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MemFlow: Memory-Aware Distributed Deep Learning
Summary: MemFlow is a memory-aware distributed DNN optimizer that jointly optimizes memory usage vs training time to yield Pareto configs. It builds a memory-estimated task graph, simulates parallelism, and uses MCMC to explore recomputation vs compute tradeoffs.
(summarized by gpt-5-nano on Feb 09 2026)
- Paper ID
- 5849
- Venue
- SIGMOD
- Year
- 2020
- Pagerank
- 5.3868654e-05
- Overall Rank
- 9,083 | 36.88%
- DOI
-
10.1145/3318464.3384416
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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Cited Paper |
Year |
Venue |
Pagerank |
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