jakebolewski/Compat.jl
cross-version compatibility
This repository contains easy-to-read Python/CUDA implementations of fundamental GPU computing primitives: map, reduce, prefix sum (scan), split, radix sort, and histogram. I use these primitives to construct easy-to-read Python/CUDA implementations of the following image processing operations: Gaussian blurring, bilateral filtering, histogram equalization, red-eye removal, and seamless image cloning.
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cross-version compatibility
SHA.jl; a performant, 100% native-julia SHA2-{224,256,384,512} implementation
Saving and loading Julia variables
Public repository.