| Efficient Execution of Multiple Queries on Deep Memory Hierarchy |
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| 作者姓名： | Yan Zhang Zhi-Feng Chen and Yuan-Yuan Zhou |
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| 作者单位： | [1]National Laboratory on Machine Perception, Peking University, Beijing 100871, China [2]Google Inc., 1600 Amphitheatre Parkway, Mountain View, CA 94043, U.S.A. [3]Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, U.S.A. |
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| 基金项目： | Please contact us if you want a full version. |
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| 摘 要： | This paper proposes a complementary novel idea, called MiniTasking to further reduce the number of cache misses by improving the data temporal locality for multiple concurrent queries. Our idea is based on the observation that, in many workloads such as decision support systems （DSS）, there is usually significant amount of data sharing among different concurrent queries. MiniTasking exploits such data sharing to improve data temporal locality by scheduling query execution at three levels： query level batching, operator level grouping and mini-task level scheduling. The experimental results with various types of concurrent TPC-H query workloads show that, with the traditional N-ary Storage Model （NSM） layout, MiniTasking significantly reduces the L2 cache misses by up to 83%, and thereby achieves 24% reduction in execution time. With the Partition Attributes Across （PAX） layout, MiniTasking further reduces the cache misses by 65% and the execution time by 9%. For the TPC-H throughput test workload, MiniTasking improves the end performance up to 20%.
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| 关 键 词： | 分级存储器体系 超高速缓存性能 时间局部性 微型任务调度 |
| 收稿时间： | 1 May 2006 |
| 修稿时间： | 2006-05-012006-12-19 |
Efficient Execution of Multiple Queries on Deep Memory Hierarchy |
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| Yan Zhang,Zhi-Feng Chen,and Yuan-Yuan Zhou.Efficient Execution of Multiple Queries on Deep Memory Hierarchy[J].Journal of Computer Science and Technology,2007,22(2):273-279. |
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| Authors: | Yan Zhang Zhi-Feng Chen Yuan-Yuan Zhou |
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| Affiliation: | 1National Laboratory on Machine Perception, Peking University, Beijing 100871, China ;2Google Inc., 1600 Amphitheatre Parkway, Mountain View, CA 94043, U.S.A. ;3Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, U.S.A |
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| Abstract: | This paper proposes a complementary novel idea, called MiniTasking to further reduce the number of cache misses by improving the data temporal locality for multiple concurrent, queries. Our idea is based on the observation that, in many workloads such as decision support systems (DSS), there is usually significant amount of data sharing among different concurrent queries. MiniTasking exploits such data sharing to improve data temporal locality by scheduling query execution at three levels: query level batching, operator level grouping and mini-task level scheduling. The experimental results with various types of concurrent TPC-H query workloads show that, with the traditional N-ary Storage Model (NSM) layout, MiniTasking significantly reduces the L2 cache misses by up to 83%, and thereby achieves 24% reduction in execution time. With the Partition Attributes Across (PAX) layout, MiniTasking further reduces the cache misses by 65% and the execution time by 9%. For the TPC-H throughput test workload, MiniTasking improves the end performance up to 20%. |
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| Keywords: | cache performance temporal locality mini-task scheduling concurrent queries |
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