Cross-architecture parallel algorithms for Julia's GPU backends, from a unified KernelAbstractions.jl codebase. Targets Intel oneAPI, AMD ROCm, Apple Metal, Nvidia CUDA.
MIT License
Bot releases are hidden (Show)
Published by anicusan about 1 month ago
First release of AcceleratedKernels.jl, for archiving purposes supporting the "AcceleratedKernels.jl: Cross-Architecture Parallel Algorithms from a Unified, Transpiled Codebase" paper.
CPU/GPU parallel performance portable layer in Julia via functions as arguments
Julia support for the oneAPI programming toolkit.
CUDA programming in Julia.
⅀
🌊 Julia software for fast, friendly, flexible, ocean-flavored fluid dynamics on CPUs and GPUs
Solving differential equations in parallel on GPUs - JuliaCon 2021 workshop
A benchmarking framework for the Julia language
A tool for converting specific Julia GPU code writen in CUDA.jl, into abstract multi-backend code...
Differentiable RayTracing in Julia
AMD GPU (ROCm) programming in Julia
Julia library for visualization and annotation medical images, specialized particularly for rapid...
Metal programming in Julia
Fortran-Julia syntax comparison and Maxwell Solver in 2D using Yee numerical scheme and MPI topology
Performance and data profiles
A generic, simple and fast implementation of Deepmind's AlphaZero algorithm.