Divide-and-Conquer Checkpointing for Arbitrary Programs with No User Annotation
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Abstract (arXiv)
Classical reverse-mode automatic differentiation (AD) imposes only a small constant-factor overhead in operation count over the original computation, but has storage requirements that grow, in the worst case, in proportion to the time consumed by the original computation. This storage blowup can be ameliorated by checkpointing, a process that reorders application of classical reverse-mode AD over an execution interval to tradeoff space vs time. Application of checkpointing in a divide-and-conquer fashion to strategically chosen nested execution intervals can break classical reverse-mode AD into stages which can reduce the worst-case growth in storage from linear to sublinear. Doing this has been fully automated only for computations of particularly simple form, with checkpoints spanning execution intervals resulting from a limited set of program constructs. Here we show how the technique can be automated for arbitrary computations. The essential innovation is to apply the technique at the level of the language implementation itself, thus allowing checkpoints to span any execution interval.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Aug 2017.
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Citing papers per year: 2021: 1; 2022: 3; 2023: 1; 2024: 1.
6 papers.
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- 2024CST: Calibration Side-Tuning for Parameter and Memory Efficient Transfer Learning20 Feb 2024
- 2023Seven open problems in applied combinatorics20 Mar 2023
- 2022XEngine: Optimal Tensor Rematerialization for Neural Networks in Heterogeneous Environments19 Dec 2022
- 2022Survey on Large Scale Neural Network Training21 Feb 2022
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- 2021Physics-Aware Downsampling with Deep Learning for Scalable Flood Modeling14 Jun 2021