--- a/osc.md	Fri Jun 16 01:06:22 2017 +0900
+++ b/osc.md	Fri Jun 16 05:31:42 2017 +0900
@@ -77,6 +77,11 @@
 cmake_minimum_required(VERSION 3.7)
 - 必要なCUDAのパッケージを設定します。
 find_package(CUDA REQUIRED)
+- 必要なCUDAライブラリのpathを設定します。
+include_directories(/Developer/NVIDIA/CUDA-8.0/samples/common/inc)
+- target名とソースコードを以下のように指定する
+cuda_add_executable(matrixMul matrixMul.cu)
+- cmake .でMakefileを作成し、make matrixMul.cuでビルドします
 
 # Ninja
 

--- a/sample/Makefile	Fri Jun 16 01:06:22 2017 +0900
+++ b/sample/Makefile	Fri Jun 16 05:31:42 2017 +0900
@@ -1,5 +1,5 @@
 # CMAKE generated file: DO NOT EDIT!
-# Generated by "Unix Makefiles" Generator, CMake Version 3.8
+# Generated by "Unix Makefiles" Generator, CMake Version 3.7
 
 # Default target executed when no arguments are given to make.
 default_target: all
@@ -39,19 +39,19 @@
 SHELL = /bin/sh
 
 # The CMake executable.
-CMAKE_COMMAND = /usr/local/Cellar/cmake/3.8.2/bin/cmake
+CMAKE_COMMAND = /usr/local/Cellar/cmake/3.7.2/bin/cmake
 
 # The command to remove a file.
-RM = /usr/local/Cellar/cmake/3.8.2/bin/cmake -E remove -f
+RM = /usr/local/Cellar/cmake/3.7.2/bin/cmake -E remove -f
 
 # Escaping for special characters.
 EQUALS = =
 
 # The top-level source directory on which CMake was run.
-CMAKE_SOURCE_DIR = /Users/e135704/seminar/osc/ikkun-osc/sample
+CMAKE_SOURCE_DIR = /Users/one/hg/Papers/2017/ikkun-osc/sample
 
 # The top-level build directory on which CMake was run.
-CMAKE_BINARY_DIR = /Users/e135704/seminar/osc/ikkun-osc/sample
+CMAKE_BINARY_DIR = /Users/one/hg/Papers/2017/ikkun-osc/sample
 
 #=============================================================================
 # Targets provided globally by CMake.
@@ -59,7 +59,7 @@
 # Special rule for the target rebuild_cache
 rebuild_cache:
 	@$(CMAKE_COMMAND) -E cmake_echo_color --switch=$(COLOR) --cyan "Running CMake to regenerate build system..."
-	/usr/local/Cellar/cmake/3.8.2/bin/cmake -H$(CMAKE_SOURCE_DIR) -B$(CMAKE_BINARY_DIR)
+	/usr/local/Cellar/cmake/3.7.2/bin/cmake -H$(CMAKE_SOURCE_DIR) -B$(CMAKE_BINARY_DIR)
 .PHONY : rebuild_cache
 
 # Special rule for the target rebuild_cache
@@ -70,7 +70,7 @@
 # Special rule for the target edit_cache
 edit_cache:
 	@$(CMAKE_COMMAND) -E cmake_echo_color --switch=$(COLOR) --cyan "Running CMake cache editor..."
-	/usr/local/Cellar/cmake/3.8.2/bin/ccmake -H$(CMAKE_SOURCE_DIR) -B$(CMAKE_BINARY_DIR)
+	/usr/local/Cellar/cmake/3.7.2/bin/ccmake -H$(CMAKE_SOURCE_DIR) -B$(CMAKE_BINARY_DIR)
 .PHONY : edit_cache
 
 # Special rule for the target edit_cache
@@ -80,9 +80,9 @@
 
 # The main all target
 all: cmake_check_build_system
-	$(CMAKE_COMMAND) -E cmake_progress_start /Users/e135704/seminar/osc/ikkun-osc/sample/CMakeFiles /Users/e135704/seminar/osc/ikkun-osc/sample/CMakeFiles/progress.marks
+	$(CMAKE_COMMAND) -E cmake_progress_start /Users/one/hg/Papers/2017/ikkun-osc/sample/CMakeFiles /Users/one/hg/Papers/2017/ikkun-osc/sample/CMakeFiles/progress.marks
 	$(MAKE) -f CMakeFiles/Makefile2 all
-	$(CMAKE_COMMAND) -E cmake_progress_start /Users/e135704/seminar/osc/ikkun-osc/sample/CMakeFiles 0
+	$(CMAKE_COMMAND) -E cmake_progress_start /Users/one/hg/Papers/2017/ikkun-osc/sample/CMakeFiles 0
 .PHONY : all
 
