Hone: “Scaling Down” Hadoop on Shared-Memory Systems
Summary: Hone scales Hadoop down to shared-memory multicore servers while preserving API and binary compatibility, executing existing Hadoop jars unchanged. It avoids distributed overhead, achieving up to 10× speedup over pseudo-distributed Hadoop and sometimes outperforming a 15-node cluster for in-memory data. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. K. Ashwin Kumar (University of Maryland)
- 2. Jonathan Gluck (University of Maryland)
- 3. Amol Deshpande (University of Maryland)
- 4. Jimmy Lin (University of Maryland)
BibTeX Citation
@article{kumar_vldb13,
title = {{Hone: “Scaling Down” Hadoop on Shared-Memory Systems}},
author = {Kumar, K. Ashwin and Gluck, Jonathan and Deshpande, Amol and Lin, Jimmy},
journal = {PVLDB},
series = {{VLDB} '13},
volume = {6},
number = {12},
pages = {1354--1365},
doi = {10.14778/2536274.2536304},
url = {https://doi.org/10.14778/2536274.2536304},
year = {2013}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 12,170 | Palette: Enabling Scalable Analytics for Big-Memory, Multicore Machines | 2014 | SIGMOD | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 2 of 2 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 372 | HaLoop: Efficient Iterative Data Processing on Large Clusters | 2010 | VLDB | 0.0001981521 |
| 3,719 | M3R: Increased Performance for In-Memory Hadoop Jobs | 2012 | VLDB | 7.1740814e-05 |
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