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Optimising Operator Sets for Analytical Database Processing on FPGAs

机译:优化FPGA上分析数据库处理的运算符集

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The high throughput and partial reconfiguration capabilities of modern FPGAs make them an attractive hardware platform for query processing in analytical database systems using overlay architectures. The design of existing systems is often solely based on hardware characteristics and thus does not account for all requirements of the application. In this paper, we identify two design issues impeding system integration of low-level database operators for runtime-reconfigurable overlay architectures on FPGAs: First, the granularity of operator sets within each processing pipeline; Second, the mapping of query (sub-)graphs to complex hardware operators. We solve these issues by modeling them as variants of the subgraph isomorphism problem. Via optimised operator fusion guided by a heuristic we reduce the number of required recon-figurable regions between 30% and 85% for relevant TPC-H database benchmark queries. This increase in area efficiency is achieved without performance penalties. In 86% of iterations of the operator fusion process, the proposed heuristic finds optimal candidates, which is 3.6 × more often than for a naive greedy approach.
机译:现代FPGA的高吞吐量和部分重新配置功能使它们成为使用叠加体系结构的分析数据库系统中查询处理的有吸引力的硬件平台。现有系统的设计通常仅基于硬件特性,因此不考虑应用程序的所有要求。在本文中,我们确定了两个设计问题阻碍了在FPGA上的运行时可重新配置的覆盖体系结构的低级数据库运算符的系统集成:首先,每个处理管道内的操作员集的粒度;其次,将查询(子)图映射到复杂的硬件运算符。我们通过将它们建模作为子图同样问题的变种来解决这些问题。通过由启发式引导的优化操作员融合,我们可以减少相关的TPC-H数据库基准查询的30%和85%之间所需的重新计算区域的数量。在没有绩效惩罚的情况下实现了面积效率的这种增加。在86%的操作员融合过程的迭代中,拟议的启发式发现最佳候选人,这比天真的贪婪方法更常见。

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