ArrayMorph: Optimizing Hyperslab Queries on the Cloud for Machine Learning Pipelines
Summary: ArrayMorph cost-basedly optimizes hyperslab (region-of-interest) reads from cloud-resident arrays by choosing among chunking, byte-range, and server-side filtering based on layout and platform costs. PyTorch integration cuts transferred data up to 9.8×, improving runtime 1.7× and cost 9×. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Ruochen Jiang (Ohio State University)
- 2. Spyros Blanas (Ohio State University)
BibTeX Citation
@article{jiang_vldb25,
title = {{ArrayMorph: Optimizing Hyperslab Queries on the Cloud for Machine Learning Pipelines}},
author = {Jiang, Ruochen and Blanas, Spyros},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {9},
pages = {3189--3202},
doi = {10.14778/3746405.3746437},
url = {https://doi.org/10.14778/3746405.3746437},
year = {2025}
}
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