Distributed Communication-Optimal Matrix-Matrix Multiplication Algorithm

Matrix multiplication on GPUs for matrices stored on a CPU. Similar to cublasXt, but ported to both NVIDIA and AMD GPUs.

Floating-point matrix multiplication implementation (arbitrary precision)

Matrix multiplication on the NPU inside RK3588

ForMatmul - A Fortran library that overloads the matmul function to enable efficient matrix multiplication with/without coarray.

This project integrates a custom CUDA-based matrix multiplication kernel into a PyTorch deep learning model, leveraging GPU acceleration for matrix operations. The goal is to compare the performance of this custom kernel with PyTorch's built-in matrix multiplication and demonstrate how custom CUDA kernels can optimize compute-intensive operations.

This repository provides a comprehensive guide to optimizing GPU kernels for performance, with a focus on NVIDIA GPUs. It covers key tools and techniques such as CUDA, PyTorch, and Triton, aimed at improving computational efficiency for deep learning and scientific computing tasks.

Generate optimized MatMul cuda kernel automatically using tvm auto schedule.

Bit-exact, cross-hardware deterministic matrix multiplication using Q16.16 fixed-point arithmetic and SHA-256 verification. Provides identical AI inference results across NVIDIA GPUs, CPUs, and OSs. Essential for ZK-ML, Fintech, and AI Safety. Includes a PyTorch drop-in replacement, API and GPT-2 demo.

Raspberry Pi Pico (RP2040) and Adafruit Metro M7 (NXP IMXRT10XX) benchmark

OpenMP Matrix Multiplication Offloading Playground

In this project, ınstruction numbers from a c program are counted with pin and c++.

A hnsw implementation

Optimised matrix multiplication kernels for acceleration on the Tenstorrent Tensix architecture

📰 This repository contains time measurements of various algorithms on the CPU and GPU using PyCuda: matrix multiplication, Pi computation, and bilateral filtering.

The provided code is a Python script that uses the CuPy library to perform optimized GPU operations, specifically matrix multiplication. The script includes a custom CUDA kernel that is optimized for performance and energy consumption. The kernel uses half-precision floating-point numbers (float16) for improved performance and warp utilization.

Implementations of Linear algebra algorithms in CPU and GPU

This repo contains the python scripts for MatMul's all modules testing.