142 lines
4.7 KiB
Plaintext
142 lines
4.7 KiB
Plaintext
#include "mat_mul.h"
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#include <cstdio>
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#include <cuda_runtime.h>
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#define CUDA_CALL(f) \
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{ \
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cudaError_t err = (f); \
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if (err != cudaSuccess) { \
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fprintf(stderr, "CUDA error at [%s:%d] %d %s\n", __FILE__, __LINE__, \
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err, cudaGetErrorString(err)); \
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exit(1); \
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} \
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}
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#define TILE_WIDTH 16
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#define TS 32 // The square-root of the 2D tile-size (== work-group dims)
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#define WPT 8 // The amount of work-per-thread, i.e. the thread-coarsening factor
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#define RTS (TS/WPT) // The reduced tile-size in one dimension
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#define MAX_NUM_GPU 4
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int num_devices = 0;
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__global__ void sgemm(float *A, float *B, float *C, int M, int N, int K) {
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__shared__ float sm_M[TILE_WIDTH][TILE_WIDTH];
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__shared__ float sm_N[TILE_WIDTH][TILE_WIDTH];
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int blockx = blockIdx.x, blocky = blockIdx.y,
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threadx = threadIdx.x, thready = threadIdx.y,
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Row = blocky * TILE_WIDTH + thready,
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Col = blockx * TILE_WIDTH + threadx;
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float Pval = 0;
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for (int m = 0; m < (K-1)/TILE_WIDTH+1; ++m) {
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if (Row < M && m*TILE_WIDTH+threadx < K)
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sm_M[thready][threadx] = A[Row*K + m*TILE_WIDTH+threadx];
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else
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sm_M[thready][threadx] = 0;
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if (Col < N && m*TILE_WIDTH+thready < K)
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sm_N[thready][threadx] = B[(m*TILE_WIDTH+thready)*N+Col];
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else
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sm_N[thready][threadx] = 0;
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__syncthreads();
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for (int k = 0; k < TILE_WIDTH; ++k)
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Pval += sm_M[thready][k] * sm_N[k][threadx];
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__syncthreads();
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}
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if (Row < M && Col < N)
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C[Row*N+Col] = Pval;
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}
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// Array of device (GPU) pointers
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static float *a_d[MAX_NUM_GPU];
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static float *b_d[MAX_NUM_GPU];
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static float *c_d[MAX_NUM_GPU];
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static int M, N, K;
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static int Mbegin[MAX_NUM_GPU], Mend[MAX_NUM_GPU];
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void mat_mul(float *_A, float *_B, float *_C, int _M, int _N, int _K) {
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// Launch kernel on every GPU
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for (int i = 0; i < num_devices; i++) {
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int MSize = Mend[i] - Mbegin[i];
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dim3 gridDim((N-1)/TILE_WIDTH+1, (MSize-1)/TILE_WIDTH+1, 1);
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dim3 blockDim(TILE_WIDTH, TILE_WIDTH, 1);
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CUDA_CALL( cudaSetDevice(i) );
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sgemm<<<gridDim, blockDim>>>(a_d[i], b_d[i], c_d[i], MSize , N, K);
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}
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// DO NOT REMOVE; NEEDED FOR TIME MEASURE
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for (int i = 0; i < num_devices; i++) {
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CUDA_CALL( cudaDeviceSynchronize() );
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}
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}
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void mat_mul_init(float *A, float *B, float *C, int _M, int _N, int _K) {
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M = _M, N = _N, K = _K;
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CUDA_CALL( cudaGetDeviceCount(&num_devices) );
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//num_devices = 1;
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printf("Using %d devices\n", num_devices);
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for (int i = 0; i < num_devices; i++) {
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cudaDeviceProp prop;
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CUDA_CALL( cudaGetDeviceProperties(&prop, i) );
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// Try printing more detailed information here
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printf("[GPU %d] %s\n", i, prop.name);
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}
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if (num_devices <= 0) {
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printf("No CUDA device found. Aborting\n");
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exit(1);
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}
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// Setup problem size for each GPU
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for (int i = 0; i < num_devices; i++) {
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Mbegin[i] = (M / num_devices) * i;
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Mend[i] = (M / num_devices) * (i + 1);
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}
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Mend[num_devices - 1] = M;
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// Allocate device memory for each GPU
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for (int i = 0; i < num_devices; i++) {
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CUDA_CALL( cudaSetDevice(i) );
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CUDA_CALL( cudaMalloc(&a_d[i], (Mend[i] - Mbegin[i]) * K * sizeof(float)) );
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CUDA_CALL( cudaMalloc(&b_d[i], K * N * sizeof(float)) );
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CUDA_CALL( cudaMalloc(&c_d[i], (Mend[i] - Mbegin[i]) * N * sizeof(float)) );
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}
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// Upload A and B matrix to every GPU
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for (int i = 0; i < num_devices; i++) {
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CUDA_CALL( cudaMemcpy(a_d[i], A + Mbegin[i] * K,
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(Mend[i] - Mbegin[i]) * K * sizeof(float),
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cudaMemcpyHostToDevice) );
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CUDA_CALL( cudaMemcpy(b_d[i], B, K * N * sizeof(float), cudaMemcpyHostToDevice) );
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}
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// DO NOT REMOVE; NEEDED FOR TIME MEASURE
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for (int i = 0; i < num_devices; i++) {
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CUDA_CALL( cudaDeviceSynchronize() );
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}
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}
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void mat_mul_final(float *A, float *B, float *C, int M, int N, int K) {
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// Do any post-matmul cleanup work here.
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// Download C matrix from GPUs
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for (int i = 0; i < num_devices; i++) {
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CUDA_CALL( cudaMemcpy(C + Mbegin[i] * N, c_d[i],
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(Mend[i] - Mbegin[i]) * N * sizeof(float),
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cudaMemcpyDeviceToHost) );
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}
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// DO NOT REMOVE; NEEDED FOR TIME MEASURE
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for (int i = 0; i < num_devices; i++) {
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CUDA_CALL( cudaDeviceSynchronize() );
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}
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} |