Summary of Comprehensive Performance Modeling and System Design Insights For Foundation Models, by Shashank Subramanian et al.
Comprehensive Performance Modeling and System Design Insights for Foundation Models
by Shashank Subramanian, Ermal Rrapaj, Peter Harrington, Smeet Chheda, Steven Farrell, Brian Austin, Samuel Williams, Nicholas Wright, Wahid Bhimji
First submitted to arxiv on: 30 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC)
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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 abstract discusses the performance characteristics of large transformer models, specifically generative AI models used in science and industry. It analyzes how these models are affected by various factors such as the type of transformer model, parallelization strategy, and high-performance computing (HPC) system features like accelerators and interconnects. The authors use a performance model to explore this complex design space and highlight its key components. They find that different transformer types have different requirements for parallelism and HPC system characteristics depending on the training regime. For instance, large language models are performant with 3D parallelism and require reduced dependence on accelerator capacity and bandwidth at pre-training scales. On the other hand, long-sequence transformers, representative of scientific foundation models, place a uniform dependence on network and capacity requirements with necessary 4D parallelism. The analysis emphasizes the need for closer performance modeling of different transformer types considering system features and provides a path towards this. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large AI models are changing how we do science and industry. These models use powerful computers to learn from lots of data. But, they also need special computer systems to work well. This paper looks at how these models perform on different computer systems. It finds that different types of models need different kinds of computers to train properly. Some models need a lot of power and memory, while others are more efficient. The paper shows that by understanding how these models work with different computers, we can build better ones. |
Keywords
» Artificial intelligence » Transformer
