mirror of
https://github.com/NVIDIA/cuda-samples.git
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698 lines
26 KiB
Plaintext
698 lines
26 KiB
Plaintext
/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions
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* are met:
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* * Redistributions of source code must retain the above copyright
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* notice, this list of conditions and the following disclaimer.
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* * Redistributions in binary form must reproduce the above copyright
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* notice, this list of conditions and the following disclaimer in the
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* documentation and/or other materials provided with the distribution.
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* * Neither the name of NVIDIA CORPORATION nor the names of its
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* contributors may be used to endorse or promote products derived
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* from this software without specific prior written permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
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* EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
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* PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
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* CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
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* EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
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* PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
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* PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
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* OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*/
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#include <helper_cuda.h>
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#include <helper_timer.h>
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#include "commonDefs.hpp"
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#include "commonKernels.hpp"
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#define VERIFY_GPU_CORRECTNESS 0
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size_t maxSampleSizeInMb = 64;
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int numKernelRuns = 20;
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int verboseResults = 0;
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const char *memAllocTypeStr[MEMALLOC_TYPE_COUNT] = {
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"Managed_Memory_With_Hints",
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"Managed_Memory_With_Hints_FullyAsync",
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"Managed_Memory_NoHints",
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"Zero_Copy",
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"Memcpy_HostMalloc_DeviceCudaMalloc",
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"MemcpyAsync_HostMalloc_DeviceCudaMalloc",
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"Memcpy_HostCudaHostAlloc_DeviceCudaMalloc",
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"MemcpyAsync_HostCudaHostAlloc_DeviceCudaMalloc"};
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const char *memAllocTypeShortStr[MEMALLOC_TYPE_COUNT] = {
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"UMhint", // Managed Memory With Hints
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"UMhntAs", // Managed Memory With_Hints Async
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"UMeasy", // Managed_Memory with No Hints
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"0Copy", // Zero Copy
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"MemCopy", // USE HOST PAGEABLE AND DEVICE_MEMORY
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"CpAsync", // USE HOST PAGEABLE AND DEVICE_MEMORY ASYNC
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"CpHpglk", // USE HOST PAGELOCKED AND DEVICE MEMORY
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"CpPglAs" // USE HOST PAGELOCKED AND DEVICE MEMORY ASYNC
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};
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static float RandFloat(float low, float high) {
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float t = (float)rand() / (float)RAND_MAX;
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return (1.0f - t) * low + t * high;
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}
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void fillMatrixWithRandomValues(float *matrix, unsigned int matrixDim) {
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unsigned int i, j;
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for (i = 0; i < matrixDim; ++i) {
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for (j = 0; j < matrixDim; ++j) {
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matrix[j + i * matrixDim] = RandFloat(0.0f, 10.0f);
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}
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}
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}
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#if VERIFY_GPU_CORRECTNESS
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void verifyMatrixMultiplyCorrectness(float *C, float *A, float *B,
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unsigned int matrixDim) {
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unsigned int i, j, k, numErrors = 0;
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for (i = 0; i < matrixDim; ++i) {
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for (j = 0; j < matrixDim; ++j) {
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float result = 0.0f;
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for (k = 0; k < matrixDim; ++k) {
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result += A[k + i * matrixDim] * B[j + k * matrixDim];
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}
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if (fabs(C[j + i * matrixDim] - result) > 0.001 * matrixDim) {
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printf("At [%u, %u]: Expected %f, Found %f\n", i, j, result,
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C[j + i * matrixDim]);
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++numErrors;
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}
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}
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}
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if (numErrors != 0) {
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printf("%d value mismatches occured\n", numErrors);
