mirror of
https://github.com/NVIDIA/cuda-samples.git
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Apply consistent code formatting across the repo. Add clang-format and pre-commit hooks.
This commit is contained in:
Rob Armstrong
@@ -40,314 +40,303 @@
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*/
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// System includes
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#include <stdio.h>
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#include <assert.h>
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#include <stdio.h>
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// CUDA runtime
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#include <cuda_runtime.h>
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#include <cuda_profiler_api.h>
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#include <cuda_runtime.h>
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// Helper functions and utilities to work with CUDA
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#include <helper_functions.h>
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#include <helper_cuda.h>
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#include <helper_functions.h>
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/**
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* Matrix multiplication (CUDA Kernel) on the device: C = A * B
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* wA is A's width and wB is B's width
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*/
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template <int BLOCK_SIZE> __global__ void MatrixMulCUDA(float *C, float *A,
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float *B, int wA,
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int wB) {
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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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template <int BLOCK_SIZE> __global__ void MatrixMulCUDA(float *C, float *A, float *B, int wA, int wB)
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{
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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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// Thread index
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int tx = threadIdx.x;
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int ty = threadIdx.y;
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// Index of the first sub-matrix of A processed by the block
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int aBegin = wA * BLOCK_SIZE * by;
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// Index of the first sub-matrix of A processed by the block
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int aBegin = wA * 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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// 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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// 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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// 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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// 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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// 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;
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a <= aEnd;
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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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// 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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// 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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// 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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// 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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// 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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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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// 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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// 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 ConstantInit(float *data, int size, float val) {
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for (int i = 0; i < size; ++i) {
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data[i] = val;
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}
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void ConstantInit(float *data, int size, float val)
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{
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for (int i = 0; i < size; ++i) {
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data[i] = val;
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}
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}
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/**
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* Run a simple test of matrix multiplication using CUDA
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*/
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int MatrixMultiply(int argc, char **argv,
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int block_size, const dim3 &dimsA,
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const dim3 &dimsB) {
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// Allocate host memory for matrices A and B
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unsigned int size_A = dimsA.x * dimsA.y;
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unsigned int mem_size_A = sizeof(float) * size_A;
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float *h_A;
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checkCudaErrors(cudaMallocHost(&h_A, mem_size_A));
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unsigned int size_B = dimsB.x * dimsB.y;
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unsigned int mem_size_B = sizeof(float) * size_B;
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float *h_B;
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checkCudaErrors(cudaMallocHost(&h_B, mem_size_B));
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cudaStream_t stream;
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int MatrixMultiply(int argc, char **argv, int block_size, const dim3 &dimsA, const dim3 &dimsB)
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{
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// Allocate host memory for matrices A and B