 # The main clean target

--- a/sample/cmake_install.cmake	Fri Jun 16 01:06:22 2017 +0900
+++ b/sample/cmake_install.cmake	Fri Jun 16 05:31:42 2017 +0900
@@ -1,4 +1,4 @@
-# Install script for directory: /Users/e135704/seminar/osc/ikkun-osc/sample
+# Install script for directory: /Users/one/hg/Papers/2017/ikkun-osc/sample
 
 # Set the install prefix
 if(NOT DEFINED CMAKE_INSTALL_PREFIX)
@@ -35,5 +35,5 @@
 
 string(REPLACE ";" "\n" CMAKE_INSTALL_MANIFEST_CONTENT
        "${CMAKE_INSTALL_MANIFEST_FILES}")
-file(WRITE "/Users/e135704/seminar/osc/ikkun-osc/sample/${CMAKE_INSTALL_MANIFEST}"
+file(WRITE "/Users/one/hg/Papers/2017/ikkun-osc/sample/${CMAKE_INSTALL_MANIFEST}"
      "${CMAKE_INSTALL_MANIFEST_CONTENT}")

Binary file sample/hello has changed

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/sample2/matrixMul.cu	Fri Jun 16 05:31:42 2017 +0900
@@ -0,0 +1,452 @@
+/**
+ * Copyright 1993-2015 NVIDIA Corporation.  All rights reserved.
+ *
+ * Please refer to the NVIDIA end user license agreement (EULA) associated
+ * with this source code for terms and conditions that govern your use of
+ * this software. Any use, reproduction, disclosure, or distribution of
+ * this software and related documentation outside the terms of the EULA
+ * is strictly prohibited.
+ *
+ */
+
+/**
+ * Matrix multiplication: C = A * B.
+ * Host code.
+ *
+ * This sample implements matrix multiplication as described in Chapter 3
+ * of the programming guide.
+ * It has been written for clarity of exposition to illustrate various CUDA
+ * programming principles, not with the goal of providing the most
+ * performant generic kernel for matrix multiplication.
+ *
+ * See also:
+ * V. Volkov and J. Demmel, "Benchmarking GPUs to tune dense linear algebra,"
+ * in Proc. 2008 ACM/IEEE Conf. on Supercomputing (SC '08),
+ * Piscataway, NJ: IEEE Press, 2008, pp. Art. 31:1-11.
+ */
+
+// System includes
+#include <stdio.h>
+#include <assert.h>
+
+// CUDA runtime
+#include <cuda_runtime.h>
+
+// Helper functions and utilities to work with CUDA
+#include <helper_functions.h>
+#include <helper_cuda.h>
+
+/**
+ * Matrix multiplication (CUDA Kernel) on the device: C = A * B
+ * wA is A's width and wB is B's width
+ */
+template <int BLOCK_SIZE> __global__ void
+matrixMulCUDA(float *C, float *A, float *B, int wA, int wB)
+{
+    // Block index
+    int bx = blockIdx.x;
+    int by = blockIdx.y;
+
+    // Thread index
+    int tx = threadIdx.x;
+    int ty = threadIdx.y;
+
+    // Index of the first sub-matrix of A processed by the block
+    int aBegin = wA * BLOCK_SIZE * by;
+
+    // Index of the last sub-matrix of A processed by the block
+    int aEnd   = aBegin + wA - 1;
+
+    // Step size used to iterate through the sub-matrices of A
+    int aStep  = BLOCK_SIZE;
+
+    // Index of the first sub-matrix of B processed by the block
+    int bBegin = BLOCK_SIZE * bx;
+
+    // Step size used to iterate through the sub-matrices of B
+    int bStep  = BLOCK_SIZE * wB;
+
+    // Csub is used to store the element of the block sub-matrix
+    // that is computed by the thread
+    float Csub = 0;
+
+    // Loop over all the sub-matrices of A and B
+    // required to compute the block sub-matrix
+    for (int a = aBegin, b = bBegin;
+         a <= aEnd;
+         a += aStep, b += bStep)
+    {
+
+        // Declaration of the shared memory array As used to
+        // store the sub-matrix of A
+        __shared__ float As[BLOCK_SIZE][BLOCK_SIZE];
+
+        // Declaration of the shared memory array Bs used to
+        // store the sub-matrix of B
+        __shared__ float Bs[BLOCK_SIZE][BLOCK_SIZE];
+
+        // Load the matrices from device memory
