#gpu

Suite of python packages for multiparticle simulations of particle accelerators.

Fast non-allocating calculations of gradients, Jacobians, and Hessians with sparsity support

Typed, reproducible high-content microscopy workflows across GUI, Python, CellProfiler, Napari/Fiji, GPU, and local MCP agents.

Опис репозиторію не опубліковано.

Serverside, functional (ELM style) front-end and UI for Aardvark, an open-source platform for visual computing, real-time graphics and visualization.

GPU-accelerated transparent compression S3-compatible storage gateway. Drop-in replacement for AWS S3 endpoints; cuts your S3 bill 50-80% with no app changes (Rust, nvCOMP, zstd).

Shared GPU infrastructure for the gogpu ecosystem (DeviceProvider, EventSource, Registry)

.NET library for on-demand local AI model inference — zero bundled models, lazy loading, hardware-aware GPU/CPU selection, 10 task types including embeddings, generation, vision, and audio.

Pure managed C# tensor library for .NET: hand-written AVX2/AVX-512 SIMD kernels, autodiff and optional CUDA, HIP, OpenCL, Metal, Vulkan and WebGPU backends. Measured benchmarks against TorchSharp, ML.NET, TensorFlow.NET, NumSharp and MathNet are in the README.

A JAX backend for Apple Metal Performance Shaders (MPS), enabling GPU-accelerated JAX computations on Apple Silicon.

CUDA driver API in pure Go, no cgo: loads libcuda at runtime, embeds PTX, JIT-compiles through the driver

Production-oriented browser CAD viewer for DWG, DXF, DWF, DWFx and XPS with LibreDWG WASM, WebGL/Canvas rendering, external SHX references and native DWF support.

Search code and feature records by meaning through the command line or Model Context Protocol (MCP). Inference runs on your GPU.

Make GPUs do branchy stuff fast.

WebGPU native for C#

Опис репозиторію не опубліковано.

Aardvark.Rendering is a high-performance, dependency-aware rendering engine. It is part of the open-source Aardvark Platform for visual computing, real-time graphics, and visualization.

Kubernetes-native remote dev environments for AI/ML engineers. Code locally, run on cluster GPUs — live file sync, SSH/tmux into pods, multi-pod PyTorchJob support, and detached job control.

Опис репозиторію не опубліковано.

Run, deploy and monitor CLI agents in secure cloud sandboxes.

JPEG XR decoder in Rust with optional Metal and CUDA backends

Simulate GPU clusters on Kubernetes. Test GPU-aware schedulers, autoscalers and platform tooling with zero GPU hardware.

a GPU powered yet browserless tool to view markdown files in the blink of an eye

ArrayFire: a general purpose GPU library.

Monte Carlo eXtreme for OpenCL (MCXCL)

A Rust implementation of the Khronos OpenCL 3.0 API.

Python-facing GUI framework backend — Rust-native rendering, layout, animation, and accessibility engine with Material Design 3 components (PyO3 bindings).

A tiny hardware-accelerated pixel frame buffer. 🦀

Pure Python bindings for the pure C++11/OpenCL Qrack quantum computer simulator library