A Comparative Evaluation of Systems for Scalable Linear Algebra-based Analytics
Summary: LA-focused benchmarks compare scalable linear-algebra systems (MADlib, MLlib, SystemML, ScaLAPACK, SciDB, TensorFlow) on real and synthetic data. Reveals bottlenecks, unusual performance trends, and bugs; guides improvements and provides open code/data for replication. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Anthony Thomas (University of California San Diego)
- 2. Arun Kumar (University of California San Diego)
BibTeX Citation
@article{thomas_vldb18,
title = {{A Comparative Evaluation of Systems for Scalable Linear Algebra-based Analytics}},
author = {Thomas, Anthony and Kumar, Arun},
journal = {PVLDB},
series = {{VLDB} '18},
volume = {11},
number = {13},
pages = {2168--2182},
doi = {10.14778/3275366.3275367},
url = {https://doi.org/10.14778/3275366.3275367},
year = {2018}
}
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