parallel-processing

Parallel processing with progress bars

A lightweight Python framework for parallel, isolated, collaborative AI reasoning.

Parallel SSH Tools

适用于高性能系统的多进程解压缩软件(A multiprocess decompression software for high-performance system)

Text Mining and Topic Modeling Toolkit for Python with parallel processing power

Python Multi-Process Execution Pool: concurrent asynchronous execution pool with custom resource constraints (memory, timeouts, affinity, CPU cores and caching), load balancing and profiling capabilities of the external apps on NUMA architecture

The Accelerator is a tool for fast and reproducible processing of large amounts of data.

A rotating proxy solution to bot YouTube views

Repository for Parallel Programming course given by Assoc. Prof. Dr. Bora Canbula at Computer Engineering Department of Manisa Celal Bayar University.

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.

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.

Fast regression and mediation analysis of vertex or voxel MRI data with TFCE

Process 1000 calls to ChatGPT API in 2m

Runtime provenance for AI and scientific workflows—capture, enrich, and query workflow data via observability adapters and code annotation across edge, cloud, and HPC.

Parallel computing implementation examples

fragments image and re-assemble them back to original image. #Prim's MST algorithm

Scheduling Big Data Workloads and Data Pipelines in the Cloud with pyDag

Parallel NDJSON Reader for Python

Easy ETL