Hash Join Optimization Based on Shared Cache Chip Multi-processor

Authors:
Deng Yadan;Jing Ning;Xiong Wei;Chen Luo;Chen Hongsheng
Affiliations:
College of Electronic Science and Engineering, National University of Defense Technology, Changsha, China 410073;College of Electronic Science and Engineering, National University of Defense Technology, Changsha, China 410073;College of Electronic Science and Engineering, National University of Defense Technology, Changsha, China 410073;College of Electronic Science and Engineering, National University of Defense Technology, Changsha, China 410073;College of Electronic Science and Engineering, National University of Defense Technology, Changsha, China 410073
Venue:
DASFAA '09 Proceedings of the 14th International Conference on Database Systems for Advanced Applications
Year:
2009

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Abstract

Chip Multi-Processor(CMP) allows multiple threads to execute simultaneously. Because threads share various resources of CMP, such as L2-Cache, CMP system is inherently different from multiprocessors system and, CMP is also different from simultaneous multithreading (SMT). It could support more than two threads to execute simultaneously, and some executing units are owned by each core. We present hash join optimization based on shared cache CMP. Firstly, we propose multithreaded hash join execution framework based on Radix-Join algorithm, then we analyze the factors which affect performance of multithreaded Radix-Join algorithm in CMP. Basing on this analysis, we optimize the performance of various threads and their shared-cache access performance in the framework, and then theoretic analysis of speedup in multithreaded cluster partition phase is presents which could give some advices to cluster partition thread optimization. All of our algorithms are implemented in EaseDB. In the experiments, we evaluate performance of the multithreaded hash join execution framework, and the results show that our algorithm could effectively resolve cache access conflict and load imbalance in multithreaded environment. Hash join performance is improved.