Sciris
Sciris: Simplifying scientific software in Python - Published in JOSS (2023)
https://github.com/agnostiqhq/covalent
Pythonic tool for orchestrating machine-learning/high performance/quantum-computing workflows in heterogeneous compute environments.
future
:rocket: R package: future: Unified Parallel and Distributed Processing in R for Everyone
https://github.com/agnostiqhq/covalent-ssh-plugin
Executor plugin interfacing Covalent with remote backends using SSH
https://github.com/madsjulia/robustpmap.jl
Robust pmap calls for efficient parallelization and high-performance computing
https://github.com/futureverse/future.apply
:rocket: R package: future.apply - Apply Function to Elements in Parallel using Futures
https://github.com/agnostiqhq/covalent-awsbatch-plugin
Executor plugin interfacing Covalent with AWS Batch
https://github.com/agnostiqhq/covalent-awslambda-plugin
Executor plugin interfacing Covalent with AWS Lambda
https://github.com/agnostiqhq/covalent-ecs-plugin
Executor plugin interfacing Covalent with Amazon ECS Fargate
https://github.com/agnostiqhq/covalent-braket-plugin
Executor plugin interfacing Covalent with Amazon Braket Hybrid Jobs
https://github.com/agnostiqhq/covalent-gcpbatch-plugin
Executor plugin interfacing Covalent with GCP Batch
https://github.com/agnostiqhq/covalent-kubernetes-plugin
Executor plugin interfacing Covalent with Kubernetes
https://github.com/agnostiqhq/covalent-slurm-plugin
Executor plugin interfacing Covalent with Slurm
petar
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.
https://github.com/bluebrain/bluepyparallel
Provides an embarrassingly parallel tool with sql backend
autoparallelizepy
An easy-to-implement python library plugin for mpi4py along with worked examples designed to streamline domain decomposition and add a simplifying layer to noncontiguous MPI parallelization of multidimensional datasets.