214 lines
7.0 KiB
Plaintext
214 lines
7.0 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 TS 32
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#define WPT 16
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#define RTS (TS / WPT)
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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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__global__ void sgemm(float *A, float *B, float *C, int M, int N, int K) {
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// Thread identifiers
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const int row = threadIdx.x; // Local row ID (max: TS)
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const int col = threadIdx.y; // Local col ID (max: TS/WPT == RTS)
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const int globalRow = (blockDim.x * WPT) * blockIdx.x + row; // Row ID of C (0..M)
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const int globalCol = blockDim.y * blockIdx.y + col; // Col ID of C (0..N)
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// Local memory to fit a tile of TS*TS elements of A and B
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__shared__ float Asub[TS][TS];
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__shared__ float Bsub[TS][TS];
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// Initialise the accumulation registers
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float tempVal[WPT];
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for (int w=0; w<WPT; w++)
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tempVal[w] = 0.0f;
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// Loop over all tiles
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const int numTiles = (K + TS - 1) / TS ; // Ceiling to Tile Size
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//const int numTiles = (K / TS) + 1 - (K % TS == 0);
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for (int t=0; t<numTiles; t++) {
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// Load one tile of A and B into local memory
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for (int w=0; w<WPT; w++) {
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const int tiledRow = TS*t + row;
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const int tiledCol = TS*t + col;
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const int orgM = globalRow + w*RTS ;
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const int orgK = tiledRow + w*RTS ;
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// Copy Global A to local
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if ( (orgM < M) && (tiledCol < K) )
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Asub[row + w*RTS][col] = A[orgM*K + tiledCol] ;
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else
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Asub[row + w*RTS][col] = 0.0f ;
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// Copy Global B to local
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if ( (orgK < K) && (globalCol < N) )
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Bsub[row + w*RTS][col] = B[orgK*N + globalCol] ;
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else
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Bsub[row + w*RTS][col] = 0.0f ;
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}
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// Synchronise to make sure the tile is loaded
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//barrier(CLK_LOCAL_MEM_FENCE);
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__syncthreads() ;
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// Perform the computation for a single tile
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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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tempVal[w] += Asub[row + w*RTS][k] * Bsub[k][col];
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}
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}
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// Synchronise before loading the next tile
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//barrier(CLK_LOCAL_MEM_FENCE);
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__syncthreads() ;
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}
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// Store the final results in C
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for (int w=0; w<WPT; w++) {
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const int orgM = globalRow + w*RTS ;
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if ( (orgM < M) && (globalCol < N) )
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C[orgM * N + globalCol] = tempVal[w];
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}
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//int i = blockDim.x * blockIdx.x + threadIdx.x;
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//int j = blockDim.y * blockIdx.y + threadIdx.y;
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//if (i >= M || j >= N)
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// return;
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//
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//C[i * N + j] = 0;
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//for (int k = 0; k < K; ++k) {
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// C[i * N + j] += A[i * K + k] * B[k * N + j];
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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 M_str[MAX_NUM_GPU], M_siz[MAX_NUM_GPU];
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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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dim3 blockDim(TS/WPT, TS, 1);
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dim3 gridDim( (M_siz[i] + TS - 1) / TS, (N + TS - 1) / TS, 1);
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//dim3 gridDim( (Mend[i] - Mbegin[i] +TS - 1) / TS, (N + TS - 1) / TS, 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_siz[i], 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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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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M_str [0] = 0 ;
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M_siz [0] = M / num_devices ;
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int mod = M % num_devices ;
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if (mod != 0)
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M_siz[0] = M_siz[0] + mod ;
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// Assign Remained Device
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for (int i = 1 ; i < num_devices ; i++){
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M_str[i] = M_str[i - 1] + M_siz[i - 1];
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if (i == num_devices - 1) { // Last Device
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M_siz[i] = M - M_str[i] ;
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} else {
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M_siz[i] = M / num_devices ;
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}
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}
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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], M_siz[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], M_siz[i] * N * sizeof(float)) );
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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[M_str[i] * K], M_siz[i] * K * sizeof(float), cudaMemcpyHostToDevice) );
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CUDA_CALL( cudaMemcpy(b_d[i], B , K * N * sizeof(float), cudaMemcpyHostToDevice) );
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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[M_str[i] * N], c_d[i], M_siz[i] * N * sizeof(float), cudaMemcpyDeviceToHost) );
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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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}
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