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fflush(stdout);
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exit(EXIT_FAILURE); // exit since value mismatches occured
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}
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}
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#endif
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void copyMatrix(float *dstMatrix, float *srcMatrix, unsigned int matrixDim) {
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size_t size = matrixDim * matrixDim * sizeof(float);
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memcpy(dstMatrix, srcMatrix, size);
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}
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void verifyMatrixData(float *expectedData, float *observedData,
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unsigned int matrixDim) {
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unsigned int i, j, numErrors = 0;
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for (i = 0; i < matrixDim; ++i) {
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for (j = 0; j < matrixDim; ++j) {
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if (expectedData[j + i * matrixDim] != observedData[j + i * matrixDim]) {
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++numErrors;
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if (verboseResults) {
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printf("At [%u, %u]: Expected %f, Found %f\n", i, j,
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expectedData[j + i * matrixDim],
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observedData[j + i * matrixDim]);
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}
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}
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}
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}
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if (numErrors != 0) {
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printf("%d value mismatches occured\n", numErrors);
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fflush(stdout);
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exit(EXIT_FAILURE); // exit since value mismatches occured
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}
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}
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#define BLOCK_SIZE 32
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__global__ void matrixMultiplyKernel(float *C, float *A, float *B,
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unsigned int matrixDim) {
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// Block index
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int bx = blockIdx.x;
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int by = blockIdx.y;
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// Thread index
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int tx = threadIdx.x;
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int ty = threadIdx.y;
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unsigned int wA = matrixDim;
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unsigned int wB = matrixDim;
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// Index of the first sub-matrix of A processed by the block
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int aBegin = matrixDim * BLOCK_SIZE * by;
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// Index of the last sub-matrix of A processed by the block
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int aEnd = aBegin + wA - 1;
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// Step size used to iterate through the sub-matrices of A
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int aStep = BLOCK_SIZE;
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// Index of the first sub-matrix of B processed by the block
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int bBegin = BLOCK_SIZE * bx;
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// Step size used to iterate through the sub-matrices of B
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int bStep = BLOCK_SIZE * wB;
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// Csub is used to store the element of the block sub-matrix
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// that is computed by the thread
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float Csub = 0;
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// Loop over all the sub-matrices of A and B
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// required to compute the block sub-matrix
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for (int a = aBegin, b = bBegin; a <= aEnd; a += aStep, b += bStep) {
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// Declaration of the shared memory array As used to
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// store the sub-matrix of A
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__shared__ float As[BLOCK_SIZE][BLOCK_SIZE];
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// Declaration of the shared memory array Bs used to
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// store the sub-matrix of B
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__shared__ float Bs[BLOCK_SIZE][BLOCK_SIZE];
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// Load the matrices from device memory
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// to shared memory; each thread loads
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// one element of each matrix
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As[ty][tx] = A[a + wA * ty + tx];
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Bs[ty][tx] = B[b + wB * ty + tx];
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// Synchronize to make sure the matrices are loaded
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__syncthreads();
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// Multiply the two matrices together;
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// each thread computes one element
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// of the block sub-matrix
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#pragma unroll
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for (int k = 0; k < BLOCK_SIZE; ++k) {
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Csub += As[ty][k] * Bs[k][tx];
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}
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// Synchronize to make sure that the preceding
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// computation is done before loading two new
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// sub-matrices of A and B in the next iteration
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__syncthreads();
