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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
4.3807157e-05
Overall Rank
9,174 | 36.25%
DOI
10.1145/3318464.3384416

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Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
8,340 Deep Learning: Systems and Responsibility 2021 SIGMOD 4.5381546e-05
9,596 Scalable Graph Convolutional Network Training on Distributed-Memory Systems 2023 VLDB 4.3150788e-05
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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