Compressed Linear Algebra for Large-Scale Machine Learning
Summary: CLA applies database-style compression to matrices and executes matrix-vector products directly on compressed data. Novel column compression, cache-conscious operators, and a sampling-based scheme yield near-uncompressed performance with substantial memory savings. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Ahmed Elgohary (University of Maryland)
- 2. Matthias Boehm (IBM)
- 3. Peter J. Haas (IBM)
- 4. Frederick R. Reiss (IBM)
- 5. Berthold Reinwald (IBM)
BibTeX Citation
@article{elgohary_vldb16,
title = {{Compressed Linear Algebra for Large-Scale Machine Learning}},
author = {Elgohary, Ahmed and Boehm, Matthias and Haas, Peter J. and Reiss, Frederick R. and Reinwald, Berthold},
journal = {PVLDB},
series = {{VLDB} '16},
volume = {9},
number = {12},
pages = {960--971},
doi = {10.14778/2994509.2994510},
url = {https://doi.org/10.14778/2994509.2994510},
year = {2016}
}
Incoming Citations (Sorted by Pagerank)
Showing 30 of 30 citing papers.
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 16 of 16 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 10,589 | Morphing-based Compression for Data-centric ML Pipelines | 2026 | VLDB |
| 2 | 10,580 | GPU Acceleration of SQL Analytics on Compressed Data | 2026 | VLDB |
| 3 | 3,459 | A Comparative Evaluation of Systems for Scalable Linear Algebra-based Analytics | 2018 | VLDB |
| 4 | 4,049 | Resource Elasticity for Large-Scale Machine Learning | 2015 | SIGMOD |
| 5 | 6,143 | ColumnML: Column-Store Machine Learning with On-The-Fly Data Transformation | 2019 | VLDB |
| 6 | 6,485 | Tuple-oriented Compression for Large-scale Mini-batch Stochastic Gradient Descent | 2019 | SIGMOD |
| 7 | 9,520 | Experimental Analysis of Large-scale Learnable Vector Storage Compression | 2024 | VLDB |
| 8 | 6,046 | Automatic Optimization of Matrix Implementations for Distributed Machine Learning and Linear Algebra | 2021 | SIGMOD |
| 9 | 8,794 | AWARE: Workload-aware, Redundancy-exploiting Linear Algebra | 2023 | SIGMOD |
| 10 | 8,617 | Improving Matrix-vector Multiplication via Lossless Grammar-Compressed Matrices | 2022 | VLDB |