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}
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// Write the block sub-matrix to device memory;
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// each thread writes one element
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int c = wB * BLOCK_SIZE * by + BLOCK_SIZE * bx;
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C[c + wB * ty + tx] = Csub;
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}
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void runMatrixMultiplyKernel(unsigned int matrixDim, int allocType,
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unsigned int numLoops, double *gpuLaunchCallsTimes,
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double *gpuTransferToCallsTimes,
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double *gpuTransferFromCallsTimes,
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double *gpuLaunchAndTransferCallsTimes,
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double *gpuLaunchTransferSyncTimes,
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double *cpuAccessTimes, double *overallTimes,
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int device_id) {
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float *dptrA = NULL, *hptrA = NULL;
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float *dptrB = NULL, *hptrB = NULL;
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float *dptrC = NULL, *hptrC = NULL;
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float *randValuesX = NULL, *randValuesY = NULL;
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float *randValuesVerifyXmulY = NULL, *randValuesVerifyYmulX = NULL;
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bool copyRequired = false, hintsRequired = false;
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bool someTransferOpRequired;
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bool isAsync = false;
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cudaStream_t streamToRunOn;
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unsigned int *latch;
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size_t size = matrixDim * matrixDim * sizeof(float);
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dim3 threads(32, 32);
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dim3 grid(matrixDim / threads.x, matrixDim / threads.y);
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StopWatchInterface *gpuLaunchCallsTimer = 0, *gpuTransferCallsTimer = 0;
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StopWatchInterface *gpuSyncTimer = 0, *cpuAccessTimer = 0;
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sdkCreateTimer(&gpuLaunchCallsTimer);
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sdkCreateTimer(&gpuTransferCallsTimer);
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sdkCreateTimer(&gpuSyncTimer);
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sdkCreateTimer(&cpuAccessTimer);
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unsigned int i;
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cudaDeviceProp deviceProp;
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checkCudaErrors(cudaGetDeviceProperties(&deviceProp, device_id));
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checkCudaErrors(cudaStreamCreate(&streamToRunOn));
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randValuesX = (float *)malloc(size);
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if (!randValuesX) {
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exit(EXIT_FAILURE); // exit since memory allocation error
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}
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randValuesY = (float *)malloc(size);
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if (!randValuesY) {
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exit(EXIT_FAILURE); // exit since memory allocation error
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}
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randValuesVerifyXmulY = (float *)malloc(size);
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if (!randValuesVerifyXmulY) {
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exit(EXIT_FAILURE); // exit since memory allocation error
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}
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randValuesVerifyYmulX = (float *)malloc(size);
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if (!randValuesVerifyYmulX) {
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exit(EXIT_FAILURE); // exit since memory allocation error
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}
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checkCudaErrors(cudaMalloc(&dptrA, size));
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checkCudaErrors(cudaMalloc(&dptrB, size));
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checkCudaErrors(cudaMalloc(&dptrC, size));
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fillMatrixWithRandomValues(randValuesX, matrixDim);
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fillMatrixWithRandomValues(randValuesY, matrixDim);
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checkCudaErrors(
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cudaMemcpyAsync(dptrA, randValuesX, size, cudaMemcpyHostToDevice));
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checkCudaErrors(
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cudaMemcpyAsync(dptrB, randValuesY, size, cudaMemcpyHostToDevice));
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matrixMultiplyKernel<<<grid, threads>>>(dptrC, dptrA, dptrB, matrixDim);
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checkCudaErrors(cudaMemcpyAsync(randValuesVerifyXmulY, dptrC, size,
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cudaMemcpyDeviceToHost));
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checkCudaErrors(cudaStreamSynchronize(NULL));
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matrixMultiplyKernel<<<grid, threads>>>(dptrC, dptrB, dptrA, matrixDim);
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checkCudaErrors(cudaMemcpyAsync(randValuesVerifyYmulX, dptrC, size,
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cudaMemcpyDeviceToHost));
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checkCudaErrors(cudaStreamSynchronize(NULL));
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#if VERIFY_GPU_CORRECTNESS
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verifyMatrixMultiplyCorrectness(randValuesVerifyXmulY, randValuesX,
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randValuesY, matrixDim);
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verifyMatrixMultiplyCorrectness(randValuesVerifyYmulX, randValuesY,
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randValuesX, matrixDim);
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#endif
