A high-performance distributed training framework for Reinforcement Learning
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Updated
May 23, 2024 - Python
A high-performance distributed training framework for Reinforcement Learning
Open-source, free, self-hosted alternative to Cypress Dashboard
for mass exploiting
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
Pythonic tool for orchestrating machine-learning/high performance/quantum-computing workflows in heterogeneous compute environments.
🚀 R package: future: Unified Parallel and Distributed Processing in R for Everyone
FastFlow pattern-based parallel programming framework (formerly on sourceforge)
Software rendering engine with PBR. Built from scratch on C++.
Maven plugin that simplifies running Cucumber scenarios in parallel.
ClusterRunner makes it easy to parallelize test suites across your infrastructure in the fastest and most efficient way possible.
Modified version of Alphafold to divide CPU part (MSA and template searching) and GPU part. This can accelerate Alphafold when predicting multiple structures
A Tool for Automatic Parallelization of Deep Learning Training in Distributed Multi-GPU Environments.
An evolutionary computation framework to (automatically) build fast parallel stochastic optimization solvers
PARALLEL: Stata module for parallel computing
Header only framework for data analysis in massively parallel platforms.
Enables the parallelization of Symfony Console commands.
PeTar is a high-performance N-body code for modelling the evolution of star clusters and tidal streams, including the effect of galactic potential, dynamics of binary and hierarchical system, single and binary stellar evolution.
Parallel computing implementation examples
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