Gpu Computing
Showing 36 of 92 projects
A write-once-run-anywhere GPGPU library for Rust that abstracts WebGPU for CUDA-like compute with portability across desktop, mobile, and browser.
A deep learning framework for Julia with GPU support and automatic differentiation using dynamic computational graphs.
A modern C++20 GPU numerical computing library with Python-like syntax for near-native performance on NVIDIA GPUs.
An R interface to TensorFlow, providing access to the complete TensorFlow API for numerical computation and machine learning.
A discontinued Python neural network framework designed for fast, flexible experimentation with CPU and GPU backends.
A deep learning framework for Julia inspired by Caffe, featuring modular architecture and multiple backends.
A C++17 library providing efficient STL-like data structures (vector, unordered_map, etc.) for GPU programming with CUDA, OpenMP, and HIP backends.
A cross-platform, professional procedural terrain generation and texturing tool for creating realistic 3D landscapes.
A high-performance Clojure library for matrix and linear algebra computations using optimized BLAS/LAPACK routines on CPU and GPU.
A fast, modular Bayesian inference library for JAX, providing composable samplers for CPU and GPU.
A CUDA-accelerated library for rapid 3D data processing in robotics, enabling GPU-powered SLAM, collision avoidance, and path planning.
A header-only Vulkan-based library providing a CUDA Runtime API interface for GPU-accelerated applications.
Thin, unified C++ wrappers for NVIDIA's CUDA APIs (Runtime, Driver, NVRTC, NVTX) that improve safety and ease of use.
A deep learning library for Ruby that provides a native interface to LibTorch, enabling GPU-accelerated neural network development.
Rust bindings for ArrayFire, a high-performance parallel computing library with support for CUDA, OpenCL, and CPU backends.
A fast and flexible deep learning system with NumPy-like NDarray interface and easy multi-GPU support.
An open-source implementation reproducing DeepMind's Atari-playing deep reinforcement learning system from their seminal 2013 paper.
A serverless distributed hash-cracking platform built on AWS, offering pay-as-you-go GPU power with an intuitive UI.
A tensor library for differentiable functional programming in F#, with PyTorch-like APIs and GPU support.
A collection of GLSL procedural noise functions (Perlin, simplex, Worley) for WebGL with no external dependencies.
A fast C++ GPU implementation of Convolutional Neural Networks with multi-GPU support.
An extensible Rust framework for backend-agnostic, high-performance parallel computations on CUDA, OpenCL, and CPU.
A framework for executing native Java and Scala code on the GPU via OpenCL for data-parallel computation.
Real-time 2D fluid dynamics simulation running on GPU via WebGL and Three.js.
A fast Clojure library for tensor operations and deep learning with optimized CPU/GPU support.
A minimalist GPU-only framework for N-dimensional convolutional neural networks focused on speed and hackability.
A tutorial demonstrating how to extend JAX with custom C++ and CUDA operations for high-performance computing.
A C++ template library optimized for GPUs providing high-performance implementations of common algorithms like scan, reduce, transform, and sort.
A collection of open-source machine learning and quantitative analysis models implemented in TensorFlow and PyTorch.
A Clojure library for high-performance Bayesian data analysis and machine learning on the GPU.
An optimized, lightweight CUDA-based 3D reconstruction implementation derived from the original Kinfu algorithm.
A CUDA backend for Torch7 that enables GPU-accelerated tensor operations with a familiar Torch API.
A JAX-powered probabilistic programming library focused on performant sampling methods for Bayesian inference on CPU, GPU, and TPU.
An introductory lesson on fragment shaders in WebGL, covering GLSL basics and simple image effects using Shadertoy.
A Common Lisp library for NVIDIA CUDA programming, providing a kernel description language and memory management.
A pure Go GPU computing framework for graphics and compute operations with dual backends and zero CGO.
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