> For the complete documentation index, see [llms.txt](https://bryan-guner.gitbook.io/knowledge/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://bryan-guner.gitbook.io/knowledge/14-pure-education/knowledge-master/computer-graphics/cuda.md).

# CUDA

## Links

* [CUB](https://github.com/NVlabs/cub) - Flexible library of cooperative threadblock primitives and other utilities for CUDA kernel programming.
* [Jitify](https://github.com/NVIDIA/jitify) - Single-header C++ library for simplifying the use of CUDA Runtime Compilation.
* [Gunrock](https://github.com/gunrock/gunrock) - CUDA library for graph-processing designed specifically for the GPU.
* [Lantern](https://github.com/feiwang3311/Lantern) - Implementation of a machine learning framework prototype in Scala.
* [Jitify](https://github.com/NVIDIA/jitify) - Single-header C++ library for simplifying the use of CUDA Runtime Compilation (NVRTC).
* [CUDA.jl](https://github.com/JuliaGPU/CUDA.jl) - Programming in Julia. ([CUDA.jl 2.0](https://juliagpu.org/2020-10-02-cuda_2.0/))
* [GPGPU Programming with CUDA](https://github.com/CoffeeBeforeArch/cuda_programming)
* [k-NN algorithm on CUDA](https://github.com/vincentfpgarcia/kNN-CUDA)
* [KBLAS CUDA](https://github.com/ecrc/kblas-gpu) - Subset of BLAS routines optimized for NVIDIA GPUs.
* [CudaSift](https://github.com/Celebrandil/CudaSift) - SIFT features with CUDA.
* [ZLUDA](https://github.com/vosen/ZLUDA) - CUDA on Intel GPUs.
* [CUDA Library Samples](https://github.com/NVIDIA/CUDALibrarySamples)
* [PyCUDA](https://github.com/inducer/pycuda) - Access Nvidia's CUDA parallel computation API from Python.
* [CUTLASS](https://github.com/NVIDIA/cutlass) - CUDA Templates for Linear Algebra Subroutines.
* [Reaction Diffusion modelling with Julia and CUDA (2020)](https://www.moll.dev/projects/reaction-diffusion-julia/)