There are 11 repositories under matrix-multiplication topic.
A collection of algorithms and data structures
High-efficiency floating-point neural network inference operators for mobile, server, and Web
Acceleration package for neural networks on multi-core CPUs
Fast Clojure Matrix Library
Library for specialized dense and sparse matrix operations, and deep learning primitives.
Introduction to PyTorch, covering tensor initialization, operations, indexing, and reshaping.
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
💥 Fast matrix-multiplication as a self-contained Python library – no system dependencies!
Sparse matrix formats for linear algebra supporting scientific and machine learning applications
Meta.Numerics is library for advanced numerical computing on the .NET platform. It offers an object-oriented API for statistical analysis, advanced functions, Fourier transforms, numerical integration and optimization, and matrix algebra.
Python wrapper for Intel Math Kernel Library (MKL) matrix multiplication
🧮 alphatensor matrix breakthrough algorithms + simd + rust.
N-dimensional matrix class for Rust
Cayley hashing as in "Navigating in the Cayley Graph of SL₂(𝔽ₚ)"
Parallel Matrix Multiplication Using OpenMP, Phtreads, and MPI
The only library allowing to create Tensors (matrices extension) with custom types
This is a simple project that shows how to multiply two 3x3 matrixes in Verilog.
BELLA: a Computationally-Efficient and Highly-Accurate Long-Read to Long-Read Aligner and Overlapper
Serial and parallel implementations of matrix multiplication
Decentralized Computing Backend for Artificial Intelligence, Web3, Metaverse, and Gaming Application
Scientific computing with Metal in C++: Matrix multiplication example
ParserNG is a powerful , fast math expression parser that parses and evaluates math expressions, does differential calculus(symbolic) evaluations, numerical integration, equation solving(quadratic, Tartaglia's, numerical solutions of other equations) , matrix operations and statistics amongst other functionality. It is written in pure java and has no native dependencies.
The simplest but fast implementation of matrix multiplication in CUDA.