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)
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Authors
- 1. Iraklis Psaroudakis (EPFL)
- 2. Tobias Scheuer (SAP)
- 3. Norman May (SAP)
- 4. Abdelkader Sellami (SAP)
- 5. Anastasia Ailamaki (EPFL)
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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