Awesome GitHub RepositoriesGPU Acceleration Libraries

This project is a high-performance numerical computing library designed for large-scale scientific and machine learning workloads. It functions as an automatic differentiation framework and a just-in-time compilation engine, transforming high-level Python code into optimized machine instructions. By enforcing pure functional programming patterns and immutable array semantics, the library ensures that mathematical functions remain compatible with automated graph transformations and symbolic differentiation. The platform distinguishes itself through its distributed array computing capabilities,

XGBoost is a distributed machine learning library for implementing scalable gradient boosting decision trees used for regression, classification, and ranking. It functions as a predictive model framework and a cross-language toolkit, providing a core implementation with native bindings for Python, R, Java, Scala, and C++. The system is designed as a GPU-accelerated library that utilizes CUDA and NCCL to speed up the training of decision tree ensembles. It operates as a distributed framework capable of scaling training and prediction across multi-node clusters and GPU environments to process m

NVIDIA Docker is a container runtime wrapper that enables the use of host-level graphics processing units within isolated container environments. It functions as a containerized GPU orchestrator, mapping physical hardware resources into virtualized environments to support high-performance computing and machine learning workloads. The project provides a toolkit that facilitates integration between containerized applications and host-level graphics hardware. By utilizing a pre-start hook to intercept container creation, the runtime injects necessary device drivers and libraries into the isolate

This library is a JavaScript framework for general-purpose computing on graphics processing units. It enables the execution of parallel mathematical operations directly within the browser by offloading data-heavy calculations to graphics hardware. The project functions as a web-based math accelerator that converts standard JavaScript functions into shader code for execution on the graphics processor. It provides a unified interface that detects available graphics APIs and manages data transfer between system and graphics memory. To ensure compatibility across diverse environments, the library

Dask 是一个并行计算框架和分布式任务调度器，旨在将 Python 数据科学工作流从单机扩展到大型集群。它作为一个集群资源管理器，通过将任务及其依赖项表示为有向无环图来编排计算逻辑。这种架构允许系统在管理复杂执行要求的同时，自动将工作负载分配到可用硬件上。 该项目通过一个延迟评估引擎脱颖而出，该引擎将数据操作推迟到明确请求时才执行，从而实现全局图优化和高效的资源分配。它结合了内存感知数据溢出功能，以防止在处理超过可用内存的数据集时系统崩溃，并利用任务图融合将操作序列组合成单个执行步骤，从而最大限度地减少调度开销和节点间通信。 该平台为大规模数据分析提供了全面的功能面，包括对分布式机器学习、高性能计算集成和并行数据处理的支持。它提供了用于集群生命周期管理、性能分析和任务执行实时监控的广泛工具。用户可以在各种基础设施上部署这些环境，包括本地硬件、云提供商、容器化系统和高性能计算集群。

Cpp-taskflow is a C++ task-parallelism framework and task graph scheduler designed to manage and execute complex dependency graphs of parallel tasks across CPU and GPU hardware. It provides a parallel algorithm library for high-performance implementations of reductions, sorts, pipelines, and iterations. The framework distinguishes itself through its ability to offload heavy computational workloads from a task graph to graphics processors for acceleration. It also includes a task profiling tool and a performance analysis interface for visualizing task execution flow and dependency structures t

CuPy 是一个 CUDA 数组计算库，实现了与 NumPy 兼容的接口，用于在 NVIDIA GPU 上执行数组操作和数值计算。它作为一个 GPU 加速数值库和基于 CUDA 的 SciPy 实现，将繁重的计算卸载到图形硬件上，以提高科学和工程工作负载的处理速度。 该库支持多框架张量交换，允许使用标准化的内存布局在不同的深度学习框架之间共享数据缓冲区，从而避免内存拷贝。它还支持自定义 GPU 内核集成，允许将数组数据连接到低级 API，以便精确控制硬件执行。 该项目广泛涵盖了高性能数组处理和科学计算工作流。其功能包括加速数组计算和提供大规模数值计算工具。

This is a Glide image transformation library for Android that provides a collection of image processing filters and shapes. It functions as a suite of hardware-accelerated tools for image cropping, artistic filtering, and transformation pipeline management. The project distinguishes itself through an image filter pipeline that allows for sequential transformation chaining, enabling multiple visual effects and color modifications to be applied in a single pass. It utilizes GPU acceleration to implement artistic effects such as pixelation, sketching, and blur. The library covers a broad range

jetson-inference is a set of libraries and tools for executing optimized deep learning models on embedded GPU hardware. Its primary purpose is to enable real-time computer vision and AI inference at the edge with low latency and high throughput. The project distinguishes itself through high-performance streaming analytics and the ability to execute concurrent AI pipelines on auto-grade silicon. It provides specialized support for multi-sensor stream processing, utilizing zero-copy data transport to load camera frames directly into GPU memory. The codebase covers a broad surface of capabiliti

