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引用次数: 16
摘要
在提供独特的性能和节能优势的同时,使用现场可编程门阵列(fpga)进行数据库加速已经要求系统设计师做出重大让步。可编程芯片要么用于非常基本的应用程序任务(例如实现一类严格的选择谓词),要么必须在运行时完全重新编译它们的电路定义——这是一项非常耗费cpu和时间的工作。这项工作消除了这种让步的需要。作为XLynx实现(基于fpga的XML过滤器)的一部分,我们提出了框架自动机,这是一种用于数据密集型硬件电路的设计原则,它同时提供了高表达性和快速重新配置。骨架自动机提供了一类有限状态自动机的通用实现。它们可以在几微秒或更短的时间内参数化为任何特定的自动机实例(完全重新编译需要几分钟或几小时)。我们展示了基于XML投影的骨架自动机[Marian and sim 2003],这是一种过滤技术,说明了我们的策略在现实世界中具有挑战性的任务中的可行性。通过在硬件中执行XML投影并在网络中过滤数据,我们报告了几个因素的性能改进,同时保持对后端XML处理器的非侵入性(我们使用Saxon引擎评估XLynx)。
XLynx—An FPGA-based XML filter for hybrid XQuery processing
While offering unique performance and energy-saving advantages, the use of Field-Programmable Gate Arrays (FPGAs) for database acceleration has demanded major concessions from system designers. Either the programmable chips have been used for very basic application tasks (such as implementing a rigid class of selection predicates) or their circuit definition had to be completely recompiled at runtime—a very CPU-intensive and time-consuming effort.
This work eliminates the need for such concessions. As part of our XLynx implementation—an FPGA-based XML filter—we present skeleton automata, which is a design principle for data-intensive hardware circuits that offers high expressiveness and quick reconfiguration at the same time. Skeleton automata provide a generic implementation for a class of finite-state automata. They can be parameterized to any particular automaton instance in a matter of microseconds or less (as opposed to minutes or hours for complete recompilation).
We showcase skeleton automata based on XML projection [Marian and Siméon 2003], a filtering technique that illustrates the feasibility of our strategy for a real-world and challenging task. By performing XML projection in hardware and filtering data in the network, we report on performance improvements of several factors while remaining nonintrusive to the back-end XML processor (we evaluate XLynx using the Saxon engine).
期刊介绍:
Heavily used in both academic and corporate R&D settings, ACM Transactions on Database Systems (TODS) is a key publication for computer scientists working in data abstraction, data modeling, and designing data management systems. Topics include storage and retrieval, transaction management, distributed and federated databases, semantics of data, intelligent databases, and operations and algorithms relating to these areas. In this rapidly changing field, TODS provides insights into the thoughts of the best minds in database R&D.