Summary of Partir: Composing Spmd Partitioning Strategies For Machine Learning, by Sami Alabed et al.
PartIR: Composing SPMD Partitioning Strategies for Machine Learning
by Sami Alabed, Daniel Belov, Bart Chrzaszcz, Juliana Franco, Dominik Grewe, Dougal Maclaurin, James Molloy, Tom Natan, Tamara Norman, Xiaoyue Pan, Adam Paszke, Norman A. Rink, Michael Schaarschmidt, Timur Sitdikov, Agnieszka Swietlik, Dimitrios Vytiniotis, Joel Wee
First submitted to arxiv on: 20 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC); Programming Languages (cs.PL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents PartIR, a neural network (NN) partitioning system that allows for the composition of simpler parallelization strategies while being predictable and hardware-and-runtime agnostic. The design focuses on an incremental approach, featuring a simple but powerful API for composing sharding strategies and a simulator to validate them. The system is driven by high-level programmer-issued partitioning tactics, which can be both manual and automatic. Importantly, the tactics are specified separately from the model code, making them easy to change. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary PartIR is a tool that helps train big neural networks on computers or machines. It lets you combine different ways of dividing up the data or the network itself, making it more efficient. The best part is that it’s easy to use and understand. You can tell it what to do by giving it simple instructions, which makes it flexible and powerful. |
Keywords
* Artificial intelligence * Neural network
