Parallel Computing
Showing 16 of 52 projects
A general-purpose GPU compute framework built on Vulkan for cross-vendor graphics cards, enabling high-performance data processing and machine learning.
HyperLearn provides 2-2000x faster machine learning algorithms with 50% less memory usage, optimized for all hardware.
NVIDIA's implementation of the C++ Standard Library for CUDA C++ development.
A Node.js module that implements the Web Worker API using native threads for CPU-bound tasks.
A cross-platform, dependency-free C++ and Python DAG framework for building parallel computational graphs.
A distributed query execution engine that extends Apache DataFusion to run SQL queries in parallel across multiple nodes.
A hybrid thread/fiber task scheduler for C++11 that enables efficient execution of blocking tasks.
A Go toolkit for building concurrent programs using composable, channel-based pipelines with automatic error propagation.
A visual workflow-based AI deployment framework for multi-platform and multi-backend inference, supporting large models and edge devices.
A header-only C++ library for CUDA providing accelerated primitives for solving irregularly parallel problems on GPUs.
A high-performance, feature-rich Entity Component System (ECS) library for Rust game development with minimal boilerplate.
A JIT compiler for writing high-performance GPU programs in .NET languages like C#, offering CUDA-level performance with C# convenience.
A C++ GPU computing library providing an STL-like interface for OpenCL-based parallel programming.
A collection of high-performance GICP-based point cloud registration algorithms with multi-threaded and GPU-accelerated implementations.
A computational parallel flow library for Elixir built on top of GenStage for parallel processing of collections.
A portable C++ library providing SIMD vector types for explicit data-parallel programming with zero-overhead abstractions.
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