#include "mat_mul.h" #include #include #define CUDA_CALL(f) \ { \ cudaError_t err = (f); \ if (err != cudaSuccess) { \ fprintf(stderr, "CUDA error at [%s:%d] %d %s\n", __FILE__, __LINE__, \ err, cudaGetErrorString(err)); \ exit(1); \ } \ } #define MAX_NUM_GPU 4 int num_devices = 0; #define TILE_SIZE 16 __global__ void sgemm(float *A, float *B, float *C, int M, int N, int K) { int bx = blockIdx.x, by = blockIdx.y; int tx = threadIdx.x, ty = threadIdx.y; __shared__ float As[TILE_SIZE][TILE_SIZE]; __shared__ float Bs[TILE_SIZE][TILE_SIZE]; int aBegin = K * TILE_SIZE * by; int aEnd = aBegin + K - 1; int aStep = TILE_SIZE; int bBegin = TILE_SIZE * bx; int bStep = TILE_SIZE * N; float Csub = 0; for (int i = aBegin, j = bBegin; i <= aEnd; i += aStep, j += bStep) { As[ty][tx] = A[i + K * ty + tx]; Bs[tx][ty] = B[j + N * tx + ty]; __syncthreads(); for (int k = 0; k < TILE_SIZE; ++k) { Csub += As[ty][k]*Bs[k][tx]; } __syncthreads(); } int cIdx = N * TILE_SIZE * by + TILE_SIZE * bx; C[cIdx + N * ty + tx] = Csub; } // Array of device (GPU) pointers static float *a_d[MAX_NUM_GPU]; static float *b_d[MAX_NUM_GPU]; static float *c_d[MAX_NUM_GPU]; static int M, N, K; static int Mbegin[MAX_NUM_GPU], Mend[MAX_NUM_GPU]; void mat_mul(float *_A, float *_B, float *_C, int _M, int _N, int _K) { // Launch kernel on every GPU // FIXME: Every GPU redundantly calculate all elements of C for (int i = 0; i < num_devices; i++) { dim3 blockDim(TILE_SIZE, TILE_SIZE, 1); dim3 gridDim(N / TILE_SIZE, (Mend[i] - Mbegin[i]) / TILE_SIZE, 1); cudaSetDevice(i); sgemm<<>>(a_d[i], b_d[i], c_d[i], Mend[i] - Mbegin[i], N, K); } // DO NOT REMOVE; NEEDED FOR TIME MEASURE for (int i = 0; i < num_devices; i++) { CUDA_CALL( cudaDeviceSynchronize() ); } } void mat_mul_init(float *A, float *B, float *C, int _M, int _N, int _K) { M = _M, N = _N, K = _K; cudaGetDeviceCount(&num_devices); printf("Using %d devices\n", num_devices); for (int i = 0; i < num_devices; i++) { cudaDeviceProp prop; cudaGetDeviceProperties(&prop, i); // Try printing more detailed information here printf("[GPU %d] %s\n", i, prop.name); } if (num_devices <= 0) { printf("No CUDA device found. Aborting\n"); exit(1); } // Setup problem size for each GPU for (int i = 0; i < num_devices; i++) { Mbegin[i] = (M / num_devices) * i; Mend[i] = (M / num_devices) * (i + 1); } Mend[num_devices - 1] = M; // Allocate device memory for each GPU for (int i = 0; i < num_devices; i++) { cudaSetDevice(i); cudaMalloc(&a_d[i], (Mend[i] - Mbegin[i]) * K * sizeof(float)); cudaMalloc(&b_d[i], K * N * sizeof(float)); cudaMalloc(&c_d[i], (Mend[i] - Mbegin[i]) * N * sizeof(float)); } // Upload A and B matrix to every GPU for (int i = 0; i < num_devices; i++) { cudaMemcpy(a_d[i], A + Mbegin[i] * K, (Mend[i] - Mbegin[i]) * K * sizeof(float), cudaMemcpyHostToDevice); cudaMemcpy(b_d[i], B, K * N * sizeof(float), cudaMemcpyHostToDevice); } // DO NOT REMOVE; NEEDED FOR TIME MEASURE for (int i = 0; i < num_devices; i++) { cudaDeviceSynchronize(); } } void mat_mul_final(float *A, float *B, float *C, int M, int N, int K) { // Do any post-matmul cleanup work here. // Download C matrix from GPU 0 // FIXME: Result from the other GPUs are ignored for (int i = 0; i < num_devices; i++) { cudaMemcpy(C + Mbegin[i] * N, c_d[i], (Mend[i] - Mbegin[i]) * N * sizeof(float), cudaMemcpyDeviceToHost); } // DO NOT REMOVE; NEEDED FOR TIME MEASURE for (int i = 0; i < num_devices; i++) { cudaDeviceSynchronize(); } }