PixiEditor is a multi-functional graphics suite that serves as a pixel art editor, a node-based graphics editor, and a vector graphics tool. It functions as a shader-based painting tool and 2D animation software, providing a comprehensive environment for creating raster images and frame-by-frame motion. The project is distinguished by its use of node-based workflows for building complex image transformations, visual effects, and custom digital brush designs. It utilizes a shader-based brush engine and a node graph to define personalized painting tool behaviors and procedural animations. The

GoCV is a computer vision library and Go language binding for OpenCV. It serves as an image processing toolkit and deep learning inference engine, providing programmatic access to a wide range of algorithms for image manipulation, object detection, and video analysis. The project differentiates itself through high-performance native bindings and hardware acceleration. It utilizes a foreign function interface to map Go calls to C++ functions and includes a hardware-agnostic backend dispatch to route neural network tasks to computation engines such as CUDA and OpenVINO. The library covers a br

Feast is an open-source feature store for machine learning that provides a central platform for defining, storing, and serving features across both training and inference workflows. It operates as a declarative system where feature definitions are written as code in Python files, synchronized to a central registry, and made available for low-latency online retrieval or point-in-time correct historical joins for training datasets. The project abstracts storage behind a pluggable architecture, allowing offline and online backends to be swapped without changing retrieval logic, and coordinates ma

Warp is a Python framework that JIT-compiles Python functions into CUDA kernels for GPU-accelerated parallel computation, with built-in automatic differentiation and multi-framework array interoperability. At its core, it provides a GPU kernel compilation system that enables writing and executing custom GPU kernels directly from Python, while supporting automatic gradient computation through those kernels for integration with machine learning pipelines. The framework also includes tile-based cooperative computing, where thread blocks partition into tiles for shared-memory and tensor-core opera

NVIDIA DALI is a GPU-accelerated data loading and preprocessing library designed for deep learning workflows. It constructs high-performance data pipelines that offload decoding, augmentation, and normalization to the GPU, eliminating CPU bottlenecks in training and inference. The library reads data from multiple storage formats and streams it directly into GPU memory, with support for multi-GPU execution to scale throughput across large-scale workloads. DALI distinguishes itself by enabling data pipelines to be built once and executed across multiple deep learning frameworks without code cha

LiteRT-LM is a high-performance inference framework designed to execute large language models locally on mobile, desktop, and IoT hardware. It serves as an on-device model runtime that utilizes CPU, GPU, and NPU acceleration to provide low-latency processing. The framework is distinguished by its ability to process text, vision, and audio inputs through a single multi-modal inference engine. It features a local HTTP server that emulates OpenAI-compatible API endpoints and a WebGPU-based runtime for executing models directly within a web browser. To ensure output reliability, it includes a con

Flash Linear Attention is a training framework and inference engine for sequence models that use linear attention and state space mechanisms, designed to process long contexts with reduced memory and compute overhead. It provides hardware-optimized token mixing layers and fused CUDA kernels that minimize memory bandwidth and launch overhead across different GPU architectures, and includes a causal inference engine that generates text token-by-token using cached hidden states for efficient autoregressive decoding. The project supports building hybrid sequence models that interleave standard at

TileLang is a Python-embedded domain-specific language compiler that JIT-compiles and autotunes GPU kernels. It uses a tile-based DSL, automatic software pipelining, and parallel autotuning to generate optimized GPU kernels at runtime. It supports tensor core operations with Pythonic syntax, automatic memory management, and thread mapping. The compiler searches over tile sizes, thread counts, and scheduling policies, compiling and benchmarking candidates in parallel to find the fastest kernel. It also caches compiled binaries and tuning results to disk for reuse across sessions. TileLang inc

StableSwarmUI 是一个用于 Stable Diffusion 图像生成的 Web 界面和后端编排器。它作为一个分布式 GPU 图像生成器和模块化 AI 图像流水线，提供了一个集中式控制器来管理图像生成请求。 该系统通过将生成任务拆分到多个图形处理器以提高批处理吞吐量的能力而脱颖而出。它利用后端无关的接口连接到本地服务器、远程服务器和云 API，并包含一个用于定义复杂图像处理操作的基于图的可视化工作流设计器。 该平台包括用于添加自定义功能的动态插件扩展系统，以及用于配置系统级依赖的自动化实用程序。它将模块化生成工具和快速编辑界面与跨分布式硬件路由工作负载的能力相结合。