PARL A high-performance distributed training framework for Reinforcement Learning

Extract Transform Load for Python 3.5+

A package which efficiently applies any function to a pandas dataframe or series in the fastest available manner

Fast and distributed workflow runner using ActiveJob and Redis

🚀 R package: future: Unified Parallel and Distributed Processing in R for Everyone

for mass exploiting

An open-source on-premise, self-hosted alternative to cypress dashboard

Software rendering engine with PBR. Built from scratch on C++.

ClusterRunner makes it easy to parallelize test suites across your infrastructure in the fastest and most efficient way possible.

Cuneiform distributed programming language

Enables the parallelization of Symfony Console commands.

🚀 R package: future.apply - Apply Function to Elements in Parallel using Futures

A Tool for Automatic Parallelization of Deep Learning Training in Distributed Multi-GPU Environments.

FastFlow pattern-based parallel programming framework (formerly on sourceforge)

Maven plugin that simplifies running Cucumber Scenarios in parallel.

Header only framework for data analysis in massively parallel platforms.

Easy to use map and starmap python equivalents

PARALLEL: Stata module for parallel computing

Simplifies the parallelization of function calls.

On-demand worker pools for parallelizable tasks

Actor4j is an actor-oriented Java framework. Useful for building lightweighted microservices (these are the actors themselves or groups of them). Enhanced performance of message passing.

Par4All is an automatic parallelizing and optimizing compiler (workbench) for C and Fortran sequential programs

🚀 R package future.callr: A Future API for Parallel Processing using 'callr'

A C++ evolutionary computation framework to build parallel stochastic optimization solvers

The Selenified Test Framework provides mechanisms for simply testing applications at multiple tiers while easily integrating into DevOps build environments. Selenified provides traceable reporting for both web and API testing, wraps and extends Selenium calls to more appropriately handle testing errors, and supports testing over multiple browsers locally, or in the cloud in parallel. It can be a great starting point for building or improving test automation in your organization.

corebench - run your benchmarks against high performance computing servers with many CPU cores

Tracking the progress of mc*apply with progress bar.

Open Optimizing Parallelizing System

Parallel file copying for Django's collectstatic.