+        // to shared memory; each thread loads
+        // one element of each matrix
+        As[ty][tx] = A[a + wA * ty + tx];
+        Bs[ty][tx] = B[b + wB * ty + tx];
+
+        // Synchronize to make sure the matrices are loaded
+        __syncthreads();
+
+        // Multiply the two matrices together;
+        // each thread computes one element
+        // of the block sub-matrix
+#pragma unroll
+
+        for (int k = 0; k < BLOCK_SIZE; ++k)
+        {
+            Csub += As[ty][k] * Bs[k][tx];
+        }
+
+        // Synchronize to make sure that the preceding
+        // computation is done before loading two new
+        // sub-matrices of A and B in the next iteration
+        __syncthreads();
+    }
+
+    // Write the block sub-matrix to device memory;
+    // each thread writes one element
+    int c = wB * BLOCK_SIZE * by + BLOCK_SIZE * bx;
+    C[c + wB * ty + tx] = Csub;
+}
+
+void constantInit(float *data, int size, float val)
+{
+    for (int i = 0; i < size; ++i)
+    {
+        data[i] = val;
+    }
+}
+
+/**
+ * Run a simple test of matrix multiplication using CUDA
+ */
+int matrixMultiply(int argc, char **argv, int block_size, dim3 &dimsA, dim3 &dimsB)
+{
+    // Allocate host memory for matrices A and B
+    unsigned int size_A = dimsA.x * dimsA.y;
+    unsigned int mem_size_A = sizeof(float) * size_A;
+    float *h_A = (float *)malloc(mem_size_A);
+    unsigned int size_B = dimsB.x * dimsB.y;
+    unsigned int mem_size_B = sizeof(float) * size_B;
+    float *h_B = (float *)malloc(mem_size_B);
+
+    // Initialize host memory
+    const float valB = 0.01f;
+    constantInit(h_A, size_A, 1.0f);
+    constantInit(h_B, size_B, valB);
+
+    // Allocate device memory
+    float *d_A, *d_B, *d_C;
+
+    // Allocate host matrix C
+    dim3 dimsC(dimsB.x, dimsA.y, 1);
+    unsigned int mem_size_C = dimsC.x * dimsC.y * sizeof(float);
+    float *h_C = (float *) malloc(mem_size_C);
+
+    if (h_C == NULL)
+    {
+        fprintf(stderr, "Failed to allocate host matrix C!\n");
+        exit(EXIT_FAILURE);
+    }
+
+    cudaError_t error;
+
+    error = cudaMalloc((void **) &d_A, mem_size_A);
+
+    if (error != cudaSuccess)
+    {
+        printf("cudaMalloc d_A returned error %s (code %d), line(%d)\n", cudaGetErrorString(error), error, __LINE__);
+        exit(EXIT_FAILURE);
+    }
+
+    error = cudaMalloc((void **) &d_B, mem_size_B);
+
+    if (error != cudaSuccess)
+    {
+        printf("cudaMalloc d_B returned error %s (code %d), line(%d)\n", cudaGetErrorString(error), error, __LINE__);
+        exit(EXIT_FAILURE);
+    }
+
+    error = cudaMalloc((void **) &d_C, mem_size_C);
+
+    if (error != cudaSuccess)
+    {
+        printf("cudaMalloc d_C returned error %s (code %d), line(%d)\n", cudaGetErrorString(error), error, __LINE__);
+        exit(EXIT_FAILURE);
+    }
+
+    // copy host memory to device
+    error = cudaMemcpy(d_A, h_A, mem_size_A, cudaMemcpyHostToDevice);
+
+    if (error != cudaSuccess)
+    {
+        printf("cudaMemcpy (d_A,h_A) returned error %s (code %d), line(%d)\n", cudaGetErrorString(error), error, __LINE__);
+        exit(EXIT_FAILURE);
+    }
+
+    error = cudaMemcpy(d_B, h_B, mem_size_B, cudaMemcpyHostToDevice);
+
+    if (error != cudaSuccess)
+    {
+        printf("cudaMemcpy (d_B,h_B) returned error %s (code %d), line(%d)\n", cudaGetErrorString(error), error, __LINE__);
+        exit(EXIT_FAILURE);
+    }
+
+    // Setup execution parameters
+    dim3 threads(block_size, block_size);
+    dim3 grid(dimsB.x / threads.x, dimsA.y / threads.y);
+
+    // Create and start timer
+    printf("Computing result using CUDA Kernel...\n");
+
+    // Performs warmup operation using matrixMul CUDA kernel
