9ca33cf4d5
Sources (all CUDA 10.2 compatible, no CUTLASS/Triton dependency): - leimao/CUDA-GEMM-Optimization: v00-v07, fp16 WMMA variant, double buffered - siboehm/SGEMM_CUDA: kernel 1-12, warp tiling + double buffering - wangzyon/NVIDIA_SGEMM_PRACTICE: kernel 1-7 - edtallison/sgemm-cuda: kernel 1-12 (reimplementation with notes) Key porting issue: ALL kernels hardcode WARPSIZE=32. BI-V100 has warp_size=64. Need to: 1. Replace all 32U / WARPSIZE constants with 64 2. Adjust warp subtile decomposition (WMITER, WNITER, WSUBM, WSUBN) 3. Adjust shared memory bank conflict avoidance (may have different bank count) 4. Test __shfl_down_sync with mask=0xFFFFFFFFFFFFFFFF (64-bit)
42 lines
1.3 KiB
Plaintext
42 lines
1.3 KiB
Plaintext
#pragma once
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#include <stdio.h>
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#include <stdlib.h>
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#include <time.h>
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#include <unistd.h>
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#include <sys/time.h>
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#include <cuda_runtime.h>
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#include <cublas_v2.h>
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/*
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=====================================
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CUDA操作
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=====================================
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*/
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void cudaCheck(cudaError_t error, const char *file, int line); //CUDA错误检查
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void CudaDeviceInfo(); // 打印CUDA信息
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/*
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=====================================
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矩阵操作
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=====================================
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*/
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void randomize_matrix(float *mat, int N); // 随机初始化矩阵
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void copy_matrix(float *src, float *dest, int N); // 复制矩阵
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void print_matrix(const float *A, int M, int N); // 打印矩阵
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bool verify_matrix(float *mat1, float *mat2, int N); // 验证矩阵
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/*
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=====================================
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计时操作
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=====================================
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*/
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float get_current_sec(); // 获取当前时刻
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float cpu_elapsed_time(float &beg, float &end); // 计算时间差
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/*
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=====================================
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kernel操作
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=====================================
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*/
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//调用指定核函数计算矩阵乘法
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void test_kernel(int kernel_num, int m, int n, int k, float alpha, float *A, float *B, float beta, float *C, cublasHandle_t handle); |