There are 35 repositories under simd topic.
ncnn is a high-performance neural network inference framework optimized for the mobile platform
John the Ripper jumbo - advanced offline password cracker, which supports hundreds of hash and cipher types, and runs on many operating systems, CPUs, GPUs, and even some FPGAs
The Compute Library is a set of computer vision and machine learning functions optimised for both Arm CPUs and GPUs using SIMD technologies.
🚀 efficient approximate nearest neighbor search algorithm collections library written in Rust 🦀 .
Open source c++ skeletal animation library and toolset
Implementations of SIMD instruction sets for systems which don't natively support them.
C++ image processing and machine learning library with using of SIMD: SSE, AVX, AVX-512, AMX for x86/x64, VMX(Altivec) and VSX(Power7) for PowerPC, NEON for ARM.
C++ wrappers for SIMD intrinsics and parallelized, optimized mathematical functions (SSE, AVX, AVX512, NEON, SVE))
Acceleration package for neural networks on multi-core CPUs
Building game development ecosystem for @ziglang!
High-efficiency floating-point neural network inference operators for mobile, server, and Web
Fast, modern C++ DSP framework, FFT, Sample Rate Conversion, FIR/IIR/Biquad Filters (SSE, AVX, AVX-512, ARM NEON)
DirectXMath is an all inline SIMD C++ linear algebra library for use in games and graphics apps
A comprehensive guide to 50 years of evolution of strict C programming, a tribute to Dennis Ritchie's language
A simple and fast linear algebra library for games and graphics
High performance LINQ implementation with minimal heap allocations. Supports enumerables, async enumerables, arrays and Span<T>.
Go library providing algorithms optimized to leverage the characteristics of modern CPUs
The world's fastest log analysis tool
Up to 100x Faster FastAPI. JSON-RPC with io_uring, SIMDJSON, and pure CPython bindings
Linq-like extension functions for Arrays, Span<T>, and List<T> that are faster and allocate less.
Library for specialized dense and sparse matrix operations, and deep learning primitives.