+    if (block_size == 16)
+    {
+        matrixMulCUDA<16><<< grid, threads >>>(d_C, d_A, d_B, dimsA.x, dimsB.x);
+    }
+    else
+    {
+        matrixMulCUDA<32><<< grid, threads >>>(d_C, d_A, d_B, dimsA.x, dimsB.x);
+    }
+
+    printf("done\n");
+
+    cudaDeviceSynchronize();
+
+    // Allocate CUDA events that we'll use for timing
+    cudaEvent_t start;
+    error = cudaEventCreate(&start);
+
+    if (error != cudaSuccess)
+    {
+        fprintf(stderr, "Failed to create start event (error code %s)!\n", cudaGetErrorString(error));
+        exit(EXIT_FAILURE);
+    }
+
+    cudaEvent_t stop;
+    error = cudaEventCreate(&stop);
+
+    if (error != cudaSuccess)
+    {
+        fprintf(stderr, "Failed to create stop event (error code %s)!\n", cudaGetErrorString(error));
+        exit(EXIT_FAILURE);
+    }
+
+    // Record the start event
+    error = cudaEventRecord(start, NULL);
+
+    if (error != cudaSuccess)
+    {
+        fprintf(stderr, "Failed to record start event (error code %s)!\n", cudaGetErrorString(error));
+        exit(EXIT_FAILURE);
+    }
+
+    // Execute the kernel
+    int nIter = 300;
+
+    for (int j = 0; j < nIter; j++)
+    {
+        if (block_size == 16)
+        {
+            matrixMulCUDA<16><<< grid, threads >>>(d_C, d_A, d_B, dimsA.x, dimsB.x);
+        }
+        else
+        {
+            matrixMulCUDA<32><<< grid, threads >>>(d_C, d_A, d_B, dimsA.x, dimsB.x);
+        }
+    }
+
+    // Record the stop event
+    error = cudaEventRecord(stop, NULL);
+
+    if (error != cudaSuccess)
+    {
+        fprintf(stderr, "Failed to record stop event (error code %s)!\n", cudaGetErrorString(error));
+        exit(EXIT_FAILURE);
+    }
+
+    // Wait for the stop event to complete
+    error = cudaEventSynchronize(stop);
+
+    if (error != cudaSuccess)
+    {
+        fprintf(stderr, "Failed to synchronize on the stop event (error code %s)!\n", cudaGetErrorString(error));
+        exit(EXIT_FAILURE);
+    }
+
+    float msecTotal = 0.0f;
+    error = cudaEventElapsedTime(&msecTotal, start, stop);
+
+    if (error != cudaSuccess)
+    {
+        fprintf(stderr, "Failed to get time elapsed between events (error code %s)!\n", cudaGetErrorString(error));
+        exit(EXIT_FAILURE);
+    }
+
+    // Compute and print the performance
+    float msecPerMatrixMul = msecTotal / nIter;
+    double flopsPerMatrixMul = 2.0 * (double)dimsA.x * (double)dimsA.y * (double)dimsB.x;
+    double gigaFlops = (flopsPerMatrixMul * 1.0e-9f) / (msecPerMatrixMul / 1000.0f);
+    printf(
+        "Performance= %.2f GFlop/s, Time= %.3f msec, Size= %.0f Ops, WorkgroupSize= %u threads/block\n",
+        gigaFlops,
+        msecPerMatrixMul,
+        flopsPerMatrixMul,
+        threads.x * threads.y);
+
+    // Copy result from device to host
+    error = cudaMemcpy(h_C, d_C, mem_size_C, cudaMemcpyDeviceToHost);
+
+    if (error != cudaSuccess)
+    {
+        printf("cudaMemcpy (h_C,d_C) returned error %s (code %d), line(%d)\n", cudaGetErrorString(error), error, __LINE__);
+        exit(EXIT_FAILURE);
+    }
+
+    printf("Checking computed result for correctness: ");
+    bool correct = true;
+
+    // test relative error by the formula
+    //     |<x, y>_cpu - <x,y>_gpu|/<|x|, |y|>  < eps
+    double eps = 1.e-6 ; // machine zero
+
+    for (int i = 0; i < (int)(dimsC.x * dimsC.y); i++)
+    {
+        double abs_err = fabs(h_C[i] - (dimsA.x * valB));
+        double dot_length = dimsA.x;
+        double abs_val = fabs(h_C[i]);
+        double rel_err = abs_err/abs_val/dot_length ;
+
+        if (rel_err > eps)
+        {
+            printf("Error! Matrix[%05d]=%.8f, ref=%.8f error term is > %E\n", i, h_C[i], dimsA.x*valB, eps);
