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OLAP on Modern Chiplet-Based Processors

Summary: Shows chiplet-based CPUs introduce intra-socket memory heterogeneity and core-performance variability that invalidate OLAP engines' assumptions of uniform access and cache locality, causing non-linear scaling and poor CPU utilization. Proposes chiplet-aware task/data placement to restore utilization and scalability, achieving up to 7x speedup versus hardware‑oblivious deployments. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
13553
Venue
VLDB
Year
2024
Pagerank
4.1945683e-05
Overall Rank
11,066 | 23.02%
DOI
10.14778/3681954.3682011

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Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
9,980 Declarative Memory Services 2026 CIDR 4.1945683e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 9 of 9 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
66 Spark SQL: Relational Data Processing in Spark 2015 SIGMOD 0.00061639801
137 H-Store: A High-Performance, Distributed Main Memory Transaction Processing System 2008 VLDB 0.00042342967
426 Amazon Redshift and the Case for Simpler Data Warehouses 2015 SIGMOD 0.00023594359
1,284 Amazon Redshift Re-invented 2022 SIGMOD 0.00012837822
1,361 The End of Slow Networks: It's Time for a Redesign 2016 VLDB 0.00012379741
2,445 OLTP on Hardware Islands 2012 VLDB 8.8057827e-05
2,545 POLARIS: The Distributed SQL Engine in Azure Synapse 2020 VLDB 8.5725413e-05
2,916 Quantifying TPC-H Choke Points and Their Optimizations 2020 VLDB 7.9068048e-05
3,443 Distributed Join Algorithms on Thousands of Cores 2017 VLDB 7.0887214e-05
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