Palette: Enabling Scalable Analytics for Big-Memory, Multicore Machines
Summary: Palette enables in-memory analytics on big NUMA multicore machines with multiple input representations, trading space for time. A cost-based selector automatically picks the fastest operator while preserving Hadoop APIs; demos cover creation and auto-selection. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Fei Chen (Hewlett Packard Enterprise)
- 2. Tere Gonzalez (Hewlett Packard Enterprise)
- 3. Jun Li (Hewlett Packard Enterprise)
- 4. Manish Marwah (Hewlett Packard Enterprise)
- 5. Jim Pruyne (Hewlett Packard Enterprise)
- 6. Krishnamurthy Viswanathan (Hewlett Packard Enterprise)
- 7. Mijung Kim (Hewlett Packard Enterprise)
BibTeX Citation
@inproceedings{chen_sigmod14,
title = {{Palette: Enabling Scalable Analytics for Big-Memory, Multicore Machines}},
author = {Chen, Fei and Gonzalez, Tere and Li, Jun and Marwah, Manish and Pruyne, Jim and Viswanathan, Krishnamurthy and Kim, Mijung},
series = {{SIGMOD} '14},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/2588555.2594509},
url = {https://dl.acm.org/doi/10.1145/2588555.2594509},
year = {2014}
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 38 | Hekaton: SQL Server’s Memory-Optimized OLTP Engine | 2013 | SIGMOD | 0.00047648573 |
| 252 | Multi-Core, Main-Memory Joins: Sort vs. Hash Revisited | 2014 | VLDB | 0.00023242719 |
| 3,719 | M3R: Increased Performance for In-Memory Hadoop Jobs | 2012 | VLDB | 7.1740814e-05 |
| 7,932 | Hone: “Scaling Down” Hadoop on Shared-Memory Systems | 2013 | VLDB | 5.5181056e-05 |
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