+            correct = false;
+        }
+    }
+
+    printf("%s\n", correct ? "Result = PASS" : "Result = FAIL");
+
+    // Clean up memory
+    free(h_A);
+    free(h_B);
+    free(h_C);
+    cudaFree(d_A);
+    cudaFree(d_B);
+    cudaFree(d_C);
+
+    printf("\nNOTE: The CUDA Samples are not meant for performance measurements. Results may vary when GPU Boost is enabled.\n");
+
+    if (correct)
+    {
+        return EXIT_SUCCESS;
+    }
+    else
+    {
+        return EXIT_FAILURE;
+    }
+}
+
+
+/**
+ * Program main
+ */
+int main(int argc, char **argv)
+{
+    printf("[Matrix Multiply Using CUDA] - Starting...\n");
+
+    if (checkCmdLineFlag(argc, (const char **)argv, "help") ||
+        checkCmdLineFlag(argc, (const char **)argv, "?"))
+    {
+        printf("Usage -device=n (n >= 0 for deviceID)\n");
+        printf("      -wA=WidthA -hA=HeightA (Width x Height of Matrix A)\n");
+        printf("      -wB=WidthB -hB=HeightB (Width x Height of Matrix B)\n");
+        printf("  Note: Outer matrix dimensions of A & B matrices must be equal.\n");
+
+        exit(EXIT_SUCCESS);
+    }
+
+    // By default, we use device 0, otherwise we override the device ID based on what is provided at the command line
+    int devID = 0;
+
+    if (checkCmdLineFlag(argc, (const char **)argv, "device"))
+    {
+        devID = getCmdLineArgumentInt(argc, (const char **)argv, "device");
+        cudaSetDevice(devID);
+    }
+
+    cudaError_t error;
+    cudaDeviceProp deviceProp;
+    error = cudaGetDevice(&devID);
+
+    if (error != cudaSuccess)
+    {
+        printf("cudaGetDevice returned error %s (code %d), line(%d)\n", cudaGetErrorString(error), error, __LINE__);
+    }
+
+    error = cudaGetDeviceProperties(&deviceProp, devID);
+
+    if (deviceProp.computeMode == cudaComputeModeProhibited)
+    {
+        fprintf(stderr, "Error: device is running in <Compute Mode Prohibited>, no threads can use ::cudaSetDevice().\n");
+        exit(EXIT_SUCCESS);
+    }
+
+    if (error != cudaSuccess)
+    {
+        printf("cudaGetDeviceProperties returned error %s (code %d), line(%d)\n", cudaGetErrorString(error), error, __LINE__);
+    }
+    else
+    {
+        printf("GPU Device %d: \"%s\" with compute capability %d.%d\n\n", devID, deviceProp.name, deviceProp.major, deviceProp.minor);
+    }
+
+    // Use a larger block size for Fermi and above
+    int block_size = (deviceProp.major < 2) ? 16 : 32;
+
+    dim3 dimsA(5*2*block_size, 5*2*block_size, 1);
+    dim3 dimsB(5*4*block_size, 5*2*block_size, 1);
+
+    // width of Matrix A
+    if (checkCmdLineFlag(argc, (const char **)argv, "wA"))
+    {
+        dimsA.x = getCmdLineArgumentInt(argc, (const char **)argv, "wA");
+    }
+
+    // height of Matrix A
+    if (checkCmdLineFlag(argc, (const char **)argv, "hA"))
+    {
+        dimsA.y = getCmdLineArgumentInt(argc, (const char **)argv, "hA");
+    }
+
+    // width of Matrix B
+    if (checkCmdLineFlag(argc, (const char **)argv, "wB"))
+    {
+        dimsB.x = getCmdLineArgumentInt(argc, (const char **)argv, "wB");
+    }
+
+    // height of Matrix B
+    if (checkCmdLineFlag(argc, (const char **)argv, "hB"))
+    {
+        dimsB.y = getCmdLineArgumentInt(argc, (const char **)argv, "hB");
+    }
+
+    if (dimsA.x != dimsB.y)
+    {
+        printf("Error: outer matrix dimensions must be equal. (%d != %d)\n",
+               dimsA.x, dimsB.y);
+        exit(EXIT_FAILURE);
+    }
+
+    printf("MatrixA(%d,%d), MatrixB(%d,%d)\n", dimsA.x, dimsA.y, dimsB.x, dimsB.y);
+
+    int matrix_result = matrixMultiply(argc, argv, block_size, dimsA, dimsB);
+
+    exit(matrix_result);
+}