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checkCudaErrors(cudaFree(dptrA));
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checkCudaErrors(cudaFree(dptrB));
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checkCudaErrors(cudaFree(dptrC));
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checkCudaErrors(cudaMallocHost(&latch, sizeof(unsigned int)));
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switch (allocType) {
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case USE_HOST_PAGEABLE_AND_DEVICE_MEMORY:
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case USE_HOST_PAGEABLE_AND_DEVICE_MEMORY_ASYNC:
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hptrA = (float *)malloc(size);
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if (!hptrA) {
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exit(EXIT_FAILURE); // exit since memory allocation error
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}
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hptrB = (float *)malloc(size);
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if (!hptrB) {
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exit(EXIT_FAILURE); // exit since memory allocation error
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}
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hptrC = (float *)malloc(size);
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if (!hptrC) {
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exit(EXIT_FAILURE); // exit since memory allocation error
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}
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checkCudaErrors(cudaMalloc(&dptrA, size));
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checkCudaErrors(cudaMalloc(&dptrB, size));
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checkCudaErrors(cudaMalloc(&dptrC, size));
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copyRequired = true;
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break;
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case USE_HOST_PAGELOCKED_AND_DEVICE_MEMORY:
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case USE_HOST_PAGELOCKED_AND_DEVICE_MEMORY_ASYNC:
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checkCudaErrors(cudaMallocHost(&hptrA, size));
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checkCudaErrors(cudaMallocHost(&hptrB, size));
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checkCudaErrors(cudaMallocHost(&hptrC, size));
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checkCudaErrors(cudaMalloc(&dptrA, size));
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checkCudaErrors(cudaMalloc(&dptrB, size));
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checkCudaErrors(cudaMalloc(&dptrC, size));
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copyRequired = true;
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break;
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case USE_ZERO_COPY:
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checkCudaErrors(cudaMallocHost(&hptrA, size));
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checkCudaErrors(cudaMallocHost(&hptrB, size));
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checkCudaErrors(cudaMallocHost(&hptrC, size));
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checkCudaErrors(cudaHostGetDevicePointer(&dptrA, hptrA, 0));
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checkCudaErrors(cudaHostGetDevicePointer(&dptrB, hptrB, 0));
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checkCudaErrors(cudaHostGetDevicePointer(&dptrC, hptrC, 0));
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break;
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case USE_MANAGED_MEMORY:
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checkCudaErrors(cudaMallocManaged(&dptrA, size));
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checkCudaErrors(cudaMallocManaged(&dptrB, size));
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checkCudaErrors(cudaMallocManaged(&dptrC, size));
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hptrA = dptrA;
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hptrB = dptrB;
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hptrC = dptrC;
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break;
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case USE_MANAGED_MEMORY_WITH_HINTS:
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case USE_MANAGED_MEMORY_WITH_HINTS_ASYNC:
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if (deviceProp.concurrentManagedAccess) {
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checkCudaErrors(cudaMallocManaged(&dptrA, size));
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checkCudaErrors(cudaMallocManaged(&dptrB, size));
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checkCudaErrors(cudaMallocManaged(&dptrC, size));
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checkCudaErrors(cudaMemPrefetchAsync(dptrA, size, cudaCpuDeviceId));
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checkCudaErrors(cudaMemPrefetchAsync(dptrB, size, cudaCpuDeviceId));
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checkCudaErrors(cudaMemPrefetchAsync(dptrC, size, cudaCpuDeviceId));
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} else {
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checkCudaErrors(cudaMallocManaged(&dptrA, size, cudaMemAttachHost));
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checkCudaErrors(cudaMallocManaged(&dptrB, size, cudaMemAttachHost));
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checkCudaErrors(cudaMallocManaged(&dptrC, size, cudaMemAttachHost));
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}
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hptrA = dptrA;
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hptrB = dptrB;
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hptrC = dptrC;
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hintsRequired = true;
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break;
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default:
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exit(EXIT_FAILURE); // exit with error
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}
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if (allocType == USE_HOST_PAGEABLE_AND_DEVICE_MEMORY_ASYNC ||
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allocType == USE_HOST_PAGELOCKED_AND_DEVICE_MEMORY_ASYNC ||
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allocType == USE_MANAGED_MEMORY_WITH_HINTS_ASYNC) {
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isAsync = true;
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}
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someTransferOpRequired = copyRequired || hintsRequired;
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// fill buffers with 0 to avoid any first access page-fault overheads.
