Model-Parallel Model Selection for Deep Learning Systems
Summary: Introduces shard parallelism that blends task- and model-parallelism to run DL models on multi-device setups. Hydra partitions models into fine-grained shards and schedules them for higher utilization and faster training than classic model-parallelism. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Kabir Nagrecha (University of California San Diego)
BibTeX Citation
@inproceedings{nagrecha_sigmod21,
title = {{Model-Parallel Model Selection for Deep Learning Systems}},
author = {Nagrecha, Kabir},
series = {{SIGMOD} '21},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3448016.3450571},
url = {https://dl.acm.org/doi/10.1145/3448016.3450571},
year = {2021}
}Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 8,454 | D3-GNN: Dynamic Distributed Dataflow for Streaming Graph Neural Networks | 2024 | VLDB | 5.4226e-05 |
| 9,737 | Saturn: An Optimized Data System for Multi-Large-Model Deep Learning Workloads | 2024 | VLDB | 5.227679e-05 |
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 0 of 0 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|
Previous
Page 1 / 1
Next