162 lines
5.0 KiB
Plaintext
162 lines
5.0 KiB
Plaintext
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#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 MAX_NUM_GPU 4
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int num_devices = 0;
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#define TS 32
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#define WPT 16
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#define RTS (TS / WPT)
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__global__ void sgemm(float *A, float *B, float *C, int M, int N, int K) {
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int row = threadIdx.x;
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int col = threadIdx.y;
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int global_row = WPT * blockDim.x * blockIdx.x + row; // row index of C
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int global_col = blockDim.y * blockIdx.y + col; // column index of C
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__shared__ float A_SUB[TS][TS];
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__shared__ float B_SUB[TS][TS];
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float iv[WPT];
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for (int w = 0; w < WPT; w++) {
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iv[w] = 0.0f;
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}
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int _K = (K + TS - 1) / TS * TS;
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for (int t = 0; t < _K / TS; t++) {
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for (int w = 0; w < WPT; w++) {
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const int t_row = TS * t + row;
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const int t_col = TS * t + col;
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const int indexA = (global_row + w*RTS) * K + t_col;
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const int indexB = (t_row + w*RTS) * N + global_col;
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A_SUB[row + w*RTS][col] = (global_row + w*RTS < M && t_col < K) ? A[indexA] : 0.0f;
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B_SUB[row + w*RTS][col] = (t_row + w*RTS < K && global_col < N) ? B[indexB] : 0.0f;
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}
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__syncthreads();
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for (int k = 0; k < TS; k++) {
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for (int w = 0; w < WPT; w++) {
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iv[w] += A_SUB[row + w * RTS][k] * B_SUB[k][col];
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}
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}
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__syncthreads();
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}
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if (global_row >= M || global_col >= N) return; // boundary check
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for (int w = 0; w < WPT; w++) {
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C[(global_row + w * RTS)* N + global_col] = iv[w];
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}
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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 MTS = ((TS + WPT - 1) / WPT * WPT) / WPT; // modified TS;
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int MGWS = ((Mend[i] - Mbegin[i] + WPT - 1) / WPT * WPT) / WPT;
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size_t gws[2] = {(size_t)MGWS, (size_t)N}, lws[2] = {(size_t)MTS, (size_t)TS};
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for (int j = 0; j < 2; ++j) {
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gws[j] = (gws[j] + lws[j] - 1) / lws[j] * lws[j];
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}
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dim3 blockDim(lws[0], lws[1], 1);
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dim3 gridDim(gws[0]/lws[0], gws[1]/lws[1], 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], M, 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( cudaSetDevice(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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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( cudaSetDevice(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( cudaSetDevice(i) );
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CUDA_CALL( cudaDeviceSynchronize() );
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}
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}
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