GPU Programming: When, Why and How?(enccs.github.io)

142 points by transpute 3 years ago | 37 comments

WebGPU and the Price of Compiling WGSL(hugodaniel.com)

38 points by todsacerdoti 1 year ago | 15 comments

Intel D270 GPU AI Accelerator–Up to 480 GB LPDDR5X(intel.com)

293 points by hurricanepootis 13 days ago | 168 comments

Intel product page for its data center GPU D-series AI inference accelerators, including the D270 with up to 480 GB LPDDR5X memory.

GPUHammer: Rowhammer attacks on GPU memories are practical(gpuhammer.com)

260 points by jonbaer 1 year ago | 89 comments

Were RNNs all we needed? A GPU programming perspective(dhruvmsheth.github.io)

88 points by omegablues 1 year ago | 24 comments

Navier-Stokes fluid simulation explained with Godot game engine(myzopotamia.dev)

216 points by myzek 5 months ago | 25 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.

Grid World(alex.miller.garden)

580 points by tobr 3 years ago | 86 comments

A personal web page titled "Grid World" with a hand-drawn pixel-style title over a black grid, apparently an essay about grids.

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.

We were wrong about GPUs(fly.io)

834 points by mxstbr 1 year ago | 529 comments

Fly.io blog post reflecting on how the company's bet on offering GPU machines to developers did not pan out as expected.

Fly.io has GPUs now(fly.io)

552 points by andes314 2 years ago | 196 comments

Fly.io blog post announcing that GPU machines are now available on its cloud platform.

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.