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unsigned int size_A = dimsA.x * dimsA.y;
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unsigned int mem_size_A = sizeof(float) * size_A;
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float *h_A;
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checkCudaErrors(cudaMallocHost(&h_A, mem_size_A));
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unsigned int size_B = dimsB.x * dimsB.y;
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unsigned int mem_size_B = sizeof(float) * size_B;
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float *h_B;
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checkCudaErrors(cudaMallocHost(&h_B, mem_size_B));
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cudaStream_t stream;
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// Initialize host memory
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const float valB = 0.01f;
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ConstantInit(h_A, size_A, 1.0f);
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ConstantInit(h_B, size_B, valB);
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// Initialize host memory
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const float valB = 0.01f;
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ConstantInit(h_A, size_A, 1.0f);
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ConstantInit(h_B, size_B, valB);
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// Allocate device memory
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float *d_A, *d_B, *d_C;
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// Allocate device memory
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float *d_A, *d_B, *d_C;
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// Allocate host matrix C
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dim3 dimsC(dimsB.x, dimsA.y, 1);
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unsigned int mem_size_C = dimsC.x * dimsC.y * sizeof(float);
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float *h_C;
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checkCudaErrors(cudaMallocHost(&h_C, mem_size_C));
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// Allocate host matrix C
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dim3 dimsC(dimsB.x, dimsA.y, 1);
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unsigned int mem_size_C = dimsC.x * dimsC.y * sizeof(float);
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float *h_C;
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checkCudaErrors(cudaMallocHost(&h_C, mem_size_C));
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if (h_C == NULL) {
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fprintf(stderr, "Failed to allocate host matrix C!\n");
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exit(EXIT_FAILURE);
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}
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if (h_C == NULL) {
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fprintf(stderr, "Failed to allocate host matrix C!\n");
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exit(EXIT_FAILURE);
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}
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checkCudaErrors(cudaMalloc(reinterpret_cast<void **>(&d_A), mem_size_A));
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checkCudaErrors(cudaMalloc(reinterpret_cast<void **>(&d_B), mem_size_B));
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checkCudaErrors(cudaMalloc(reinterpret_cast<void **>(&d_C), mem_size_C));
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// Allocate CUDA events that we'll use for timing
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cudaEvent_t start, stop;
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checkCudaErrors(cudaEventCreate(&start));
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checkCudaErrors(cudaEventCreate(&stop));
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checkCudaErrors(cudaMalloc(reinterpret_cast<void **>(&d_A), mem_size_A));
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checkCudaErrors(cudaMalloc(reinterpret_cast<void **>(&d_B), mem_size_B));
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checkCudaErrors(cudaMalloc(reinterpret_cast<void **>(&d_C), mem_size_C));
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// Allocate CUDA events that we'll use for timing
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cudaEvent_t start, stop;
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checkCudaErrors(cudaEventCreate(&start));
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checkCudaErrors(cudaEventCreate(&stop));
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checkCudaErrors(cudaStreamCreateWithFlags(&stream, cudaStreamNonBlocking));
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checkCudaErrors(cudaStreamCreateWithFlags(&stream, cudaStreamNonBlocking));
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// copy host memory to device
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checkCudaErrors(
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cudaMemcpyAsync(d_A, h_A, mem_size_A, cudaMemcpyHostToDevice, stream));
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checkCudaErrors(
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cudaMemcpyAsync(d_B, h_B, mem_size_B, cudaMemcpyHostToDevice, stream));
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// copy host memory to device
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checkCudaErrors(cudaMemcpyAsync(d_A, h_A, mem_size_A, cudaMemcpyHostToDevice, stream));
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checkCudaErrors(cudaMemcpyAsync(d_B, h_B, mem_size_B, cudaMemcpyHostToDevice, stream));
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// Setup execution parameters
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dim3 threads(block_size, block_size);
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dim3 grid(dimsB.x / threads.x, dimsA.y / threads.y);
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// Setup execution parameters
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dim3 threads(block_size, block_size);
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dim3 grid(dimsB.x / threads.x, dimsA.y / threads.y);
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// Create and start timer
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printf("Computing result using CUDA Kernel...\n");
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// Create and start timer
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printf("Computing result using CUDA Kernel...\n");
