A Novel Multicontext Coarse-Grained Reconfigurable Architecture (CGRA) For Accelerating Column-Oriented Databases
A Novel Multicontext Coarse-Grained Reconfigurable Architecture (CGRA) For Accelerating Column-Oriented Databases
复制标题
用于加速列式数据库的新型多上下文粗粒度可重构架构 (CGRA)
DOI：
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发表时间：
2011
期刊：
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通讯作者：
J. Lee
中科院分区：
文献类型：
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作者：
Pranav S. Vaidya;J. Lee
面向列的数据库的存储模型在结构上类似于许多高性能计算应用中的密集矩阵/向量。因此，使用可重构逻辑（RL）协处理器的硬件加速向量化矩阵运算可能与数据库的硬件加速有相似之处。在本文中，我们通过提出一种多上下文、粗粒度可重构协处理器单元 (RU) 模型来探索这一假设，该模型用于加速面向列数据库的硬件中的一些数据库操作。然后，我们描述等连接、非等连接和反向查找数据库操作的硬件算法的实现。最后，我们使用微基准查询来评估这些算法。我们的结果表明，所提出的 RU 模型上的查询执行比纯软件查询执行快一到两个数量级。
The storage model of column-oriented databases is similar in structure to densely packed matrices/vectors found in many high-performance computing applications. Hence, hardware-accelerated vectorized matrix operations using Reconfigurable Logic (RL) coprocessors may find parallels in hardware acceleration of databases. In this article, we explore this hypothesis by proposing a multicontext, coarse-grained Reconfigurable coprocessor Unit (RU) model that is used to accelerate some of the database operations in hardware for column-oriented databases. We then describe the implementation of hardware algorithms for the equi-join, nonequi-join, and inverse-lookup database operations. Finally, we evaluate these algorithms using a microbenchmark query. Our results indicate that the query execution on the proposed RU model is one to two orders of magnitude faster than the software-only query execution.