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Scaling Up Concurrent Main-Memory Column-Store Scans: Towards Adaptive NUMA-aware Data and Task Placement

Summary: NUMA-aware data placement and task scheduling for concurrent main-memory column-stores. Comparative evaluation shows unnecessary partitioning reduces throughput up to 70% and memory-heavy task stealing up to 58%, motivating adaptive, workload-aware design. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
11210
Venue
VLDB
Year
2015
Pagerank
6.9663191e-05
Overall Rank
4,001 | 72.56%
DOI
10.14778/2824032.2824037

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{psaroudakis_vldb15,
        title = {{Scaling Up Concurrent Main-Memory Column-Store Scans: Towards Adaptive NUMA-aware Data and Task Placement}},
        author = {Psaroudakis, Iraklis and Scheuer, Tobias and May, Norman and Sellami, Abdelkader and Ailamaki, Anastasia},
        journal = {PVLDB},
        series = {{VLDB} '15},
        volume = {8},
        number = {12},
        pages = {1442--1453},
        doi = {10.14778/2824032.2824037},
        url = {https://doi.org/10.14778/2824032.2824037},
        year = {2015}
}

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