Video stabilization using gyroscope data
A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.
Where the GPU for the Web work happens!
The fastest and most memory efficient lattice Boltzmann CFD software, running on all GPUs and CPUs via OpenCL. Free for non-commercial use.
A modular and open-ended toolkit for WebGPU, with advanced type inference and the ability to write shaders in TypeScript
Lingvo
CUDA Core Compute Libraries
CUDA integration for Python, plus shiny features
Parallel Computing and Scientific Machine Learning (SciML): Methods and Applications (MIT 18.337J/6.338J)
Compiler for multiple programming models (SYCL, C++ standard parallelism, HIP/CUDA) for CPUs and GPUs from all vendors: The independent, community-driven compiler for C++-based heterogeneous programming models. Lets applications adapt themselves to all the hardware in the system - even at runtime!
ILGPU JIT Compiler for high-performance .Net GPU programs
Simulation of spiking neural networks (SNNs) using PyTorch.
Write Java. Run on GPUs. Fast.
An efficient C++20 GPU numerical computing library with Python-like syntax
A fast, ergonomic and portable tensor library in Nim with a deep learning focus for CPU, GPU and embedded devices via OpenMP, Cuda and OpenCL backends
LuxCore source repository
Thin, unified, C++-flavored wrappers for the CUDA APIs
Blender Integration for LuxCore
List of Deep Learning Cloud Providers
Performance-Portable Particle-in-Cell Simulations for the Exascale Era :sparkles:
RDNA-native LLM inference engine in Rust.
Numerical linear algebra software package
Heterogeneous programming in Julia
A framework for high-performance medical image processing, neural network inference and visualization
Run CUDA-targeted Windows applications on AMD GPUs with ZLUDA + ROCm/HIP.
Beautiful shader components, built on vgpu and TypeGPU. 100% Free, Zero config, one command setup.
Keep Python on your laptop. Run PyTorch operations and hold tensors on a remote GPU, including from a Mac with no CUDA installation.
GPUMesh lets people you trust run Docker GPU jobs on your machine β and lets you run jobs on theirs β over authenticated peer-to-peer connections. No SSH, no VPN, no cloud that sees your workload. Pair once, allowlist stays default-deny, jobs stay in containers.
https://shuqi2077.github.io/RUDA/ Rust Full GPU computing stack