Scalability and Efficiency of Database Queries on Future Many-Core Systems

P. Petrides, Andreas Diavastos, C. Christofi, P. Trancoso
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引用次数: 2

Abstract

Decision Support System (DSS) workloads are known to be one of the most time-consuming database workloads that process large data sets. Traditionally, DSS queries have been accelerated using large-scale multiprocessors. In this work we exploit the benefits of using future many-core architectures, more specifically on-chip clustered many-core architectures. To achieve this goal we propose different representative data parallel versions of the original database scan and join algorithms. We also study the impact on the performance when on-chip memory, shared among all cores, is used as a prefetching buffer. For our experiments we study the behaviour of three queries from the standard DSS benchmark TPC-H executing on the Intel Single chip Cloud Computer experimental processor (Intel SCC). Our results show that parallelism can be well exploited by such architectures and how important it is to have a balance between computation and data intensity. Moreover, from our experimental results we show that performance improvement of 5x and 10x for the corresponding query implementation without data prefetching. Finally we show how we could efficiently use the system in order to achieve high power-performance efficiency when using the proposed prefetching buffer.
未来多核系统中数据库查询的可扩展性和效率
众所周知,决策支持系统(DSS)工作负载是处理大型数据集时最耗时的数据库工作负载之一。传统上,DSS查询使用大规模多处理器来加速。在这项工作中,我们利用了使用未来多核架构的好处,更具体地说,是片上集群多核架构。为了实现这一目标,我们提出了原始数据库扫描和连接算法的不同代表性数据并行版本。我们还研究了在所有内核之间共享的片上内存用作预取缓冲区时对性能的影响。在我们的实验中,我们研究了在英特尔单芯片云计算机实验处理器(英特尔SCC)上执行的来自标准DSS基准TPC-H的三个查询的行为。我们的结果表明,这种架构可以很好地利用并行性,并且在计算和数据强度之间取得平衡是多么重要。此外,从我们的实验结果中,我们表明,在没有数据预取的情况下,相应的查询实现的性能提高了5倍和10倍。最后,我们展示了如何有效地使用该系统,以便在使用所提出的预取缓冲区时实现高功率性能效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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