmt); def_stmt_loop = def_bb->loop_father; def_arg = PHI_ARG_DEF_FROM_EDGE (reduc_def_stmt, loop_preheader_edge (def_stmt_loop)); if (TREE_CODE (def_arg) == SSA_NAME && (def_arg_stmt = SSA_NAME_DEF_STMT (def_arg)) && gimple_code (def_arg_stmt) == GIMPLE_PHI && flow_bb_inside_loop_p (outer_loop, gimple_bb (def_arg_stmt)) && vinfo_for_stmt (def_arg_stmt) && STMT_VINFO_DEF_TYPE (vinfo_for_stmt (def_arg_stmt)) == vect_double_reduction_def) double_reduc = true; } epilog_reduc_code = ERROR_MARK; if (STMT_VINFO_VEC_REDUCTION_TYPE (stmt_info) != COND_REDUCTION) { if (reduction_code_for_scalar_code (orig_code, &epilog_reduc_code)) { reduc_optab = optab_for_tree_code (epilog_reduc_code, vectype_out, optab_default); if (!reduc_optab) { if (dump_enabled_p ()) dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location, "no optab for reduction.\n"); epilog_reduc_code = ERROR_MARK; } else if (optab_handler (reduc_optab, vec_mode) == CODE_FOR_nothing) { if (dump_enabled_p ()) dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location, "reduc op not supported by target.\n"); epilog_reduc_code = ERROR_MARK; } } else { if (!nested_cycle || double_reduc) { if (dump_enabled_p ()) dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location, "no reduc code for scalar code.\n"); return false; } } } else { int scalar_precision = GET_MODE_PRECISION (SCALAR_TYPE_MODE (scalar_type)); cr_index_scalar_type = make_unsigned_type (scalar_precision); cr_index_vector_type = build_vector_type (cr_index_scalar_type, TYPE_VECTOR_SUBPARTS (vectype_out)); optab = optab_for_tree_code (REDUC_MAX_EXPR, cr_index_vector_type, optab_default); if (optab_handler (optab, TYPE_MODE (cr_index_vector_type)) != CODE_FOR_nothing) epilog_reduc_code = REDUC_MAX_EXPR; } if ((double_reduc || STMT_VINFO_VEC_REDUCTION_TYPE (stmt_info) != TREE_CODE_REDUCTION) && ncopies > 1) { if (dump_enabled_p ()) dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location, "multiple types in double reduction or condition " "reduction.\n"); return false; } /* In case of widenning multiplication by a constant, we update the type of the constant to be the type of the other operand. We check that the constant fits the type in the pattern recognition pass. */ if (code == DOT_PROD_EXPR && !types_compatible_p (TREE_TYPE (ops[0]), TREE_TYPE (ops[1]))) { if (TREE_CODE (ops[0]) == INTEGER_CST) ops[0] = fold_convert (TREE_TYPE (ops[1]), ops[0]); else if (TREE_CODE (ops[1]) == INTEGER_CST) ops[1] = fold_convert (TREE_TYPE (ops[0]), ops[1]); else { if (dump_enabled_p ()) dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location, "invalid types in dot-prod\n"); return false; } } if (STMT_VINFO_VEC_REDUCTION_TYPE (stmt_info) == COND_REDUCTION) { widest_int ni; if (! max_loop_iterations (loop, &ni)) { if (dump_enabled_p ()) dump_printf_loc (MSG_NOTE, vect_location, "loop count not known, cannot create cond " "reduction.\n"); return false; } /* Convert backedges to iterations. */ ni += 1; /* The additional index will be the same type as the condition. Check that the loop can fit into this less one (because we'll use up the zero slot for when there are no matches). */ tree max_index = TYPE_MAX_VALUE (cr_index_scalar_type); if (wi::geu_p (ni, wi::to_widest (max_index))) { if (dump_enabled_p ()) dump_printf_loc (MSG_NOTE, vect_location, "loop size is greater than data size.\n"); return false; } } /* In case the vectorization factor (VF) is bigger than the number of elements that we can fit in a vectype (nunits), we have to generate more than one vector stmt - i.e - we need to "unroll" the vector stmt by a factor VF/nunits. For more details see documentation in vectorizable_operation. */ /* If the reduction is used in an outer loop we need to generate VF intermediate results, like so (e.g. for ncopies=2): r0 = phi (init, r0) r1 = phi (init, r1) r0 = x0 + r0; r1 = x1 + r1; (i.e. we generate VF results in 2 registers). In this case we have a separate def-use cycle for each copy, and therefore for each copy we get the vector def for the reduction variable from the respective phi node created for this copy. Otherwise (the reduction is unused in the loop nest), we can combine together intermediate results, like so (e.g. for ncopies=2): r = phi (init, r) r = x0 + r; r = x1 + r; (i.e. we generate VF/2 results in a single register). In this case for each copy we get the vector def for the reduction variable from the vectorized reduction operation generated in the previous iteration. This only works when we see both the reduction PHI and its only consumer in vectorizable_reduction and there are no intermediate stmts participating. */ use_operand_p use_p; gimple *use_stmt; if (ncopies > 1 && (STMT_VINFO_RELEVANT (stmt_info) <= vect_used_only_live) && single_imm_use (gimple_phi_result (reduc_def_stmt), &use_p, &use_stmt) && (use_stmt == stmt || STMT_VINFO_RELATED_STMT (vinfo_for_stmt (use_stmt)) == stmt)) { single_defuse_cycle = true; epilog_copies = 1; } else epilog_copies = ncopies; /* If the reduction stmt is one of the patterns that have lane reduction embedded we cannot handle the case of ! single_defuse_cycle. */ if ((ncopies > 1 && ! single_defuse_cycle) && (code == DOT_PROD_EXPR || code == WIDEN_SUM_EXPR || code == SAD_EXPR)) { if (dump_enabled_p ()) dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location, "multi def-use cycle not possible for lane-reducing " "reduction operation\n"); return false; } if (!vec_stmt) /* transformation not required. */ { if (first_p) vect_model_reduction_cost (stmt_info, epilog_reduc_code, ncopies); STMT_VINFO_TYPE (stmt_info) = reduc_vec_info_type; return true; } /* Transform. */ if (dump_enabled_p ()) dump_printf_loc (MSG_NOTE, vect_location, "transform reduction.