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AWARE: Workload-aware, Redundancy-exploiting Linear Algebra
Summary: Introduces AWARE, a workload-aware compression framework for ML pipelines that summarizes their workload and optimizes compression plus execution plans to minimize runtime. It exploits redundancy beyond sparsity with new schemes and kernels, delivering up to 10,000x per-op and 6.6x ML gains over uncompressed baselines.
(summarized by gpt-5-nano on Feb 09 2026)
Paper ID
6567
Venue
SIGMOD
Year
2023
Pagerank
5.370464e-05
Overall Rank
8,794 | 39.67%
DOI
10.1145/3588682
Incoming Non-self Citations Over Time
BibTeX Citation
Copy BibTeX
@inproceedings{baunsgaard_sigmod23,
title = {{AWARE: Workload-aware, Redundancy-exploiting Linear Algebra}},
author = {Baunsgaard, Sebastian and Boehm, Matthias},
series = {{SIGMOD} '23},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3588682},
url = {https://dl.acm.org/doi/10.1145/3588682},
year = {2023}
}
Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
Outgoing Citations (Sorted by Pagerank)
Showing 38 of 38 cited papers.
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