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
- 2. Tere Gonzalez
- 3. Jun Li
- 4. Manish Marwah
- 5. Jim Pruyne
- 6. Krishnamurthy Viswanathan
- 7. Mijung Kim
Incoming Citations (Sorted by Pagerank)
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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 |
|---|---|---|---|---|
| 87 | Hekaton: SQL Server’s Memory-Optimized OLTP Engine | 2013 | SIGMOD | 0.00052389723 |
| 404 | Multi-Core, Main-Memory Joins: Sort vs. Hash Revisited | 2014 | VLDB | 0.00024143076 |
| 3,504 | M3R: Increased Performance for In-Memory Hadoop Jobs | 2012 | VLDB | 7.0347515e-05 |
| 7,511 | Hone: "Scaling Down" Hadoop on Shared-Memory Systems | 2013 | VLDB | 4.7180617e-05 |
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