\n"); /* FORNOW: Multiple types are not supported for condition. */ if (code == COND_EXPR) gcc_assert (ncopies == 1); /* Create the destination vector */ vec_dest = vect_create_destination_var (scalar_dest, vectype_out); prev_stmt_info = NULL; prev_phi_info = NULL; if (slp_node) vec_num = SLP_TREE_NUMBER_OF_VEC_STMTS (slp_node); else { vec_num = 1; vec_oprnds0.create (1); vec_oprnds1.create (1); if (op_type == ternary_op) vec_oprnds2.create (1); } phis.create (vec_num); vect_defs.create (vec_num); if (!slp_node) vect_defs.quick_push (NULL_TREE); if (slp_node) phis.splice (SLP_TREE_VEC_STMTS (slp_node_instance->reduc_phis)); else phis.quick_push (STMT_VINFO_VEC_STMT (vinfo_for_stmt (reduc_def_stmt))); for (j = 0; j < ncopies; j++) { if (code == COND_EXPR) { gcc_assert (!slp_node); vectorizable_condition (stmt, gsi, vec_stmt, PHI_RESULT (phis[0]), reduc_index, NULL); /* Multiple types are not supported for condition. */ break; } /* Handle uses. */ if (j == 0) { if (slp_node) { /* Get vec defs for all the operands except the reduction index, ensuring the ordering of the ops in the vector is kept. */ auto_vec<tree, 3> slp_ops; auto_vec<vec<tree>, 3> vec_defs; slp_ops.quick_push (ops[0]); slp_ops.quick_push (ops[1]); if (op_type == ternary_op) slp_ops.quick_push (ops[2]); vect_get_slp_defs (slp_ops, slp_node, &vec_defs); vec_oprnds0.safe_splice (vec_defs[0]); vec_defs[0].release (); vec_oprnds1.safe_splice (vec_defs[1]); vec_defs[1].release (); if (op_type == ternary_op) { vec_oprnds2.safe_splice (vec_defs[2]); vec_defs[2].release (); } } else { vec_oprnds0.quick_push (vect_get_vec_def_for_operand (ops[0], stmt)); vec_oprnds1.quick_push (vect_get_vec_def_for_operand (ops[1], stmt)); if (op_type == ternary_op) vec_oprnds2.quick_push (vect_get_vec_def_for_operand (ops[2], stmt)); } } else { if (!slp_node) { gcc_assert (reduc_index != -1 || ! single_defuse_cycle); if (single_defuse_cycle && reduc_index == 0) vec_oprnds0[0] = gimple_assign_lhs (new_stmt); else vec_oprnds0[0] = vect_get_vec_def_for_stmt_copy (dts[0], vec_oprnds0[0]); if (single_defuse_cycle && reduc_index == 1) vec_oprnds1[0] = gimple_assign_lhs (new_stmt); else vec_oprnds1[0] = vect_get_vec_def_for_stmt_copy (dts[1], vec_oprnds1[0]); if (op_type == ternary_op) { if (single_defuse_cycle && reduc_index == 2) vec_oprnds2[0] = gimple_assign_lhs (new_stmt); else vec_oprnds2[0] = vect_get_vec_def_for_stmt_copy (dts[2], vec_oprnds2[0]); } } } FOR_EACH_VEC_ELT (vec_oprnds0, i, def0) { tree vop[3] = { def0, vec_oprnds1[i], NULL_TREE }; if (op_type == ternary_op) vop[2] = vec_oprnds2[i]; new_temp = make_ssa_name (vec_dest, new_stmt); new_stmt = gimple_build_assign (new_temp, code, vop[0], vop[1], vop[2]); vect_finish_stmt_generation (stmt, new_stmt, gsi); if (slp_node) { SLP_TREE_VEC_STMTS (slp_node).quick_push (new_stmt); vect_defs.quick_push (new_temp); } else vect_defs[0] = new_temp; } if (slp_node) continue; if (j == 0) STMT_VINFO_VEC_STMT (stmt_info) = *vec_stmt = new_stmt; else STMT_VINFO_RELATED_STMT (prev_stmt_info) = new_stmt; prev_stmt_info = vinfo_for_stmt (new_stmt); } /* Finalize the reduction-phi (set its arguments) and create the epilog reduction code. */ if ((!single_defuse_cycle || code == COND_EXPR) && !slp_node) vect_defs[0] = gimple_assign_lhs (*vec_stmt); vect_create_epilog_for_reduction (vect_defs, stmt, reduc_def_stmt, epilog_copies, epilog_reduc_code, phis, double_reduc, slp_node, slp_node_instance); return true; } /* Function vect_min_worthwhile_factor. For a loop where we could vectorize the operation indicated by CODE, return the minimum vectorization factor that makes it worthwhile to use generic vectors. */ int vect_min_worthwhile_factor (enum tree_code code) { switch (code) { case PLUS_EXPR: case MINUS_EXPR: case NEGATE_EXPR: return 4; case BIT_AND_EXPR: case BIT_IOR_EXPR: case BIT_XOR_EXPR: case BIT_NOT_EXPR: return 2; default: return INT_MAX; } } /* Return true if VINFO indicates we are doing loop vectorization and if it is worth decomposing CODE operations into scalar operations for that loop's vectorization factor. */ bool vect_worthwhile_without_simd_p (vec_info *vinfo, tree_code code) { loop_vec_info loop_vinfo = dyn_cast <loop_vec_info> (vinfo); return (loop_vinfo && (LOOP_VINFO_VECT_FACTOR (loop_vinfo) >= vect_min_worthwhile_factor (code))); } /* Function vectorizable_induction Check if PHI performs an induction computation that can be vectorized. If VEC_STMT is also passed, vectorize the induction PHI: create a vectorized phi to replace it, put it in VEC_STMT, and add it to the same basic block. Return FALSE if not a vectorizable STMT, TRUE otherwise. */ bool vectorizable_induction (gimple *phi, gimple_stmt_iterator *gsi ATTRIBUTE_UNUSED, gimple **vec_stmt, slp_tree slp_node) { stmt_vec_info stmt_info = vinfo_for_stmt (phi); loop_vec_info loop_vinfo = STMT_VINFO_LOOP_VINFO (stmt_info); struct loop *loop = LOOP_VINFO_LOOP (loop_vinfo); unsigned ncopies; bool nested_in_vect_loop = false; struct loop *iv_loop; tree vec_def; edge pe = loop_preheader_edge (loop); basic_block new_bb; tree new_vec, vec_init, vec_step, t; tree new_name; gimple *new_stmt; gphi *induction_phi; tree induc_def, vec_dest; tree init_expr, step_expr; int vf = LOOP_VINFO_VECT_FACTOR (loop_vinfo); unsigned i; tree expr; gimple_seq stmts; imm_use_iterator imm_iter; use_operand_p use_p; gimple *exit_phi; edge latch_e; tree loop_arg; gimple_stmt_iterator si; basic_block bb = gimple_bb (phi); if (gimple_code (phi) != GIMPLE_PHI) return false; if (!STMT_VINFO_RELEVANT_P (stmt_info)) return false; /* Make sure it was recognized as induction computation. */ if (STMT_VINFO_DEF_TYPE (stmt_info) != vect_induction_def) return false; tree vectype = STMT_VINFO_VECTYPE (stmt_info); unsigned nunits = TYPE_VECTOR_SUBPARTS (vectype); if (slp_node) ncopies = 1; else ncopies = vect_get_num_copies (loop_vinfo, vectype); gcc_assert (ncopies >= 1); /* FORNOW. These restrictions should be relaxed. */ if (nested_in_vect_loop_p (loop, phi)) { imm_use_iterator imm_iter; use_operand_p use_p; gimple *exit_phi; edge latch_e; tree loop_arg; if (ncopies > 1) { if (dump_enabled_p ()) dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location, "multiple types in nested loop.\n"); return false; } /* FORNOW: outer loop induction with SLP not supported. */ if (STMT_SLP_TYPE (stmt_info)) return false; exit_phi = NULL; latch_e = loop_latch_edge (loop->inner); loop_arg = PHI_ARG_DEF_FROM_EDGE (phi, latch_e); FOR_EACH_IMM_USE_FAST (use_p, imm_iter, loop_arg) { gimple *use_stmt = USE_STMT (use_p); if (is_gimple_debug (use_stmt)) continue; if (!flow_bb_inside_loop_p (loop->inner, gimple_bb (use_stmt))) { exit_phi = use_stmt; break; } } if (exit_phi) { stmt_vec_info exit_phi_vinfo = vinfo_for_stmt (exit_phi); if (!