UCCL is an efficient communication library for GPUs, covering collectives, P2P (e.g., KV cache transfer, RL weight transfer), and EP (e.g., GPU-driven)

Ytk-mp4j is a fast, user-friendly, cross-platform, multi-process, multi-thread collective message passing java library which includes gather, scatter, allgather, reduce-scatter, broadcast, reduce, allreduce communications for distributed machine learning.

Summary of call graphs and data structures of NVIDIA Collective Communication Library (NCCL)

Different Implementations of the AllReduce algorithm used in Distributed Deep Learning in c++

Interactive web visualization for understanding collective communication algorithms (as used in NCCL, RCCL, MPI). Learn how AllReduce, Broadcast, Reduce, AllGather and more work step by step.

This repository outlines a comprehensive guide for training a distributed deep learning model.

AdaComp: Adaptive Multi-Tier Compression Middleware for MPI Collectives — 4-tier switching with pipelined computation-communication overlap

Sparse Allreduce

Pool Apple Silicon Macs into a distributed ML training cluster. Zero-config discovery, ring AllReduce gradient sync, HMAC-authenticated fleet isolation with TLS encryption. PyTorch + MLX engines.

MPI collective operations and communication topologies demonstrated across networked compute nodes in GNS3.

AllReduce/AllGather scaling in ASTRA-sim across torus vs switch topologies on the analytical + ns-3 backends — latency-bound vs bandwidth-bound over message size and node count. Reproducible Docker/Chakra harness + write-up.

Summary of call graphs and data structures of collective communication plugin in NVIDIA TensorRT-LLM

Distributed training of a VGG-11 network on the CIFAR-10 dataset

Modified Dissemination/Bruck algorithm for commutative reduction operations in MPI