Distributed Communication-Optimal Matrix-Matrix Multiplication Algorithm
Floating-point matrix multiplication implementation (arbitrary precision)
Matrix multiplication on the NPU inside RK3588
NVIDIA GPU accelerated C++ tensor library for learning deep learning :)
ForMatmul - A Fortran library that overloads the matmul function to enable efficient matrix multiplication with/without coarray.
CCO is an open-source system for managing, validating, and improving GPU.
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.
AI Basics in Hindi
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
Optimised matrix multiplication kernels for acceleration on the Tenstorrent Tensix architecture
OpenMP Matrix Multiplication Offloading Playground
📰 This repository contains time measurements of various algorithms on the CPU and GPU using PyCuda: matrix multiplication, Pi computation, and bilateral filtering.
Implementations of Linear algebra algorithms in CPU and GPU
working on picorv-based accelerator...
Perform the matrix-matrix operation `C = α*op(A)*op(B) + β*C` where `op(X)` is either `op(X) = X` or `op(X) = X^T`.
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