(STMT_VINFO_RELEVANT_P (exit_phi_vinfo) && !STMT_VINFO_LIVE_P (exit_phi_vinfo))) { if (dump_enabled_p ()) dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location, "inner-loop induction only used outside " "of the outer vectorized loop.\n"); return false; } } nested_in_vect_loop = true; iv_loop = loop->inner; } else iv_loop = loop; gcc_assert (iv_loop == (gimple_bb (phi))->loop_father); if (!vec_stmt) /* transformation not required. */ { STMT_VINFO_TYPE (stmt_info) = induc_vec_info_type; if (dump_enabled_p ()) dump_printf_loc (MSG_NOTE, vect_location, "=== vectorizable_induction ===\n"); vect_model_induction_cost (stmt_info, ncopies); return true; } /* Transform. */ /* Compute a vector variable, initialized with the first VF values of the induction variable. E.g., for an iv with IV_PHI='X' and evolution S, for a vector of 4 units, we want to compute: [X, X + S, X + 2*S, X + 3*S]. */ if (dump_enabled_p ()) dump_printf_loc (MSG_NOTE, vect_location, "transform induction phi.\n"); latch_e = loop_latch_edge (iv_loop); loop_arg = PHI_ARG_DEF_FROM_EDGE (phi, latch_e); step_expr = STMT_VINFO_LOOP_PHI_EVOLUTION_PART (stmt_info); gcc_assert (step_expr != NULL_TREE); pe = loop_preheader_edge (iv_loop); init_expr = PHI_ARG_DEF_FROM_EDGE (phi, loop_preheader_edge (iv_loop)); /* Convert the step to the desired type. */ stmts = NULL; step_expr = gimple_convert (&stmts, TREE_TYPE (vectype), step_expr); if (stmts) { new_bb = gsi_insert_seq_on_edge_immediate (pe, stmts); gcc_assert (!new_bb); } /* Find the first insertion point in the BB. */ si = gsi_after_labels (bb); /* For SLP induction we have to generate several IVs as for example with group size 3 we need [i, i, i, i + S] [i + S, i + S, i + 2*S, i + 2*S] [i + 2*S, i + 3*S, i + 3*S, i + 3*S]. The step is the same uniform [VF*S, VF*S, VF*S, VF*S] for all. */ if (slp_node) { /* Convert the init to the desired type. */ stmts = NULL; init_expr = gimple_convert (&stmts, TREE_TYPE (vectype), init_expr); if (stmts) { new_bb = gsi_insert_seq_on_edge_immediate (pe, stmts); gcc_assert (!new_bb); } /* Generate [VF*S, VF*S, ... ]. */ if (SCALAR_FLOAT_TYPE_P (TREE_TYPE (step_expr))) { expr = build_int_cst (integer_type_node, vf); expr = fold_convert (TREE_TYPE (step_expr), expr); } else expr = build_int_cst (TREE_TYPE (step_expr), vf); new_name = fold_build2 (MULT_EXPR, TREE_TYPE (step_expr), expr, step_expr); if (! CONSTANT_CLASS_P (new_name)) new_name = vect_init_vector (phi, new_name, TREE_TYPE (step_expr), NULL); new_vec = build_vector_from_val (vectype, new_name); vec_step = vect_init_vector (phi, new_vec, vectype, NULL); /* Now generate the IVs. */ unsigned group_size = SLP_TREE_SCALAR_STMTS (slp_node).length (); unsigned nvects = SLP_TREE_NUMBER_OF_VEC_STMTS (slp_node); unsigned elts = nunits * nvects; unsigned nivs = least_common_multiple (group_size, nunits) / nunits; gcc_assert (elts % group_size == 0); tree elt = init_expr; unsigned ivn; for (ivn = 0; ivn < nivs; ++ivn) { auto_vec<tree, 32> elts (nunits); stmts = NULL; for (unsigned eltn = 0; eltn < nunits; ++eltn) { if (ivn*nunits + eltn >= group_size && (ivn*nunits + eltn) % group_size == 0) elt = gimple_build (&stmts, PLUS_EXPR, TREE_TYPE (elt), elt, step_expr); elts.quick_push (elt); } vec_init = gimple_build_vector (&stmts, vectype, elts); if (stmts) { new_bb = gsi_insert_seq_on_edge_immediate (pe, stmts); gcc_assert (!new_bb); } /* Create the induction-phi that defines the induction-operand. */ vec_dest = vect_get_new_vect_var (vectype, vect_simple_var, "vec_iv_"); induction_phi = create_phi_node (vec_dest, iv_loop->header); set_vinfo_for_stmt (induction_phi, new_stmt_vec_info (induction_phi, loop_vinfo)); induc_def = PHI_RESULT (induction_phi); /* Create the iv update inside the loop */ vec_def = make_ssa_name (vec_dest); new_stmt = gimple_build_assign (vec_def, PLUS_EXPR, induc_def, vec_step); gsi_insert_before (&si, new_stmt, GSI_SAME_STMT); set_vinfo_for_stmt (new_stmt, new_stmt_vec_info (new_stmt, loop_vinfo)); /* Set the arguments of the phi node: */ add_phi_arg (induction_phi, vec_init, pe, UNKNOWN_LOCATION); add_phi_arg (induction_phi, vec_def, loop_latch_edge (iv_loop), UNKNOWN_LOCATION); SLP_TREE_VEC_STMTS (slp_node).quick_push (induction_phi); } /* Re-use IVs when we can. */ if (ivn < nvects) { unsigned vfp = least_common_multiple (group_size, nunits) / group_size; /* Generate [VF'*S, VF'*S, ... ]. */ if (SCALAR_FLOAT_TYPE_P (TREE_TYPE (step_expr))) { expr = build_int_cst (integer_type_node, vfp); expr = fold_convert (TREE_TYPE (step_expr), expr); } else expr = build_int_cst (TREE_TYPE (step_expr), vfp); new_name = fold_build2 (MULT_EXPR, TREE_TYPE (step_expr), expr, step_expr); if (! CONSTANT_CLASS_P (new_name)) new_name = vect_init_vector (phi, new_name, TREE_TYPE (step_expr), NULL); new_vec = build_vector_from_val (vectype, new_name); vec_step = vect_init_vector (phi, new_vec, vectype, NULL); for (; ivn < nvects; ++ivn) { gimple *iv = SLP_TREE_VEC_STMTS (slp_node)[ivn - nivs]; tree def; if (gimple_code (iv) == GIMPLE_PHI) def = gimple_phi_result (iv); else def = gimple_assign_lhs (iv); new_stmt = gimple_build_assign (make_ssa_name (vectype), PLUS_EXPR, def, vec_step); if (gimple_code (iv) == GIMPLE_PHI) gsi_insert_before (&si, new_stmt, GSI_SAME_STMT); else { gimple_stmt_iterator tgsi = gsi_for_stmt (iv); gsi_insert_after (&tgsi, new_stmt, GSI_CONTINUE_LINKING); } set_vinfo_for_stmt (new_stmt, new_stmt_vec_info (new_stmt, loop_vinfo)); SLP_TREE_VEC_STMTS (slp_node).quick_push (new_stmt); } } return true; } /* Create the vector that holds the initial_value of the induction. */ if (nested_in_vect_loop) { /* iv_loop is nested in the loop to be vectorized. init_expr had already been created during vectorization of previous stmts. We obtain it from the STMT_VINFO_VEC_STMT of the defining stmt. */ vec_init = vect_get_vec_def_for_operand (init_expr, phi); /* If