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// Performs warmup operation using matrixMul CUDA kernel
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if (block_size == 16) {
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MatrixMulCUDA<16>
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<<<grid, threads, 0, stream>>>(d_C, d_A, d_B, dimsA.x, dimsB.x);
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} else {
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MatrixMulCUDA<32>
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<<<grid, threads, 0, stream>>>(d_C, d_A, d_B, dimsA.x, dimsB.x);
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}
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printf("done\n");
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checkCudaErrors(cudaStreamSynchronize(stream));
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// Record the start event
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checkCudaErrors(cudaEventRecord(start, stream));
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// Execute the kernel
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int nIter = 300;
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for (int j = 0; j < nIter; j++) {
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// Performs warmup operation using matrixMul CUDA kernel
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if (block_size == 16) {
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MatrixMulCUDA<16>
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<<<grid, threads, 0, stream>>>(d_C, d_A, d_B, dimsA.x, dimsB.x);
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} else {
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MatrixMulCUDA<32>
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<<<grid, threads, 0, stream>>>(d_C, d_A, d_B, dimsA.x, dimsB.x);
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MatrixMulCUDA<16><<<grid, threads, 0, stream>>>(d_C, d_A, d_B, dimsA.x, dimsB.x);
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}
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}
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// Record the stop event
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checkCudaErrors(cudaEventRecord(stop, stream));
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// Wait for the stop event to complete
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checkCudaErrors(cudaEventSynchronize(stop));
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float msecTotal = 0.0f;
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checkCudaErrors(cudaEventElapsedTime(&msecTotal, start, stop));
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// Compute and print the performance
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float msecPerMatrixMul = msecTotal / nIter;
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double flopsPerMatrixMul = 2.0 * static_cast<double>(dimsA.x) *
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static_cast<double>(dimsA.y) *
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static_cast<double>(dimsB.x);
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double gigaFlops =
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(flopsPerMatrixMul * 1.0e-9f) / (msecPerMatrixMul / 1000.0f);
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printf(
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"Performance= %.2f GFlop/s, Time= %.3f msec, Size= %.0f Ops,"
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" WorkgroupSize= %u threads/block\n",
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gigaFlops, msecPerMatrixMul, flopsPerMatrixMul, threads.x * threads.y);
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// Copy result from device to host
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checkCudaErrors(
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cudaMemcpyAsync(h_C, d_C, mem_size_C, cudaMemcpyDeviceToHost, stream));
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checkCudaErrors(cudaStreamSynchronize(stream));
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printf("Checking computed result for correctness: ");
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bool correct = true;
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// test relative error by the formula
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// |<x, y>_cpu - <x,y>_gpu|/<|x|, |y|> < eps
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double eps = 1.e-6; // machine zero
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for (int i = 0; i < static_cast<int>(dimsC.x * dimsC.y); i++) {
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double abs_err = fabs(h_C[i] - (dimsA.x * valB));
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double dot_length = dimsA.x;
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double abs_val = fabs(h_C[i]);
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double rel_err = abs_err / abs_val / dot_length;
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if (rel_err > eps) {
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printf("Error! Matrix[%05d]=%.8f, ref=%.8f error term is > %E\n",
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i, h_C[i], dimsA.x * valB, eps);
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correct = false;
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else {
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MatrixMulCUDA<32><<<grid, threads, 0, stream>>>(d_C, d_A, d_B, dimsA.x, dimsB.x);
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}
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}
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printf("%s\n", correct ? "Result = PASS" : "Result = FAIL");
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printf("done\n");
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checkCudaErrors(cudaStreamSynchronize(stream));
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// Clean up memory
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checkCudaErrors(cudaFreeHost(h_A));
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checkCudaErrors(cudaFreeHost(h_B));
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checkCudaErrors(cudaFreeHost(h_C));
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checkCudaErrors(cudaFree(d_A));
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checkCudaErrors(cudaFree(d_B));
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checkCudaErrors(cudaFree(d_C));
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checkCudaErrors(cudaEventDestroy(start));
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checkCudaErrors(cudaEventDestroy(stop));
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printf(
