{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/checkmate-breaking-the-memory-wall-with","title":"Checkmate: Breaking the Memory Wall with Optimal Tensor Rematerialization","arxiv_id":"1910.02653","date":"2019-10-07","proceeding":null,"authors":["Paras Jain","Ajay Jain","Aniruddha Nrusimha","Amir Gholami","Pieter Abbeel","Kurt Keutzer","Ion Stoica","Joseph E. Gonzalez"],"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.","url_abs":"https://arxiv.org/abs/1910.02653v3","url_pdf":"https://arxiv.org/pdf/1910.02653v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"checkmate-breaking-the-memory-wall-with","repo_url":"https://github.com/parasj/checkmate","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"checkmate-breaking-the-memory-wall-with","repo_url":"https://github.com/checkmateai/checkmate","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1910.02653","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}