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An open collection of implementation tips, tricks and resources for training large language models
An open collection of methodologies to help with successful training of large language models.
Distributed and decentralized training framework for PyTorch over graph
Efficient Distributed GPU Programming for Exascale, an SC/ISC Tutorial
NCCL Fast Socket is a transport layer plugin to improve NCCL collective communication performance on Google Cloud.
Sample examples of how to call collective operation functions on multi-GPU environments. A simple example of using broadcast, reduce, allGather, reduceScatter and sendRecv operations.
NCCL Examples from Official NVIDIA NCCL Developer Guide.
N-Ways to Multi-GPU Programming
use ncclSend ncclRecv realize ncclSendrecv ncclGather ncclScatter ncclAlltoall
Experiments with low level communication patterns that are useful for distributed training.
Blink+: Increase GPU group bandwidth by utilizing across tenant NVLink.
Distributed deep learning framework based on pytorch/numba/nccl and zeromq.
Tool to run rccl-tests/nccl-tests based on from an application and gather performance.
Hands-on Labs in Parallel Computing
jupyter/scipy-notebook with CUDA Toolkit, cuDNN, NCCL, and TensorRT
Librería de operaciones matemáticas con matrices multi-gpu utilizando Nvidia NCCL.
Installation script to install Nvidia driver and CUDA automatically in Ubuntu
EUMaster4HPC student challenge group 7 - EuroHPC Summit 2024 Antwerp
Single-node data parallelism in Julia with CUDA
Default Docker image used to run experiments on csquare.run.
Blood Cell Simulation server