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"\nNOTE: The CUDA Samples are not meant for performance "
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"measurements. Results may vary when GPU Boost is enabled.\n");
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// Record the start event
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checkCudaErrors(cudaEventRecord(start, stream));
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if (correct) {
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return EXIT_SUCCESS;
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} else {
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return EXIT_FAILURE;
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}
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// Execute the kernel
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int nIter = 300;
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for (int j = 0; j < nIter; j++) {
|
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if (block_size == 16) {
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MatrixMulCUDA<16><<<grid, threads, 0, stream>>>(d_C, d_A, d_B, dimsA.x, dimsB.x);
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}
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else {
|
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MatrixMulCUDA<32><<<grid, threads, 0, stream>>>(d_C, d_A, d_B, dimsA.x, dimsB.x);
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}
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}
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// Record the stop event
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checkCudaErrors(cudaEventRecord(stop, stream));
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// Wait for the stop event to complete
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checkCudaErrors(cudaEventSynchronize(stop));
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float msecTotal = 0.0f;
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checkCudaErrors(cudaEventElapsedTime(&msecTotal, start, stop));
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// Compute and print the performance
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float msecPerMatrixMul = msecTotal / nIter;
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double flopsPerMatrixMul =
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2.0 * static_cast<double>(dimsA.x) * static_cast<double>(dimsA.y) * static_cast<double>(dimsB.x);
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double gigaFlops = (flopsPerMatrixMul * 1.0e-9f) / (msecPerMatrixMul / 1000.0f);
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printf("Performance= %.2f GFlop/s, Time= %.3f msec, Size= %.0f Ops,"
|
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" WorkgroupSize= %u threads/block\n",
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gigaFlops,
|
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msecPerMatrixMul,
|
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flopsPerMatrixMul,
|
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threads.x * threads.y);
|
||||
|
||||
// Copy result from device to host
|
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checkCudaErrors(cudaMemcpyAsync(h_C, d_C, mem_size_C, cudaMemcpyDeviceToHost, stream));
|
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checkCudaErrors(cudaStreamSynchronize(stream));
|
||||
|
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printf("Checking computed result for correctness: ");
|
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bool correct = true;
|
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|
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// test relative error by the formula
|
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// |<x, y>_cpu - <x,y>_gpu|/<|x|, |y|> < eps
|
||||
double eps = 1.e-6; // machine zero
|
||||
|
||||
for (int i = 0; i < static_cast<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;
|
||||
|
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if (rel_err > eps) {
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printf("Error! Matrix[%05d]=%.8f, ref=%.8f error term is > %E\n", i, h_C[i], dimsA.x * valB, eps);
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correct = false;
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}
|
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}
|
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printf("%s\n", correct ? "Result = PASS" : "Result = FAIL");
|
||||
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||||
// Clean up memory
|
||||
checkCudaErrors(cudaFreeHost(h_A));
|
||||
checkCudaErrors(cudaFreeHost(h_B));
|
||||
checkCudaErrors(cudaFreeHost(h_C));
|
||||
checkCudaErrors(cudaFree(d_A));
|
||||
checkCudaErrors(cudaFree(d_B));
|
||||
checkCudaErrors(cudaFree(d_C));
|
||||
checkCudaErrors(cudaEventDestroy(start));
|
||||
checkCudaErrors(cudaEventDestroy(stop));
|
||||
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");
|
||||
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");
|
||||
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);
|
||||
}
|
||||
exit(EXIT_SUCCESS);
|
||||
}
|
||||
|
||||
// This will pick the best possible CUDA capable device, otherwise
|
||||
// override the device ID based on input provided at the command line
|
||||
int dev = findCudaDevice(argc, (const char **)argv);
|
||||
// This will pick the best possible CUDA capable device, otherwise
|
||||
// override the device ID based on input provided at the command line
|
||||
int dev = findCudaDevice(argc, (const char **)argv);
|
||||
|
||||
int block_size = 32;
|
||||
int block_size = 32;
|
||||
|
||||
dim3 dimsA(5 * 2 * block_size, 5 * 2 * block_size, 1);
|
||||
dim3 dimsB(5 * 4 * block_size, 5 * 2 * block_size, 1);
|
||||
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");
|
||||
}
|
||||
// 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");
|
||||
}
|
||||
// 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");
|
||||
}
|
||||
// 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");
|
||||
}
|
||||
// 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);
|
||||
}
|
||||
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);
|
||||
printf("MatrixA(%d,%d), MatrixB(%d,%d)\n", dimsA.x, dimsA.y, dimsB.x, dimsB.y);
|
||||
|
||||
checkCudaErrors(cudaProfilerStart());
|
||||
int matrix_result = MatrixMultiply(argc, argv, block_size, dimsA, dimsB);
|
||||
checkCudaErrors(cudaProfilerStop());
|
||||
checkCudaErrors(cudaProfilerStart());
|
||||
int matrix_result = MatrixMultiply(argc, argv, block_size, dimsA, dimsB);
|
||||
checkCudaErrors(cudaProfilerStop());
|
||||
|
||||
exit(matrix_result);
|
||||
exit(matrix_result);
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user