Gpu Acceleration
Showing 36 of 175 projects
A collection of CI pipelines, Docker images, and optimized examples to simplify JAX development on NVIDIA GPUs.
A Swift package for GPU-accelerated image and video editing using Swift concurrency and Metal.
A GPU-powered terrain editor and renderer for Godot 3, featuring clipmap LOD, PBR materials, and real-time sculpting.
An exascale many-physics flow solver for compressible multi-phase simulations, scaling to 200 trillion grid points on 43K+ GPUs.
A high-performance, GPU-accelerated 3D engine for ActionScript 3 and Adobe AIR, designed for browser-based games with high-resolution graphics.
A JavaScript WebGPU library for creating high-performance 2D/3D graphics and web applications.
A fast neural network framework for iOS and macOS using Swift and Metal for GPU acceleration.
An easy-to-use C# deep learning library with support for multiple backends including TensorFlow, PyTorch, and CUDA/OpenCL.
A Clojure library for high-performance Bayesian data analysis and machine learning on the GPU.
A C++/TensorRT inference module for RangeNet++, enabling fast LiDAR semantic segmentation for robotics applications.
An extremely lightweight Gaussian Process library for Python built on JAX with GPU acceleration and automatic differentiation.
A Vulkan-based GPGPU computing framework that reduces boilerplate for portable, high-performance GPU computing.
A tool and guide for cracking hashed SSH known_hosts files using hashcat to recover IP addresses.
An efficient open-source Python package for 3D photonic nanostructure simulation and design using GPU-accelerated FDTD with automatic differentiation.
A Python library for GPU-accelerated and differentiable quantum systems simulation built with JAX.
OCaml bindings for TensorFlow, enabling machine learning and neural network development in a functional programming environment.
A distributed storage benchmark tool for file systems, object stores, and block devices with GPU support.
A massively parallel library for training self-organizing maps on multicore CPUs, GPUs, and clusters with support for dense and sparse data.
A comprehensive scientific computing and AI/ML library in pure Rust, offering SciPy-compatible APIs with 10-100x performance gains.
A CPU and GPU-accelerated matrix library optimized for high-performance data mining operations.
A cross-platform Stage3D framework for creating hardware-accelerated 2D games and graphical applications in Haxe, TypeScript, JavaScript, or ActionScript 3.
Open-source runtime that executes MATLAB syntax on CPU and GPU automatically, with cross-platform hardware support and no vendor lock-in.
CUDA backend implementation for Torch's neural network package, enabling GPU acceleration for deep learning models.
A JAX-based research framework for differentiable and parallelizable acoustic simulations, running on CPU, GPU, and TPU.
A Clojure library for GPU-accelerated computing using NVIDIA CUDA, enabling high-performance parallel processing.
A CUDA-based implementation of KinectFusion for real-time dense surface reconstruction and tracking using a Kinect camera.
A pure Crystal machine learning library for building and training neural networks with CPU/GPU support and PyTorch compatibility.
Crack passwords of private key entries in Java Key Store (JKS) files using a GPU-accelerated hashcat implementation.
A JAX-powered reimplementation of MiniGrid offering over 1000x speedup for reinforcement learning experiments.
A FlashAttention 2 implementation for JAX with block-wise document mask optimization and context parallelism for efficient long-sequence training.
AS3 framework for GPU-accelerated image processing and effects in Flash/AIR applications.
A Python package built on JAX for solving inverse problems in scientific imaging using optimization and prior models.
A genetic programming platform for Python with TensorFlow for fast CPU and GPU symbolic regression and classification.
An open-source CAD framework for designing, simulating, and deploying deep neural networks on embedded platforms.
.NET Standard bindings for Apache MXNet, providing C# developers with NumPy-compatible APIs for machine learning model development, training, and deployment.
A fast, flexible, and compact deep learning framework for Julia that runs on CPU and CUDA GPU.
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