Massively parallel numa-aware hash joins
Lang H, Leis V, Albutiu MC, Neumann T, Kemper A (2015)
Publication Type: Conference contribution
Publication year: 2015
Journal
Publisher: Springer Verlag
Book Volume: 8921
Pages Range: 3-14
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Hongzhou
ISBN: 9783319139593
DOI: 10.1007/978-3-319-13960-9_1
Abstract
Driven by the two main hardware trends increasing main memory and massively parallel multi-core processing in the past few years, there has been much research effort in parallelizing well-known join algorithms. However, the non-uniform memory access (NUMA) of these architectures to main memory has only gained limited attention in the design of these algorithms. We study recent proposals of main memory hash join implementations and identify their major performance problems on NUMA architectures. We then develop a NUMA-aware hash join for massively parallel environments, and show how the specific implementation details affect the performance on a NUMA system. Our experimental evaluation shows that a carefully engineered hash join implementation outperforms previous high performance hash joins by a factor of more than two, resulting in an unprecedented throughput of 3/4 billion join argument quintuples per second.
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Germany (DE)How to cite
APA:
Lang, H., Leis, V., Albutiu, M.-C., Neumann, T., & Kemper, A. (2015). Massively parallel numa-aware hash joins. In Thomas Neumann, Andrew Pavlo, Justin Levandoski, Arun Jagatheesan (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 3-14). Hongzhou, CN: Springer Verlag.
MLA:
Lang, Harald, et al. "Massively parallel numa-aware hash joins." Proceedings of the 1st International Workshop on In-Memory Data Management and Analytics, IMDM 2013 and 2nd International Workshop on In-Memory Data Management and Analytics, IMDM 2014, Hongzhou Ed. Thomas Neumann, Andrew Pavlo, Justin Levandoski, Arun Jagatheesan, Springer Verlag, 2015. 3-14.
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