#Parallel-processing
Showing 18 of 18 repositories tagged #parallel-processing, ranked by stars
The missing standard library for multithreading in JavaScript (Works in the browser, Node.js, Deno, Bun)
Samurai-inspired multi-agent system for Claude Code. Orchestrate parallel AI tasks via tmux with shogun → karo → ashigaru hierarchy.
Python supercharged for the fastai library
A C++ platform to perform parallel computations of optimisation tasks (global and local) via the asynchronous generalized island model.
A Python platform to perform parallel computations of optimisation tasks (global and local) via the asynchronous generalized island model.
Video and Image Processing and Computer Vision Library similar to OpenCV in pure JavaScript (Browser and Nodejs)
Open-source control plane for AI coding agents — run, monitor & orchestrate dozens of parallel Claude Code, Codex & Gemini sessions from one web dashboard or your phone. Self-healing, single-file, tmux-native.
The Accelerator is a tool for fast and reproducible processing of large amounts of data.
Various C# implementations of game AI
Repository for Parallel Programming course given by Assoc. Prof. Dr. Bora Canbula at Computer Engineering Department of Manisa Celal Bayar University.
The REST API and execution engine for the Didact Platform.
The open source grid computing solution
A cross-platform GUI file cataloging program with extensive customization options to suit user preferences. Highly optimized for multi-core parallel search speed, data integrity, and repository portability.
A fast and light-weight multithreaded file processing library for Java.
Automate GitHub PR creation at scale with intelligent token rotation, proxy support, and real-time notifications. Create thousands of PRs effortlessly using sequential or high-speed parallel modes (62 PRs/min). Features: multi-token management, auto-merge, state persistence, Discord/Slack webhooks, async operations, and Windows compatibility.
Hypergol is a Data Science/Machine Learning productivity toolkit to accelerate any projects into production with autogenerated code, standardised structure for data and ML and parallel processing out-of-the-box.
Scheduling Big Data Workloads and Data Pipelines in the Cloud with pyDag
High-performance DataFrame library written in C++ with Python bindings.