NCCL
idist-snippets
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| NCCL | idist-snippets | |
|---|---|---|
| 3 | 1 | |
| 2,796 | 4 | |
| 3.5% | - | |
| 5.9 | 0.0 | |
| 8 days ago | almost 3 years ago | |
| C++ | Python | |
| GNU General Public License v3.0 or later | - |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
NCCL
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MPI jobs to test
% rm -rf /tmp/nccl ; git clone --recursive https://github.com/NVIDIA/nccl.git ; cd nccl ; git grep MPI Cloning into 'nccl'... remote: Enumerating objects: 2769, done. remote: Counting objects: 100% (336/336), done. remote: Compressing objects: 100% (140/140), done. remote: Total 2769 (delta 201), reused 287 (delta 196), pack-reused 2433 Receiving objects: 100% (2769/2769), 3.04 MiB | 3.37 MiB/s, done. Resolving deltas: 100% (1820/1820), done. README.md:NCCL (pronounced "Nickel") is a stand-alone library of standard communication routines for GPUs, implementing all-reduce, all-gather, reduce, broadcast, reduce-scatter, as well as any send/receive based communication pattern. It has been optimized to achieve high bandwidth on platforms using PCIe, NVLink, NVswitch, as well as networking using InfiniBand Verbs or TCP/IP sockets. NCCL supports an arbitrary number of GPUs installed in a single node or across multiple nodes, and can be used in either single- or multi-process (e.g., MPI) applications. src/collectives/broadcast.cc:/* Deprecated original "in place" function, similar to MPI */
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NVLink and Dual 3090s
If it's rendering, you don't really need SLI, you need to install NCCL so that GPUs memory can be pooled: https://github.com/NVIDIA/nccl
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Distributed Training Made Easy with PyTorch-Ignite
backends from native torch distributed configuration: nccl, gloo, mpi.
idist-snippets
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Distributed Training Made Easy with PyTorch-Ignite
Code snippets, as well as commands for running all the scripts, are provided in a separate repository.
What are some alternatives?
gloo - Collective communications library with various primitives for multi-machine training.
ignite - High-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently.
C++ Actor Framework - An Open Source Implementation of the Actor Model in C++
Thrust - [ARCHIVED] The C++ parallel algorithms library. See https://github.com/NVIDIA/cccl
why-ignite - Why should we use PyTorch-Ignite ?
HPX - The C++ Standard Library for Parallelism and Concurrency
xla - Enabling PyTorch on XLA Devices (e.g. Google TPU)
ompi - Open MPI main development repository
Easy Creation of GnuPlot Scripts from C++ - A simple C++17 lib that helps you to quickly plot your data with GnuPlot
Taskflow - A General-purpose Parallel and Heterogeneous Task Programming System