the initial value is not of proper type, convert it. */ if (!useless_type_conversion_p (vectype, TREE_TYPE (vec_init))) { new_stmt = gimple_build_assign (vect_get_new_ssa_name (vectype, vect_simple_var, "vec_iv_"), VIEW_CONVERT_EXPR, build1 (VIEW_CONVERT_EXPR, vectype, vec_init)); vec_init = gimple_assign_lhs (new_stmt); new_bb = gsi_insert_on_edge_immediate (loop_preheader_edge (iv_loop), new_stmt); gcc_assert (!new_bb); set_vinfo_for_stmt (new_stmt, new_stmt_vec_info (new_stmt, loop_vinfo)); } } else { /* iv_loop is the loop to be vectorized. Create: vec_init = [X, X+S, X+2*S, X+3*S] (S = step_expr, X = init_expr) */ stmts = NULL; new_name = gimple_convert (&stmts, TREE_TYPE (vectype), init_expr); auto_vec<tree, 32> elts (nunits); elts.quick_push (new_name); for (i = 1; i < nunits; i++) { /* Create: new_name_i = new_name + step_expr */ new_name = gimple_build (&stmts, PLUS_EXPR, TREE_TYPE (new_name), new_name, step_expr); elts.quick_push (new_name); } /* Create a vector from [new_name_0, new_name_1, ..., new_name_nunits-1] */ vec_init = gimple_build_vector (&stmts, vectype, elts); if (stmts) { new_bb = gsi_insert_seq_on_edge_immediate (pe, stmts); gcc_assert (!new_bb); } } /* Create the vector that holds the step of the induction. */ if (nested_in_vect_loop) /* iv_loop is nested in the loop to be vectorized. Generate: vec_step = [S, S, S, S] */ new_name = step_expr; else { /* iv_loop is the loop to be vectorized. Generate: vec_step = [VF*S, VF*S, VF*S, VF*S] */ gimple_seq seq = NULL; if (SCALAR_FLOAT_TYPE_P (TREE_TYPE (step_expr))) { expr = build_int_cst (integer_type_node, vf); expr = gimple_build (&seq, FLOAT_EXPR, TREE_TYPE (step_expr), expr); } else expr = build_int_cst (TREE_TYPE (step_expr), vf); new_name = gimple_build (&seq, MULT_EXPR, TREE_TYPE (step_expr), expr, step_expr); if (seq) { new_bb = gsi_insert_seq_on_edge_immediate (pe, seq); gcc_assert (!new_bb); } } t = unshare_expr (new_name); gcc_assert (CONSTANT_CLASS_P (new_name) || TREE_CODE (new_name) == SSA_NAME); new_vec = build_vector_from_val (vectype, t); vec_step = vect_init_vector (phi, new_vec, vectype, NULL); /* Create the following def-use cycle: loop prolog: vec_init = ... vec_step = ... loop: vec_iv = PHI <vec_init, vec_loop> ... STMT ... vec_loop = vec_iv + vec_step; */ /* Create the induction-phi that defines the induction-operand. */ vec_dest = vect_get_new_vect_var (vectype, vect_simple_var, "vec_iv_"); induction_phi = create_phi_node (vec_dest, iv_loop->header); set_vinfo_for_stmt (induction_phi, new_stmt_vec_info (induction_phi, loop_vinfo)); induc_def = PHI_RESULT (induction_phi); /* Create the iv update inside the loop */ vec_def = make_ssa_name (vec_dest); new_stmt = gimple_build_assign (vec_def, PLUS_EXPR, induc_def, vec_step); gsi_insert_before (&si, new_stmt, GSI_SAME_STMT); set_vinfo_for_stmt (new_stmt, new_stmt_vec_info (new_stmt, loop_vinfo)); /* Set the arguments of the phi node: */ add_phi_arg (induction_phi, vec_init, pe, UNKNOWN_LOCATION); add_phi_arg (induction_phi, vec_def, loop_latch_edge (iv_loop), UNKNOWN_LOCATION); STMT_VINFO_VEC_STMT (stmt_info) = *vec_stmt = induction_phi; /* In case that vectorization factor (VF) is bigger than the number of elements that we can fit in a vectype (nunits), we have to generate more than one vector stmt - i.e - we need to "unroll" the vector stmt by a factor VF/nunits. For more details see documentation in vectorizable_operation. */ if (ncopies > 1) { gimple_seq seq = NULL; stmt_vec_info prev_stmt_vinfo; /* FORNOW. This restriction should be relaxed. */ gcc_assert (!nested_in_vect_loop); /* Create the vector that holds the step of the induction. */ if (SCALAR_FLOAT_TYPE_P (TREE_TYPE (step_expr))) { expr = build_int_cst (integer_type_node, nunits); expr = gimple_build (&seq, FLOAT_EXPR, TREE_TYPE (step_expr), expr); } else expr = build_int_cst (TREE_TYPE (step_expr), nunits); new_name = gimple_build (&seq, MULT_EXPR, TREE_TYPE (step_expr), expr, step_expr); if (seq) { new_bb = gsi_insert_seq_on_edge_immediate (pe, seq); gcc_assert (!new_bb); } t = unshare_expr (new_name); gcc_assert (CONSTANT_CLASS_P (new_name) || TREE_CODE (new_name) == SSA_NAME); new_vec = build_vector_from_val (vectype, t); vec_step = vect_init_vector (phi, new_vec, vectype, NULL); vec_def = induc_def; prev_stmt_vinfo = vinfo_for_stmt (induction_phi); for (i = 1; i < ncopies; i++) { /* vec_i = vec_prev + vec_step */ new_stmt = gimple_build_assign (vec_dest, PLUS_EXPR, vec_def, vec_step); vec_def = make_ssa_name (vec_dest, new_stmt); gimple_assign_set_lhs (new_stmt, vec_def); gsi_insert_before (&si, new_stmt, GSI_SAME_STMT); set_vinfo_for_stmt (new_stmt, new_stmt_vec_info (new_stmt, loop_vinfo)); STMT_VINFO_RELATED_STMT (prev_stmt_vinfo) = new_stmt; prev_stmt_vinfo = vinfo_for_stmt (new_stmt); } } if (nested_in_vect_loop) { /* Find the loop-closed exit-phi of the induction, and record the final vector of induction results: */ exit_phi = NULL; FOR_EACH_IMM_USE_FAST (use_p, imm_iter, loop_arg) { gimple *use_stmt = USE_STMT (use_p); if (is_gimple_debug (use_stmt)) continue; if (!flow_bb_inside_loop_p (iv_loop, gimple_bb (use_stmt))) { exit_phi = use_stmt; break; } } if (exit_phi) { stmt_vec_info stmt_vinfo = vinfo_for_stmt (exit_phi); /* FORNOW. Currently not supporting the case that an inner-loop induction is not used in the outer-loop (i.e. only outside the outer-loop). */ gcc_assert (STMT_VINFO_RELEVANT_P (stmt_vinfo) && !STMT_VINFO_LIVE_P (stmt_vinfo)); STMT_VINFO_VEC_STMT (stmt_vinfo) = new_stmt; if (dump_enabled_p ()) { dump_printf_loc (MSG_NOTE, vect_location, "vector of inductions after inner-loop:"); dump_gimple_stmt (MSG_NOTE, TDF_SLIM, new_stmt, 0); } } } if (dump_enabled_p ()) { dump_printf_loc (MSG_NOTE, vect_location, "transform induction: created def-use cycle: "); dump_gimple_stmt (MSG_NOTE, TDF_SLIM, induction_phi, 0); dump_gimple_stmt (MSG_NOTE, TDF_SLIM, SSA_NAME_DEF_STMT (vec_def), 0); } return true; } /* Function vectorizable_live_operation. STMT computes a value that is used outside the loop. Check if it can be supported. */ bool vectorizable_live_operation (gimple *stmt, gimple_stmt_iterator *gsi ATTRIBUTE_UNUSED, slp_tree slp_node, int slp_index, gimple **vec_stmt) { stmt_vec_info stmt_info = vinfo_for_stmt (stmt); loop_vec_info loop_vinfo = STMT_VINFO_LOOP_VINFO (stmt_info); struct loop *loop = LOOP_VINFO_LOOP (loop_vinfo); imm_use_iterator imm_iter; tree lhs, lhs_type, bitsize, vec_bitsize; tree vectype = STMT_VINFO_VECTYPE (stmt_info); int nunits = TYPE_VECTOR_SUBPARTS (vectype); int ncopies; gimple *use_stmt; auto_vec<tree> vec_oprnds; gcc_assert (STMT_VINFO_LIVE_P (stmt_info)); if (STMT_VINFO_DEF_TYPE (stmt_info) == vect_reduction_def) return false; /* FORNOW. CHECKME. */ if (nested_in_vect_loop_p (loop, stmt)) return false; /* If STMT is not relevant and it is a simple assignment and its inputs are invariant then it can remain in place, unvectorized. The original last scalar value that it computes will be used. */ if (!STMT_VINFO_RELEVANT_P (stmt_info)) { gcc_assert (is_simple_and_all_uses_invariant (stmt, loop_vinfo)); if (dump_enabled_p ()) dump_printf_loc (MSG_NOTE, vect_location, "statement is simple and uses invariant. Leaving in " "place.\n"); return true; } if (slp_node) ncopies = 1; else ncopies = vect_get_num_copies (loop_vinfo, vectype); if (!vec_stmt) /* No transformation required. */ return true; /* If stmt has a related stmt, then use that for getting the lhs. */ if (is_pattern_stmt_p (stmt_info)) stmt = STMT_VINFO_RELATED_STMT (stmt_info); lhs = (is_a <gphi *> (stmt)) ? gimple_phi_result (stmt) : gimple_get_lhs (stmt); lhs_type = TREE_TYPE (lhs); bitsize = (VECTOR_BOOLEAN_TYPE_P (vectype) ? bitsize_int (TYPE_PRECISION (TREE_TYPE (vectype))) : TYPE_SIZE (TREE_TYPE (vectype))); vec_bitsize = TYPE_SIZE (vectype); /* Get the vectorized lhs of STMT and the lane to use (counted in bits). */ tree vec_lhs, bitstart; if (slp_node) { gcc_assert (slp_index >= 0); int num_scalar = SLP_TREE_SCALAR_STMTS (slp_node).length (); int num_vec = SLP_TREE_NUMBER_OF_VEC_STMTS (slp_node); /* Get the last occurrence of the scalar index from the concatenation of all the slp vectors. Calculate which slp vector it is and the index within. */ int pos = (num_vec * nunits) - num_scalar + slp_index; int vec_entry = pos / nunits; int vec_index = pos % nunits; /* Get the correct slp vectorized stmt. */ vec_lhs = gimple_get_lhs (SLP_TREE_VEC_STMTS (slp_node)[vec_entry]); /* Get entry to use. */ bitstart = bitsize_int (vec_index); bitstart = int_const_binop (MULT_EXPR, bitsize, bitstart); } else { enum vect_def_type dt = STMT_VINFO_DEF_TYPE (stmt_info); vec_lhs = vect_get_vec_def_for_operand_1 (stmt, dt); /* For multiple copies, get the last copy. */ for (int i = 1; i < ncopies; ++i) vec_lhs = vect_get_vec_def_for_stmt_copy (vect_unknown_def_type, vec_lhs); /* Get the last lane in the vector. */ bitstart = int_const_binop (MINUS_EXPR, vec_bitsize, bitsize); } /* Create a new vectorized stmt for the uses of STMT and insert outside the loop. */ gimple_seq stmts = NULL; tree bftype = TREE_TYPE (vectype); if (VECTOR_BOOLEAN_TYPE_P (vectype)) bftype = build_nonstandard_integer_type (tree_to_uhwi (bitsize), 1); tree new_tree = build3 (BIT_FIELD_REF, bftype, vec_lhs, bitsize, bitstart); new_tree = force_gimple_operand (fold_convert (lhs_type, new_tree), &stmts, true, NULL_TREE); if (stmts) gsi_insert_seq_on_edge_immediate (single_exit (loop), stmts); /* Replace use of lhs with newly computed result. If the use stmt is a single arg PHI, just replace all uses of PHI result. It's necessary because lcssa PHI defining lhs may be before newly inserted stmt. */ use_operand_p use_p; FOR_EACH_IMM_USE_STMT (use_stmt, imm_iter, lhs) if (!flow_bb_inside_loop_p (loop, gimple_bb (use_stmt)) && !is_gimple_debug (use_stmt)) { if (gimple_code (use_stmt) == GIMPLE_PHI && gimple_phi_num_args (use_stmt) == 1) { replace_uses_by (gimple_phi_result (use_stmt), new_tree); } else { FOR_EACH_IMM_USE_ON_STMT (use_p, imm_iter) SET_USE (use_p, new_tree); } update_stmt (use_stmt); } return true; } /* Kill any debug uses outside LOOP of SSA names defined in STMT. */ static void vect_loop_kill_debug_uses (struct loop *loop, gimple *stmt) { ssa_op_iter op_iter; imm_use_iterator imm_iter; def_operand_p def_p; gimple *ustmt; FOR_EACH_PHI_OR_STMT_DEF (def_p, stmt, op_iter, SSA_OP_DEF) { FOR_EACH_IMM_USE_STMT (ustmt, imm_iter, DEF_FROM_PTR (def_p)) { basic_block bb; if (!is_gimple_debug (ustmt)) continue; bb = gimple_bb (ustmt); if (!flow_bb_inside_loop_p (loop, bb)) { if (gimple_debug_bind_p (ustmt)) { if (dump_enabled_p ()) dump_printf_loc (MSG_NOTE, vect_location, "killing debug use\n"); gimple_debug_bind_reset_value (ustmt); update_stmt (ustmt); } else gcc_unreachable (); } } } } /* Given loop represented by LOOP_VINFO, return true if computation of LOOP_VINFO_NITERS (= LOOP_VINFO_NITERSM1 + 1) doesn't overflow, false otherwise. */ static bool loop_niters_no_overflow (loop_vec_info loop_vinfo) { /* Constant case. */ if (LOOP_VINFO_NITERS_KNOWN_P (loop_vinfo)) { tree cst_niters = LOOP_VINFO_NITERS (loop_vinfo); tree cst_nitersm1 = LOOP_VINFO_NITERSM1 (loop_vinfo); gcc_assert (TREE_CODE (cst_niters) == INTEGER_CST); gcc_assert (TREE_CODE (cst_nitersm1) == INTEGER_CST); if (wi::to_widest (cst_nitersm1) < wi::to_widest (cst_niters)) return true; } widest_int max; struct loop *loop = LOOP_VINFO_LOOP (loop_vinfo); /* Check the upper bound of loop niters. */ if (get_max_loop_iterations (loop, &max)) { tree type = TREE_TYPE (LOOP_VINFO_NITERS (loop_vinfo)); signop sgn = TYPE_SIGN (type); widest_int type_max = widest_int::from (wi::max_value (type), sgn); if (max < type_max) return true; } return false; } /* Scale profiling counters by estimation for LOOP which is vectorized by factor VF. */ static void scale_profile_for_vect_loop (struct loop *loop, unsigned vf) { edge preheader = loop_preheader_edge (loop); /* Reduce loop iterations by the vectorization factor. */ gcov_type new_est_niter = niter_for_unrolled_loop (loop, vf); profile_count freq_h = loop->header->count, freq_e = preheader->count (); /* Use frequency only if counts are zero. */ if (!(freq_h > 0) && !