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memset(hptrA, 0, size);
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memset(hptrB, 0, size);
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memset(hptrC, 0, size);
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for (i = 0; i < numLoops; i++) {
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cpuAccessTimes[i] = 0.0;
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gpuLaunchCallsTimes[i] = 0.0;
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gpuTransferToCallsTimes[i] = 0.0;
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gpuTransferFromCallsTimes[i] = 0.0;
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sdkStartTimer(&cpuAccessTimer);
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{
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copyMatrix(hptrA, (i & 0x1 == 0) ? randValuesX : randValuesY, matrixDim);
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copyMatrix(hptrB, (i & 0x1 == 0) ? randValuesY : randValuesX, matrixDim);
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}
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sdkStopTimer(&cpuAccessTimer);
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cpuAccessTimes[i] += sdkGetAverageTimerValue(&cpuAccessTimer);
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sdkResetTimer(&cpuAccessTimer);
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if (isAsync && hintsRequired) {
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*latch = 0;
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// Prevent any work on stream from starting until all work is pushed
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spinWhileLessThanOne<<<1, 1, 0, streamToRunOn>>>(latch);
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}
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if (someTransferOpRequired) {
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sdkStartTimer(&gpuTransferCallsTimer);
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if (copyRequired) {
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if (isAsync) {
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checkCudaErrors(cudaMemcpyAsync(
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dptrA, hptrA, size, cudaMemcpyHostToDevice, streamToRunOn));
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checkCudaErrors(cudaMemcpyAsync(
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dptrB, hptrB, size, cudaMemcpyHostToDevice, streamToRunOn));
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} else {
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checkCudaErrors(
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cudaMemcpy(dptrA, hptrA, size, cudaMemcpyHostToDevice));
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checkCudaErrors(
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cudaMemcpy(dptrB, hptrB, size, cudaMemcpyHostToDevice));
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}
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}
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if (hintsRequired) {
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if (deviceProp.concurrentManagedAccess) {
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checkCudaErrors(
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cudaMemPrefetchAsync(dptrA, size, device_id, streamToRunOn));
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checkCudaErrors(
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cudaMemPrefetchAsync(dptrB, size, device_id, streamToRunOn));
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checkCudaErrors(
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cudaMemPrefetchAsync(dptrC, size, device_id, streamToRunOn));
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} else {
