gemm

Tuned OpenCL BLAS

Fast inference engine for Transformer models

BLISlab: A Sandbox for Optimizing GEMM

The HPC toolbox: fused matrix multiplication, convolution, data-parallel strided tensor primitives, OpenMP facilities, SIMD, JIT Assembler, CPU detection, state-of-the-art vectorized BLAS for floats and integers

Stretching GPU performance for GEMMs and tensor contractions.

Single file libraries for C/C++

DBCSR: Distributed Block Compressed Sparse Row matrix library

Code for testing the native float16 matrix multiplication performance on Tesla P100 and V100 GPU based on cublasHgemm

Optimizing SGEMM kernel functions on NVIDIA GPUs to a close-to-cuBLAS performance.

PyTorch bindings for openai-gemm

code for benchmarking GPU performance based on cublasSgemm and cublasHgemm

Stepwise optimizations of DGEMM on CPU, reaching performance faster than Intel MKL eventually, even under multithreading.

Specialized Parallel Linear Algebra, providing distributed GEMM functionality for specific matrix distributions with optional GPU acceleration.

The repository targets the OpenCL gemm function performance optimization. It compares several libraries clBLAS, clBLAST, MIOpenGemm, Intel MKL(CPU) and cuBLAS(CUDA) on different matrix sizes/vendor's hardwares/OS. Out-of-the-box easy as MSVC, MinGW, Linux(CentOS) x86_64 binary provided. 在不同矩阵大小/硬件/操作系统下比较几个BLAS库的sgemm函数性能，提供binary，开盒即用。

Manually optimize the GEMM (GEneral Matrix Multiply) operation. There is a long way to go.

Serial and parallel implementations of matrix multiplication

GEMM

fast 2D convolution implementation benchmark

Low Precision Arithmetic for Convolutional Neural Network Inference

My experiments with convolution

Fast Matrix Multiplication Implementation in C programming language. This matrix multiplication algorithm is similar to what Numpy uses to compute dot products.

gemm baseline code.

Yet another rust BLAS

HPC course for Grad 3/4th 2021

Optimization of sgemm in Kunpeng platform

CUDA kernel functions

DGEMM on KNL, achieve 75% MKL

High Performance Computing - Algorithms and Applications Course in WS18-19 at TUM