(freq_e > 0)) { freq_h = profile_count::from_gcov_type (loop->header->frequency); freq_e = profile_count::from_gcov_type (EDGE_FREQUENCY (preheader)); } if (freq_h > 0) { profile_probability p; /* Avoid dropping loop body profile counter to 0 because of zero count in loop's preheader. */ if (!(freq_e > profile_count::from_gcov_type (1))) freq_e = profile_count::from_gcov_type (1); p = freq_e.apply_scale (new_est_niter + 1, 1).probability_in (freq_h); scale_loop_frequencies (loop, p); } edge exit_e = single_exit (loop); exit_e->probability = profile_probability::always () .apply_scale (1, new_est_niter + 1); edge exit_l = single_pred_edge (loop->latch); profile_probability prob = exit_l->probability; exit_l->probability = exit_e->probability.invert (); if (prob.initialized_p () && exit_l->probability.initialized_p ()) scale_bbs_frequencies (&loop->latch, 1, exit_l->probability / prob); } /* Function vect_transform_loop. The analysis phase has determined that the loop is vectorizable. Vectorize the loop - created vectorized stmts to replace the scalar stmts in the loop, and update the loop exit condition. Returns scalar epilogue loop if any. */ struct loop * vect_transform_loop (loop_vec_info loop_vinfo) { struct loop *loop = LOOP_VINFO_LOOP (loop_vinfo); struct loop *epilogue = NULL; basic_block *bbs = LOOP_VINFO_BBS (loop_vinfo); int nbbs = loop->num_nodes; int i; tree niters_vector = NULL; int vf = LOOP_VINFO_VECT_FACTOR (loop_vinfo); bool grouped_store; bool slp_scheduled = false; gimple *stmt, *pattern_stmt; gimple_seq pattern_def_seq = NULL; gimple_stmt_iterator pattern_def_si = gsi_none (); bool transform_pattern_stmt = false; bool check_profitability = false; int th; if (dump_enabled_p ()) dump_printf_loc (MSG_NOTE, vect_location, "=== vec_transform_loop ===\n"); /* Use the more conservative vectorization threshold. If the number of iterations is constant assume the cost check has been performed by our caller. If the threshold makes all loops profitable that run at least the vectorization factor number of times checking is pointless, too. */ th = LOOP_VINFO_COST_MODEL_THRESHOLD (loop_vinfo); if (th >= LOOP_VINFO_VECT_FACTOR (loop_vinfo) && !LOOP_VINFO_NITERS_KNOWN_P (loop_vinfo)) { if (dump_enabled_p ()) dump_printf_loc (MSG_NOTE, vect_location, "Profitability threshold is %d loop iterations.\n", th); check_profitability = true; } /* Make sure there exists a single-predecessor exit bb. Do this before versioning. */ edge e = single_exit (loop); if (! single_pred_p (e->dest)) { split_loop_exit_edge (e); if (dump_enabled_p ()) dump_printf (MSG_NOTE, "split exit edge\n"); } /* Version the loop first, if required, so the profitability check comes first. */ if (LOOP_REQUIRES_VERSIONING (loop_vinfo)) { vect_loop_versioning (loop_vinfo, th, check_profitability); check_profitability = false; } /* Make sure there exists a single-predecessor exit bb also on the scalar loop copy. Do this after versioning but before peeling so CFG structure is fine for both scalar and if-converted loop to make slpeel_duplicate_current_defs_from_edges face matched loop closed PHI nodes on the exit. */ if (LOOP_VINFO_SCALAR_LOOP (loop_vinfo)) { e = single_exit (LOOP_VINFO_SCALAR_LOOP (loop_vinfo)); if (! single_pred_p (e->dest)) { split_loop_exit_edge (e); if (dump_enabled_p ()) dump_printf (MSG_NOTE, "split exit edge of scalar loop\n"); } } tree niters = vect_build_loop_niters (loop_vinfo); LOOP_VINFO_NITERS_UNCHANGED (loop_vinfo) = niters; tree nitersm1 = unshare_expr (LOOP_VINFO_NITERSM1 (loop_vinfo)); bool niters_no_overflow = loop_niters_no_overflow (loop_vinfo); epilogue = vect_do_peeling (loop_vinfo, niters, nitersm1, &niters_vector, th, check_profitability, niters_no_overflow); if (niters_vector == NULL_TREE) { if (LOOP_VINFO_NITERS_KNOWN_P (loop_vinfo)) niters_vector = build_int_cst (TREE_TYPE (LOOP_VINFO_NITERS (loop_vinfo)), LOOP_VINFO_INT_NITERS (loop_vinfo) / vf); else vect_gen_vector_loop_niters (loop_vinfo, niters, &niters_vector, niters_no_overflow); } /* 1) Make sure the loop header has exactly two entries 2) Make sure we have a preheader basic block. */ gcc_assert (EDGE_COUNT (loop->header->preds) == 2); split_edge (loop_preheader_edge (loop)); /* FORNOW: the vectorizer supports only loops which body consist of one basic block (header + empty latch). When the vectorizer will support more involved loop forms, the order by which the BBs are traversed need to be reconsidered. */ for (i = 0; i < nbbs; i++) { basic_block bb = bbs[i]; stmt_vec_info stmt_info; for (gphi_iterator si = gsi_start_phis (bb); !gsi_end_p (si); gsi_next (&si)) { gphi *phi = si.phi (); if (dump_enabled_p ()) { dump_printf_loc (MSG_NOTE, vect_location, "------>vectorizing phi: "); dump_gimple_stmt (MSG_NOTE, TDF_SLIM, phi, 0); } stmt_info = vinfo_for_stmt (phi); if (!stmt_info) continue; if (MAY_HAVE_DEBUG_STMTS && !STMT_VINFO_LIVE_P (stmt_info)) vect_loop_kill_debug_uses (loop, phi); if (!STMT_VINFO_RELEVANT_P (stmt_info) && !STMT_VINFO_LIVE_P (stmt_info)) continue; if (STMT_VINFO_VECTYPE (stmt_info) && (TYPE_VECTOR_SUBPARTS (STMT_VINFO_VECTYPE (stmt_info)) != (unsigned HOST_WIDE_INT) vf) && dump_enabled_p ()) dump_printf_loc (MSG_NOTE, vect_location, "multiple-types.\n"); if ((STMT_VINFO_DEF_TYPE (stmt_info) == vect_induction_def || STMT_VINFO_DEF_TYPE (stmt_info) == vect_reduction_def || STMT_VINFO_DEF_TYPE (stmt_info) == vect_nested_cycle) && ! PURE_SLP_STMT (stmt_info)) { if (dump_enabled_p ()) dump_printf_loc (MSG_NOTE, vect_location, "transform phi.