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checkCudaErrors(cudaStreamAttachMemAsync(streamToRunOn, dptrA, 0,
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cudaMemAttachGlobal));
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checkCudaErrors(cudaStreamAttachMemAsync(streamToRunOn, dptrB, 0,
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cudaMemAttachGlobal));
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checkCudaErrors(cudaStreamAttachMemAsync(streamToRunOn, dptrC, 0,
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cudaMemAttachGlobal));
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}
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if (!isAsync) {
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checkCudaErrors(cudaStreamSynchronize(streamToRunOn));
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}
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}
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sdkStopTimer(&gpuTransferCallsTimer);
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gpuTransferToCallsTimes[i] +=
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sdkGetAverageTimerValue(&gpuTransferCallsTimer);
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sdkResetTimer(&gpuTransferCallsTimer);
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}
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sdkStartTimer(&gpuLaunchCallsTimer);
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{
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matrixMultiplyKernel<<<grid, threads, 0, streamToRunOn>>>(
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dptrC, dptrA, dptrB, matrixDim);
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if (!isAsync) {
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checkCudaErrors(cudaStreamSynchronize(streamToRunOn));
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}
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}
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sdkStopTimer(&gpuLaunchCallsTimer);
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gpuLaunchCallsTimes[i] += sdkGetAverageTimerValue(&gpuLaunchCallsTimer);
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sdkResetTimer(&gpuLaunchCallsTimer);
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if (someTransferOpRequired) {
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sdkStartTimer(&gpuTransferCallsTimer);
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if (hintsRequired) {
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if (deviceProp.concurrentManagedAccess) {
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checkCudaErrors(cudaMemPrefetchAsync(dptrA, size, cudaCpuDeviceId));
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checkCudaErrors(cudaMemPrefetchAsync(dptrB, size, cudaCpuDeviceId));
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checkCudaErrors(cudaMemPrefetchAsync(dptrC, size, cudaCpuDeviceId));
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} else {
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checkCudaErrors(cudaStreamAttachMemAsync(streamToRunOn, dptrA, 0,
|
|
cudaMemAttachHost));
|
|
checkCudaErrors(cudaStreamAttachMemAsync(streamToRunOn, dptrB, 0,
|
|
cudaMemAttachHost));
|
|
checkCudaErrors(cudaStreamAttachMemAsync(streamToRunOn, dptrC, 0,
|
|
cudaMemAttachHost));
|
|
}
|
|
if (!isAsync) {
|
|
checkCudaErrors(cudaStreamSynchronize(streamToRunOn));
|
|
}
|
|
}
|
|
if (copyRequired) {
|
|
if (isAsync) {
|
|
checkCudaErrors(cudaMemcpyAsync(
|
|
hptrC, dptrC, size, cudaMemcpyDeviceToHost, streamToRunOn));
|
|
} else {
|
|
checkCudaErrors(
|
|
cudaMemcpy(hptrC, dptrC, size, cudaMemcpyDeviceToHost));
|
|
}
|
|
}
|
|
sdkStopTimer(&gpuTransferCallsTimer);
|
|
gpuTransferFromCallsTimes[i] +=
|
|
sdkGetAverageTimerValue(&gpuTransferCallsTimer);
|
|
sdkResetTimer(&gpuTransferCallsTimer);
|
|
}
|
|
