Matrix Multiplication 本文主要介绍如何优化cuda的矩阵乘法,接近cublas库的性能。 naive version 思路:每个线程计算一个C中的元素 #define OFFSET(row, col, ld) ((row) * (ld) + (col))__global__voidnaiveSgemm(float*__restrict__a,float*__restrict__b,float*__restrict__c,constintM,constintN,cons...
接下来让子矩阵块分别在矩阵 A 的行向以及矩阵 B 的列向上滑动,直到计算完所有k个元素的乘累加。 #include<iostream>#include<cuda_runtime.h>#define BLOCK_SIZE 16__global__voidMuld(float*,float*,int,int,float*);voidMul(float*A,float*B,inthA,intwA,intwB,float*C){intsize;float*Ad;size=hA...
cudaStatus =cudaMalloc((void**)&dev_result, count_m * count_n *sizeof(float));if(cudaStatus != cudaSuccess) {printf("%s, line %d, cudaMalloc failed!\n", __func__, __LINE__);gotoout; } cudaStatus =cudaMemcpy(dev_featureM, featureM, count_m * size *sizeof(float), cudaMemcpy...
NVIDIA A100-SXM4-80GB, CUDA 11.2, cuBLAS 11.4. 3.2. Wave Quantization While tile quantization means the problem size is quantized to the size of each tile, there is a second quantization effect where the total number of tiles is quantized to the number of...
本文深入探讨如何优化CUDA矩阵乘法性能，使其接近CUBLAS库性能。传统方法中，每个线程计算矩阵C中的一个元素，虽然访问内存成本较低，但无法充分利用CUDA并行计算优势。优化方案一：分块计算 每个块负责计算矩阵C中BM * BN个元素，每个线程计算矩阵C中TM * TN个元素。利用共享内存存放可复用的矩阵A和B元素...
CUDA Programming Guide Version 1.1 69 Chapter 6. Example of Matrix Multiplication // Device multiplication function called by Mul() // Compute C = A * B // wA is the width of A // wB is the width of B __global__ void Muld(float* A, float* B, int wA, int wB,...
Although this might be a very trivial question, but being new to CUDA I am unable to resolve it. Can someone have a look at the kernal and help me out. Thanks in advance /* This is a CUDA program that performs matrix multiplication on square matrices of equal dimensions */ ...
编译和运行:确保你的系统已安装CUDA工具包,并使用nvcc编译器来编译这段代码。例如: bash nvcc matrixmultiplication.cu -o matrixmultiplication ./matrixmultiplication 这样,你就完成了CUDA矩阵乘法模板代码的补全,并且可以编译和运行它来验证矩阵乘法的正确性。
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matrix-cuda matrix multiplication in CUDA, this is a toy program for learning CUDA, some functions are reusable for other purposes test results following tests were carried out on a Tesla M2075 card [lzhengchun@clus10 liu]$ ./a.out please type in m n and k 1024 1024 1024 Time elapsed...