ILGPU: Write GPU programs with C# and F#(github.com)

135 points by neonsunset 2 years ago | 18 comments

INapGPU: Text-mode graphics card, using only TTL gates(github.com)

76 points by userbinator 1 year ago | 11 comments

Einstein: NewtonOS running on other operating systems(github.com)

75 points by fanf2 10 months ago | 8 comments

BarraCUDA Open-source CUDA compiler targeting AMD GPUs(github.com)

428 points by rurban 8 months ago | 177 comments

GitHub repository for BarraCUDA, an open-source CUDA compiler written in C99 that compiles .cu files to AMD GFX11 GPU machine code.

Show HN: Is It Greg?(github.com)

53 points by antoineleclair 1 month ago | 19 comments

Show HN: I've made a Monte-Carlo raytracer for glTF scenes in WebGPU(github.com)

122 points by lisyarus 1 year ago | 35 comments

Nvidia Warp: A Python framework for high performance GPU simulation and graphics(github.com)

453 points by jarmitage 2 years ago | 125 comments

GitHub repository for NVIDIA Warp, a Python framework for writing high-performance GPU simulation and graphics code.

Nvtop: Linux Task Monitor for Nvidia, AMD and Intel GPUs(github.com)

404 points by alexzeitler 2 years ago | 71 comments

GitHub repository for nvtop, an ncurses-based GPU and accelerator process monitor for AMD, Apple, Huawei, Intel, NVIDIA and Qualcomm.

Map of GitHub(github.com)

651 points by vortex_ape 1 year ago | 100 comments

GitHub repository for anvaka's Map of GitHub, an interactive map visualizing GitHub repositories as countries based on shared stargazers.

What every developer should know about GPU computing(codeconfessions.substack.com)

422 points by Anon84 3 years ago | 149 comments

A Substack primer by Abhinav Upadhyay explaining GPU architecture and computing concepts for software developers familiar mainly with CPUs.

$500 GPU outperforms Claude Sonnet on coding benchmarks(github.com)

447 points by yogthos 7 months ago | 247 comments

GitHub repository for ATLAS (Adaptive Test-time Learning and Autonomous Specialization), a system for running local LLMs on consumer GPUs for coding tasks.

GPUs Go Brrr(hazyresearch.stanford.edu)

1056 points by nmstoker 2 years ago | 243 comments

Stanford Hazy Research blog post on what GPU hardware wants for efficient AI kernels, introducing the ThunderKittens CUDA kernel library.