gpuLaunchAndTransferCallsTimes[i] = gpuLaunchCallsTimes[i] +
|
|
gpuTransferToCallsTimes[i] +
|
|
gpuTransferFromCallsTimes[i];
|
|
gpuLaunchTransferSyncTimes[i] = gpuLaunchAndTransferCallsTimes[i];
|
|
if (isAsync) {
|
|
sdkStartTimer(&gpuSyncTimer);
|
|
{
|
|
if (hintsRequired) {
|
|
*latch = 1;
|
|
}
|
|
checkCudaErrors(cudaStreamSynchronize(streamToRunOn));
|
|
}
|
|
sdkStopTimer(&gpuSyncTimer);
|
|
gpuLaunchTransferSyncTimes[i] += sdkGetAverageTimerValue(&gpuSyncTimer);
|
|
sdkResetTimer(&gpuSyncTimer);
|
|
}
|
|
|
|
sdkStartTimer(&cpuAccessTimer);
|
|
{
|
|
verifyMatrixData(
|
|
(i & 0x1 == 0) ? randValuesVerifyXmulY : randValuesVerifyYmulX, hptrC,
|
|
matrixDim);
|
|
}
|
|
sdkStopTimer(&cpuAccessTimer);
|
|
cpuAccessTimes[i] += sdkGetAverageTimerValue(&cpuAccessTimer);
|
|
sdkResetTimer(&cpuAccessTimer);
|
|
overallTimes[i] = cpuAccessTimes[i] + gpuLaunchTransferSyncTimes[i];
|
|
}
|
|
|
|
switch (allocType) {
|
|
case USE_HOST_PAGEABLE_AND_DEVICE_MEMORY:
|
|
case USE_HOST_PAGEABLE_AND_DEVICE_MEMORY_ASYNC:
|
|
free(hptrA);
|
|
free(hptrB);
|
|
free(hptrC);
|
|
checkCudaErrors(cudaFree(dptrA));
|
|
checkCudaErrors(cudaFree(dptrB));
|
|
checkCudaErrors(cudaFree(dptrC));
|
|
break;
|
|
|
|
case USE_HOST_PAGELOCKED_AND_DEVICE_MEMORY:
|
|
case USE_HOST_PAGELOCKED_AND_DEVICE_MEMORY_ASYNC:
|
|
checkCudaErrors(cudaFreeHost(hptrA));
|
|
checkCudaErrors(cudaFreeHost(hptrB));
|
|
checkCudaErrors(cudaFreeHost(hptrC));
|
|
checkCudaErrors(cudaFree(dptrA));
|
|
checkCudaErrors(cudaFree(dptrB));
|
|
checkCudaErrors(cudaFree(dptrC));
|
|
break;
|
|
|
|
case USE_ZERO_COPY:
|
|
checkCudaErrors(cudaFreeHost(hptrA));
|
|
checkCudaErrors(cudaFreeHost(hptrB));
|
|
checkCudaErrors(cudaFreeHost(hptrC));
|
|
break;
|
|
|
|
case USE_MANAGED_MEMORY:
|
|
case USE_MANAGED_MEMORY_WITH_HINTS:
|
|
case USE_MANAGED_MEMORY_WITH_HINTS_ASYNC:
|
|
checkCudaErrors(cudaFree(dptrA));
|
|
checkCudaErrors(cudaFree(dptrB));
|
|
checkCudaErrors(cudaFree(dptrC));
|
|
break;
|
|
|
|
default:
|
|
exit(EXIT_FAILURE); // exit due to error
|
|
}
|
|
|
|
checkCudaErrors(cudaStreamDestroy(streamToRunOn));
|
|
checkCudaErrors(cudaFreeHost(latch));
|
|
free(randValuesX);
|
|
free(randValuesY);
|
|
free(randValuesVerifyXmulY);
|
|
free(randValuesVerifyYmulX);
|
|
sdkDeleteTimer(&gpuLaunchCallsTimer);
|
|
sdkDeleteTimer(&gpuTransferCallsTimer);
|
|
sdkDeleteTimer(&gpuSyncTimer);
|
|
sdkDeleteTimer(&cpuAccessTimer);
|
|
}
|
|
|
|
void matrixMultiplyPerfRunner(bool reportAsBandwidth,
|
|
bool print_launch_transfer_results,
|
|
bool print_std_deviation, int device_id) {
|
|
int i;
|
|
unsigned int minMatrixDim = 32;
|
|
unsigned int multiplierDim = 2;
|
|
unsigned int matrixDim;
|
|
unsigned int minSize = minMatrixDim * minMatrixDim * sizeof(float);
|
|
unsigned int maxSize =
|
|
(maxSampleSizeInMb * ONE_MB) /
|
|
4; // 3 buffers are used, but dividing by 4 (power of 2)
|
|
unsigned int multiplier = multiplierDim * multiplierDim;
|
|
unsigned int numSizesToTest;
|
|
|
|
struct testResults *results;
|
|
struct resultsData *gpuLaunchCallsTimes;
|
|
struct resultsData *gpuTransferToCallsTimes;
|
|
struct resultsData *gpuTransferFromCallsTimes;
|
|
struct resultsData *gpuLaunchAndTransferCallsTimes;
|
|
struct resultsData *gpuLaunchTransferSyncTimes;
|
|
struct resultsData *cpuAccessTimes;
|
|
struct resultsData *overallTimes;
|
|
unsigned long *sizesToTest;
|
|
unsigned int j;
|
|
|
|
numSizesToTest = findNumSizesToTest(minSize, maxSize, multiplier);
|
|
|
|
createAndInitTestResults(&results, "matrixMultiplyPerf", numKernelRuns,
|
|
numSizesToTest);
|
|
|
|
sizesToTest = getPtrSizesToTest(results);
|
|
|
|
createResultDataAndAddToTestResults(&gpuLaunchCallsTimes, results,
|
|
"GPU Kernel Launch Call Time", false,
|
|
reportAsBandwidth);
|
|
createResultDataAndAddToTestResults(&gpuTransferToCallsTimes, results,
|
|
"CPU to GPU Transfer Calls Time", false,
|
|
reportAsBandwidth);
|
|
