Gpu Acceleration
Showing 36 of 153 projects
A Java deep learning framework implementing neural networks with GPU acceleration via OpenCL and Aparapi.
A deep learning JavaScript library built from scratch with PyTorch-like syntax and GPU acceleration via GPU.js.
A high-performance immediate mode 3D plotting library for Dear ImGui, offering GPU-accelerated rendering of lines, surfaces, and meshes.
A GPU-accelerated deep learning library for Python using CUDA via PyCUDA, implementing neural networks with various training methods.
A container runtime that enables GPU acceleration in Docker containers (deprecated in favor of NVIDIA Container Toolkit).
An audio processing toolbox using PyTorch 1D convolutional neural networks for on-the-fly spectrogram generation with trainable kernels.
An open-source machine learning system for the end-to-end data science lifecycle from data preparation to model serving.
A WebGL-powered library for visualizing wind patterns using particle systems, capable of rendering up to 1 million particles at 60fps.
High-performance, end-to-end reinforcement learning implementations fully written in JAX for massive parallelization on GPUs.
Hardware-accelerated, batchable, and differentiable optimization algorithms implemented in JAX for machine learning research.
A Google Colab notebook setup for high-performance hash cracking and penetration testing tools.
An open-source GPU-accelerated password cracking tool for BitLocker-encrypted storage devices using dictionary attacks.
GPU-accelerated audio preprocessing layers for Keras/TensorFlow, enabling real-time audio feature extraction within neural networks.
A CPU and GPU-accelerated machine learning library optimized for high-performance computing.
A header-only Vulkan-based library providing a CUDA Runtime API interface for GPU-accelerated applications.
GPU-accelerated Python implementation of six fundamental deep learning algorithms using CUDA libraries.
A header-only C++ library for solving large sparse linear systems using algebraic multigrid (AMG) method with support for GPU acceleration.
A Ruby deep learning library powered by LibTorch, providing a PyTorch-like API for Ruby developers.
A high-performance 2D vector graphics library using Vulkan as its rendering backend, with a Cairo-like API.
A pure-Java/C# machine learning framework for neural networks, genetic programming, and classic ML algorithms with simple adaptable source code.
A feature-rich terminal emulator built on the Enlightenment Foundation Libraries, supporting inline media, GPU acceleration, and advanced theming.
A fast GPU-accelerated library for training Gradient Boosting Decision Trees (GBDT) and Random Forests.
TensorFlow port for AMD GPUs via ROCm, enabling machine learning on Radeon hardware.
A CUDA-accelerated library collection for point cloud processing, providing GPU-optimized alternatives to PCL functions.
A flexible, efficient, and extensible JIT-compiled framework for computational neuroscience and brain-inspired computation.
A high-performance Swift library for GPU-accelerated real-time image and video processing on Apple platforms using Metal.
A high-performance C++/DPC++ library for accelerated machine learning on CPUs, GPUs, and distributed systems.
A Python toolkit for quantum machine learning that bridges AI and quantum computing with quantum neural networks.
A collection of GPU-accelerated parallel game simulators for reinforcement learning, built with JAX.
A standalone reimplementation of TensorFlow for Ruby, supporting pure Ruby and OpenCL backends for machine learning.
A Jupyter widgets library for interactive 3-D mesh visualization and analysis with GPU-accelerated effects.
A differentiable, massively parallel Lattice Boltzmann library in Python for physics-based machine learning and fluid dynamics simulations.
A GPU-accelerated C++ library for visual-inertial odometry frontend tasks, optimized for high-speed robotics.
OCaml bindings for PyTorch, providing NumPy-like tensor computations with GPU acceleration and automatic differentiation.
An image processing library built on JAX, designed to be optimized and parallelized with JAX transformations.
A Pascal-based deep learning neural network API optimized for AVX/AVX2/AVX512 and OpenCL, supporting AMD, Intel, and NVIDIA hardware.
Related Tags
Found a gem we're missing?
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.