Open-source software for volunteer computing and grid computing.
-
Updated
May 26, 2024 - PHP
Open-source software for volunteer computing and grid computing.
A modular, primitive-first, python-first PyTorch library for Reinforcement Learning.
Tensor parallelism is all you need. Run LLMs on weak devices or make powerful devices even more powerful by distributing the workload and dividing the RAM usage.
Multi-platform Scheduling and Workflows Engine
ProActive Programming library
mirai - Minimalist Async Evaluation Framework for R
The current, performant & industrial strength version of Holochain on Rust.
LinearSolve.jl: High-Performance Unified Interface for Linear Solvers in Julia. Easily switch between factorization and Krylov methods, add preconditioners, and all in one interface.
Making large AI models cheaper, faster and more accessible
Prime95 source code from GIMPS to find Mersenne Prime.
Hazelcast is a unified real-time data platform combining stream processing with a fast data store, allowing customers to act instantly on data-in-motion for real-time insights.
Backend.AI is a streamlined, container-based computing cluster platform that hosts popular computing/ML frameworks and diverse programming languages, with pluggable heterogeneous accelerator support including CUDA GPU, ROCm GPU, TPU, IPU and other NPUs.
Arkouda (αρκούδα): Interactive Data Analytics at Supercomputing Scale 🐻
The PennyLane-Lightning plugin provides a fast state-vector simulator written in C++ for use with PennyLane
Distributed DataFrame for Python designed for the cloud, powered by Rust
The open-source serverless GPU container runtime.
Run GPU inference and training jobs on serverless infrastructure that scales with you.
Distributed computing is the field of study that deals with the division of tasks between multiple computers connected in a network.
KaMPIng: (Near) zero-overhead MPI wrapper for modern C++
zenoh unifies data in motion, data in-use, data at rest and computations. It carefully blends traditional pub/sub with geo-distributed storages, queries and computations, while retaining a level of time and space efficiency that is well beyond any of the mainstream stacks.
Add a description, image, and links to the distributed-computing topic page so that developers can more easily learn about it.
To associate your repository with the distributed-computing topic, visit your repo's landing page and select "manage topics."