\n"); vect_transform_stmt (phi, NULL, NULL, NULL, NULL); } } pattern_stmt = NULL; for (gimple_stmt_iterator si = gsi_start_bb (bb); !gsi_end_p (si) || transform_pattern_stmt;) { bool is_store; if (transform_pattern_stmt) stmt = pattern_stmt; else { stmt = gsi_stmt (si); /* During vectorization remove existing clobber stmts. */ if (gimple_clobber_p (stmt)) { unlink_stmt_vdef (stmt); gsi_remove (&si, true); release_defs (stmt); continue; } } if (dump_enabled_p ()) { dump_printf_loc (MSG_NOTE, vect_location, "------>vectorizing statement: "); dump_gimple_stmt (MSG_NOTE, TDF_SLIM, stmt, 0); } stmt_info = vinfo_for_stmt (stmt); /* vector stmts created in the outer-loop during vectorization of stmts in an inner-loop may not have a stmt_info, and do not need to be vectorized. */ if (!stmt_info) { gsi_next (&si); continue; } if (MAY_HAVE_DEBUG_STMTS && !STMT_VINFO_LIVE_P (stmt_info)) vect_loop_kill_debug_uses (loop, stmt); if (!STMT_VINFO_RELEVANT_P (stmt_info) && !STMT_VINFO_LIVE_P (stmt_info)) { if (STMT_VINFO_IN_PATTERN_P (stmt_info) && (pattern_stmt = STMT_VINFO_RELATED_STMT (stmt_info)) && (STMT_VINFO_RELEVANT_P (vinfo_for_stmt (pattern_stmt)) || STMT_VINFO_LIVE_P (vinfo_for_stmt (pattern_stmt)))) { stmt = pattern_stmt; stmt_info = vinfo_for_stmt (stmt); } else { gsi_next (&si); continue; } } else if (STMT_VINFO_IN_PATTERN_P (stmt_info) && (pattern_stmt = STMT_VINFO_RELATED_STMT (stmt_info)) && (STMT_VINFO_RELEVANT_P (vinfo_for_stmt (pattern_stmt)) || STMT_VINFO_LIVE_P (vinfo_for_stmt (pattern_stmt)))) transform_pattern_stmt = true; /* If pattern statement has def stmts, vectorize them too. */ if (is_pattern_stmt_p (stmt_info)) { if (pattern_def_seq == NULL) { pattern_def_seq = STMT_VINFO_PATTERN_DEF_SEQ (stmt_info); pattern_def_si = gsi_start (pattern_def_seq); } else if (!gsi_end_p (pattern_def_si)) gsi_next (&pattern_def_si); if (pattern_def_seq != NULL) { gimple *pattern_def_stmt = NULL; stmt_vec_info pattern_def_stmt_info = NULL; while (!gsi_end_p (pattern_def_si)) { pattern_def_stmt = gsi_stmt (pattern_def_si); pattern_def_stmt_info = vinfo_for_stmt (pattern_def_stmt); if (STMT_VINFO_RELEVANT_P (pattern_def_stmt_info) || STMT_VINFO_LIVE_P (pattern_def_stmt_info)) break; gsi_next (&pattern_def_si); } if (!gsi_end_p (pattern_def_si)) { if (dump_enabled_p ()) { dump_printf_loc (MSG_NOTE, vect_location, "==> vectorizing pattern def " "stmt: "); dump_gimple_stmt (MSG_NOTE, TDF_SLIM, pattern_def_stmt, 0); } stmt = pattern_def_stmt; stmt_info = pattern_def_stmt_info; } else { pattern_def_si = gsi_none (); transform_pattern_stmt = false; } } else transform_pattern_stmt = false; } if (STMT_VINFO_VECTYPE (stmt_info)) { unsigned int nunits = (unsigned int) TYPE_VECTOR_SUBPARTS (STMT_VINFO_VECTYPE (stmt_info)); if (!STMT_SLP_TYPE (stmt_info) && nunits != (unsigned int) vf && dump_enabled_p ()) /* For SLP VF is set according to unrolling factor, and not to vector size, hence for SLP this print is not valid. */ dump_printf_loc (MSG_NOTE, vect_location, "multiple-types.\n"); } /* SLP. Schedule all the SLP instances when the first SLP stmt is reached. */ if (STMT_SLP_TYPE (stmt_info)) { if (!slp_scheduled) { slp_scheduled = true; if (dump_enabled_p ()) dump_printf_loc (MSG_NOTE, vect_location, "=== scheduling SLP instances ===\n"); vect_schedule_slp (loop_vinfo); } /* Hybrid SLP stmts must be vectorized in addition to SLP. */ if (!vinfo_for_stmt (stmt) || PURE_SLP_STMT (stmt_info)) { if (!transform_pattern_stmt && gsi_end_p (pattern_def_si)) { pattern_def_seq = NULL; gsi_next (&si); } continue; } } /* -------- vectorize statement ------------ */ if (dump_enabled_p ()) dump_printf_loc (MSG_NOTE, vect_location, "transform statement.\n"); grouped_store = false; is_store = vect_transform_stmt (stmt, &si, &grouped_store, NULL, NULL); if (is_store) { if (STMT_VINFO_GROUPED_ACCESS (stmt_info)) { /* Interleaving. If IS_STORE is TRUE, the vectorization of the interleaving chain was completed - free all the stores in the chain. */ gsi_next (&si); vect_remove_stores (GROUP_FIRST_ELEMENT (stmt_info)); } else { /* Free the attached stmt_vec_info and remove the stmt. */ gimple *store = gsi_stmt (si); free_stmt_vec_info (store); unlink_stmt_vdef (store); gsi_remove (&si, true); release_defs (store); } /* Stores can only appear at the end of pattern statements. */ gcc_assert (!transform_pattern_stmt); pattern_def_seq = NULL; } else if (!transform_pattern_stmt && gsi_end_p (pattern_def_si)) { pattern_def_seq = NULL; gsi_next (&si); } } /* stmts in BB */ } /* BBs in loop */ slpeel_make_loop_iterate_ntimes (loop, niters_vector); scale_profile_for_vect_loop (loop, vf); /* The minimum number of iterations performed by the epilogue. This is 1 when peeling for gaps because we always need a final scalar iteration. */ int min_epilogue_iters = LOOP_VINFO_PEELING_FOR_GAPS (loop_vinfo) ? 1 : 0; /* +1 to convert latch counts to loop iteration counts, -min_epilogue_iters to remove iterations that cannot be performed by the vector code. */ int bias = 1 - min_epilogue_iters; /* In these calculations the "- 1" converts loop iteration counts back to latch counts. */ if (loop->any_upper_bound) loop->nb_iterations_upper_bound = wi::udiv_floor (loop->nb_iterations_upper_bound + bias, vf) - 1; if (loop->any_likely_upper_bound) loop->nb_iterations_likely_upper_bound = wi::udiv_floor (loop->nb_iterations_likely_upper_bound + bias, vf) - 1; if (loop->any_estimate) loop->nb_iterations_estimate = wi::udiv_floor (loop->nb_iterations_estimate + bias, vf) - 1; if (dump_enabled_p ()) { if (!LOOP_VINFO_EPILOGUE_P (loop_vinfo)) { dump_printf_loc (MSG_NOTE, vect_location, "LOOP VECTORIZED\n"); if (loop->inner) dump_printf_loc (MSG_NOTE, vect_location, "OUTER LOOP VECTORIZED\n"); dump_printf (MSG_NOTE, "\n"); } else dump_printf_loc (MSG_NOTE, vect_location, "LOOP EPILOGUE VECTORIZED (VS=%d)\n", current_vector_size); } /* Free SLP instances here because otherwise stmt reference counting won't work. */ slp_instance instance; FOR_EACH_VEC_ELT (LOOP_VINFO_SLP_INSTANCES (loop_vinfo), i, instance) vect_free_slp_instance (instance); LOOP_VINFO_SLP_INSTANCES (loop_vinfo).release (); /* Clear-up safelen field since its value is invalid after vectorization since vectorized loop can have loop-carried dependencies. */ loop->safelen = 0; /* Don't vectorize epilogue for epilogue. */ if (LOOP_VINFO_EPILOGUE_P (loop_vinfo)) epilogue = NULL; if (epilogue) { unsigned int vector_sizes = targetm.vectorize.autovectorize_vector_sizes (); vector_sizes &= current_vector_size - 1; if (!PARAM_VALUE (PARAM_VECT_EPILOGUES_NOMASK)) epilogue = NULL; else if (!vector_sizes) epilogue = NULL; else if (LOOP_VINFO_NITERS_KNOWN_P (loop_vinfo) && LOOP_VINFO_PEELING_FOR_ALIGNMENT (loop_vinfo) >= 0) { int smallest_vec_size = 1 << ctz_hwi (vector_sizes); int ratio = current_vector_size / smallest_vec_size; int eiters = LOOP_VINFO_INT_NITERS (loop_vinfo) - LOOP_VINFO_PEELING_FOR_ALIGNMENT (loop_vinfo); eiters = eiters % vf; epilogue->nb_iterations_upper_bound = eiters - 1; if (eiters < vf / ratio) epilogue = NULL; } } if (epilogue) { epilogue->force_vectorize = loop->force_vectorize; epilogue->safelen = loop->safelen; epilogue->dont_vectorize = false; /* We may need to if-convert epilogue to vectorize it. */ if (LOOP_VINFO_SCALAR_LOOP (loop_vinfo)) tree_if_conversion (epilogue); } return epilogue; } /* The code below is trying to perform simple optimization - revert if-conversion for masked stores, i.e. if the mask of a store is zero do not perform it and all stored value producers also if possible. For example, for (i=0; i<n; i++) if (c[i]) { p1[i] += 1; p2[i] = p3[i] +2; } this transformation will produce the following semi-hammock: if (!mask__ifc__42.18_165 == { 0, 0, 0, 0, 0, 0, 0, 0 }) { vect__11.19_170 = MASK_LOAD (vectp_p1.20_168, 0B, mask__ifc__42.18_165); vect__12.22_172 = vect__11.19_170 + vect_cst__171; MASK_STORE (vectp_p1.23_175, 0B, mask__ifc__42.18_165, vect__12.22_172); vect__18.25_182 = MASK_LOAD (vectp_p3.26_180, 0B, mask__ifc__42.18_165); vect__19.28_184 = vect__18.25_182 + vect_cst__183; MASK_STORE (vectp_p2.29_187, 0B, mask__ifc__42.18_165, vect__19.28_184); } */ void optimize_mask_stores (struct loop *loop) { basic_block *bbs = get_loop_body (loop); unsigned nbbs = loop->num_nodes; unsigned i; basic_block bb; struct loop *bb_loop; gimple_stmt_iterator gsi; gimple *stmt; auto_vec<gimple *> worklist; vect_location = find_loop_location (loop); /* Pick up all masked stores in loop if any. */ for (i = 0; i < nbbs; i++) { bb = bbs[i]; for (gsi = gsi_start_bb (bb); !gsi_end_p (gsi); gsi_next (&gsi)) { stmt = gsi_stmt (gsi); if (gimple_call_internal_p (stmt, IFN_MASK_STORE)) worklist.safe_push (stmt); } } free (bbs); if (worklist.is_empty ()) return; /* Loop has masked stores. */ while (!worklist.is_empty ()) { gimple *last, *last_store; edge e, efalse; tree mask; basic_block store_bb, join_bb; gimple_stmt_iterator gsi_to; tree vdef, new_vdef; gphi *phi; tree vectype; tree zero; last = worklist.pop (); mask = gimple_call_arg (last, 2); bb = gimple_bb (last); /* Create then_bb and if-then structure in CFG, then_bb belongs to the same loop as if_bb. It could be different to LOOP when two level loop-nest is vectorized and mask_store belongs to the inner one. */ e = split_block (bb, last); bb_loop = bb->loop_father; gcc_assert (loop == bb_loop || flow_loop_nested_p (loop, bb_loop)); join_bb = e->dest; store_bb = create_empty_bb (bb); add_bb_to_loop (store_bb, bb_loop); e->flags = EDGE_TRUE_VALUE; efalse = make_edge (bb, store_bb, EDGE_FALSE_VALUE); /* Put STORE_BB to likely part. */ efalse->probability = profile_probability::unlikely (); store_bb->frequency = PROB_ALWAYS - EDGE_FREQUENCY (efalse); make_single_succ_edge (store_bb, join_bb, EDGE_FALLTHRU); if (dom_info_available_p (CDI_DOMINATORS)) set_immediate_dominator (CDI_DOMINATORS, store_bb, bb); if (dump_enabled_p ()) dump_printf_loc (MSG_NOTE, vect_location, "Create new block %d to sink mask stores.", store_bb->index); /* Create vector comparison with boolean result. */ vectype = TREE_TYPE (mask); zero = build_zero_cst (vectype); stmt = gimple_build_cond (EQ_EXPR, mask, zero, NULL_TREE, NULL_TREE); gsi = gsi_last_bb (bb); gsi_insert_after (&gsi, stmt, GSI_SAME_STMT); /* Create new PHI node for vdef of the last masked store: .MEM_2 = VDEF <.MEM_1> will be converted to .MEM.3 = VDEF <.MEM_1> and new PHI node will be created in join bb .MEM_2 = PHI <.MEM_1, .MEM_3> */ vdef = gimple_vdef (last); new_vdef = make_ssa_name (gimple_vop (cfun), last); gimple_set_vdef (last, new_vdef); phi = create_phi_node (vdef, join_bb); add_phi_arg (phi, new_vdef, EDGE_SUCC (store_bb, 0), UNKNOWN_LOCATION); /* Put all masked stores with the same mask to STORE_BB if possible. */ while (true) { gimple_stmt_iterator gsi_from; gimple *stmt1 = NULL; /* Move masked store to STORE_BB. */ last_store = last; gsi = gsi_for_stmt (last); gsi_from = gsi; /* Shift GSI to the previous stmt for further traversal. */ gsi_prev (&gsi); gsi_to = gsi_start_bb (store_bb); gsi_move_before (&gsi_from, &gsi_to); /* Setup GSI_TO to the non-empty block start. */ gsi_to = gsi_start_bb (store_bb); if (dump_enabled_p ()) { dump_printf_loc (MSG_NOTE, vect_location, "Move stmt to created bb\n"); dump_gimple_stmt (MSG_NOTE, TDF_SLIM, last, 0); } /* Move all stored value producers if possible. */ while (!gsi_end_p (gsi)) { tree lhs; imm_use_iterator imm_iter; use_operand_p use_p; bool res; /* Skip debug statements. */ if (is_gimple_debug (gsi_stmt (gsi))) { gsi_prev (&gsi); continue; } stmt1 = gsi_stmt (gsi); /* Do not consider statements writing to memory or having volatile operand. */ if (gimple_vdef (stmt1) || gimple_has_volatile_ops (stmt1)) break; gsi_from = gsi; gsi_prev (&gsi); lhs = gimple_get_lhs (stmt1); if (!lhs) break; /* LHS of vectorized stmt must be SSA_NAME. */ if (TREE_CODE (lhs) != SSA_NAME) break; if (!VECTOR_TYPE_P (TREE_TYPE (lhs))) { /* Remove dead scalar statement. */ if (has_zero_uses (lhs)) { gsi_remove (&gsi_from, true); continue; } } /* Check that LHS does not have uses outside of STORE_BB. */ res = true; FOR_EACH_IMM_USE_FAST (use_p, imm_iter, lhs) { gimple *use_stmt; use_stmt = USE_STMT (use_p); if (is_gimple_debug (use_stmt)) continue; if (gimple_bb (use_stmt) != store_bb) { res = false; break; } } if (!res) break; if (gimple_vuse (stmt1) && gimple_vuse (stmt1) != gimple_vuse (last_store)) break; /* Can move STMT1 to STORE_BB. */ if (dump_enabled_p ()) { dump_printf_loc (MSG_NOTE, vect_location, "Move stmt to created bb\n"); dump_gimple_stmt (MSG_NOTE, TDF_SLIM, stmt1, 0); } gsi_move_before (&gsi_from, &gsi_to); /* Shift GSI_TO for further insertion. */ gsi_prev (&gsi_to); } /* Put other masked stores with the same mask to STORE_BB. */ if (worklist.is_empty () || gimple_call_arg (worklist.last (), 2) != mask || worklist.last () != stmt1) break; last = worklist.pop (); } add_phi_arg (phi, gimple_vuse (last_store), e, UNKNOWN_LOCATION); } }