DimmWitted: A Study of Main-Memory Statistical Analytics
Summary: Study of main-memory statistical analytics on NUMA; compares row- vs column-order access and data/model sharing granularity. Uncovers hardware-statistical efficiency tradeoffs; prototype runs popular tasks (SVM, LR, Gibbs, NN) up to 100x faster across five architectures. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Ce Zhang (Stanford University; University of Wisconsin)
- 2. Christopher Ré (Stanford University)
BibTeX Citation
@article{zhang_vldb14,
title = {{DimmWitted: A Study of Main-Memory Statistical Analytics}},
author = {Zhang, Ce and Ré, Christopher},
journal = {PVLDB},
series = {{VLDB} '14},
volume = {7},
number = {12},
pages = {1283--1294},
doi = {10.14778/2732977.2732988},
url = {https://doi.org/10.14778/2732977.2732988},
year = {2014}
}
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