Saturn: Efficient Multi-Large-Model Deep Learning

• URL: http://arxiv.org/abs/2311.02840v1
• Date: Mon, 6 Nov 2023 02:59:49 GMT
• Title: Saturn: Efficient Multi-Large-Model Deep Learning
• Authors: Kabir Nagrecha and Arun Kumar
• Abstract summary: We first identify three key interconnected systems challenges for users building large models. We then formalize these as a joint problem, and build a new system architecture to tackle these challenges simultaneously. Our evaluations show that our joint-optimization approach yields 39-49% lower model selection runtimes than typical current DL practice.
• Score: 6.377812618046872
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we propose Saturn, a new data system to improve the efficiency of multi-large-model training (e.g., during model selection/hyperparameter optimization). We first identify three key interconnected systems challenges for users building large models in this setting -- parallelism technique selection, distribution of GPUs over jobs, and scheduling. We then formalize these as a joint problem, and build a new system architecture to tackle these challenges simultaneously. Our evaluations show that our joint-optimization approach yields 39-49% lower model selection runtimes than typical current DL practice.

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