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arXiv · 1910.02653

Checkmate: Breaking the Memory Wall with Optimal Tensor Rematerialization

Paras Jain·Ajay Jain·Aniruddha Nrusimha·Amir Gholami·Pieter Abbeel·Kurt Keutzer·Ion Stoica·Joseph E. Gonzalez
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Abstract

We formalize the problem of trading-off DNN training time and memory requirements as the tensor rematerialization optimization problem, a generalization of prior checkpointing strategies. We introduce Checkmate, a system that solves for optimal rematerialization schedules in reasonable times (under an hour) using off-the-shelf MILP solvers or near-optimal schedules with an approximation algorithm, then uses these schedules to accelerate millions of training iterations. Our method scales to complex, realistic architectures and is hardware-aware through the use of accelerator-specific, profile-based cost models. In addition to reducing training cost, Checkmate enables real-world networks to be trained with up to 5.1x larger input sizes. Checkmate is an open-source project, available at https://github.com/parasj/checkmate.

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Paras Jain, Ajay Jain, Aniruddha Nrusimha, Amir Gholami, Pieter Abbeel, Kurt Keutzer, Ion Stoica, Joseph E. Gonzalez. 2020-05-14. Checkmate: Breaking the Memory Wall with Optimal Tensor Rematerialization. https://arxiv.org/abs/1910.02653

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