createResultDataAndAddToTestResults(&gpuTransferFromCallsTimes, results,
|
|
"GPU to CPU Transfer Calls Time", false,
|
|
reportAsBandwidth);
|
|
createResultDataAndAddToTestResults(&gpuLaunchAndTransferCallsTimes, results,
|
|
"GPU Launch and Transfer Calls Time",
|
|
false, reportAsBandwidth);
|
|
createResultDataAndAddToTestResults(&gpuLaunchTransferSyncTimes, results,
|
|
"GPU Launch Transfer and Sync Time",
|
|
false, reportAsBandwidth);
|
|
createResultDataAndAddToTestResults(
|
|
&cpuAccessTimes, results, "CPU Access Time", false, reportAsBandwidth);
|
|
createResultDataAndAddToTestResults(&overallTimes, results, "Overall Time",
|
|
false, reportAsBandwidth);
|
|
|
|
printf("Running ");
|
|
for (matrixDim = minMatrixDim, j = 0;
|
|
matrixDim * matrixDim <= maxSize / sizeof(float);
|
|
matrixDim *= multiplierDim, ++j) {
|
|
sizesToTest[j] = matrixDim * matrixDim * sizeof(float);
|
|
for (i = MEMALLOC_TYPE_START; i <= MEMALLOC_TYPE_END; i++) {
|
|
printf(".");
|
|
fflush(stdout);
|
|
runMatrixMultiplyKernel(
|
|
matrixDim, i, numKernelRuns,
|
|
getPtrRunTimesInMs(gpuLaunchCallsTimes, i, j),
|
|
getPtrRunTimesInMs(gpuTransferToCallsTimes, i, j),
|
|
getPtrRunTimesInMs(gpuTransferFromCallsTimes, i, j),
|
|
getPtrRunTimesInMs(gpuLaunchAndTransferCallsTimes, i, j),
|
|
getPtrRunTimesInMs(gpuLaunchTransferSyncTimes, i, j),
|
|
getPtrRunTimesInMs(cpuAccessTimes, i, j),
|
|
getPtrRunTimesInMs(overallTimes, i, j), device_id);
|
|
}
|
|
}
|
|
printf("\n");
|
|
printResults(results, print_launch_transfer_results, print_std_deviation);
|
|
freeTestResultsAndAllResultsData(results);
|
|
}
|
|
|
|
static void usage() {
|
|
printf(
|
|
"./cudaMemoryTypesPerf [-device=<device_id>] [-reportAsBandwidth] "
|
|
"[-print-launch-transfer-results] [-print-std-deviation] [-verbose]\n");
|
|
printf("Options:\n");
|
|
printf(
|
|
"-reportAsBandwidth: By default time taken is printed, this "
|
|
"option allows to instead print bandwidth.\n");
|
|
printf(
|
|
"-print-launch-transfer-results: By default overall results are printed, "
|
|
"this option allows to print data transfers and kernel time as well.\n");
|
|
printf(
|
|
"-print-std-deviation: Prints std deviation of the results.\n");
|
|
printf(
|
|
"-kernel-iterations=<num>: Number of times the kernel tests should "
|
|
"be run[default is 100 iterations].\n");
|
|
printf(
|
|
"-device=<device_id>: Allows to pass GPU Device ID on which "
|
|
"the tests will be run.\n");
|
|
printf("-verbose: Prints highly verbose output.\n");
|
|
}
|
|
|
|
int main(int argc, char **argv) {
|
|
bool reportAsBandwidth = false;
|
|
bool print_launch_transfer_results = false;
|
|
bool print_std_deviation = false;
|
|
|
|
if (checkCmdLineFlag(argc, (const char **)argv, "help") ||
|
|
checkCmdLineFlag(argc, (const char **)argv, "h")) {
|
|
usage();
|
|
printf("&&&& %s WAIVED\n", argv[0]);
|
|
exit(EXIT_WAIVED);
|
|
}
|
|
|
|
if (checkCmdLineFlag(argc, (const char **)argv, "reportAsBandwidth")) {
|
|
reportAsBandwidth = true;
|
|
}
|
|
|
|
if (checkCmdLineFlag(argc, (const char **)argv,
|
|
"print-launch-transfer-results")) {
|
|
print_launch_transfer_results = true;
|
|
}
|
|
|
|
if (checkCmdLineFlag(argc, (const char **)argv, "print-std-deviation")) {
|
|
print_std_deviation = true;
|
|
}
|
|
|
|
if (checkCmdLineFlag(argc, (const char **)argv, "kernel-iterations")) {
|
|
numKernelRuns =
|
|
getCmdLineArgumentInt(argc, (const char **)argv, "kernel-iterations");
|
|
}
|
|
|
|
if (checkCmdLineFlag(argc, (const char **)argv, "verbose")) {
|
|
verboseResults = 1;
|
|
}
|
|
|
|
int device_id = findCudaDevice(argc, (const char **)argv);
|
|
|
|
matrixMultiplyPerfRunner(reportAsBandwidth, print_launch_transfer_results,
|
|
print_std_deviation, device_id);
|
|
|
|
printf(
|
|
"\nNOTE: The CUDA Samples are not meant for performance measurements. "
|
|
"Results may vary when GPU Boost is enabled.\n");
|
|
exit(EXIT_SUCCESS);
|
|
}
|