Large production systems (rule-based systems) continue to suffer from extremely slow execution which limits their utility in practical applications as well as in research settings. Most investigations in speeding up these systems have focused on match (or knowledge-search) parallelism. Although good speed-ups have been achieved in this process, these investigations have revealed the limitations on the total speed-up available from this source. This limited speed-up is insufficient to alleviate the problem of slow execution in large-scale production system implementations. Such large-scale systems are expected to increase as researchers develop increasingly more competent production systems.
rnIn this paper, we focus on task-level parallelism, which is obtained by a high-level decomposition of the production system. Speed-ups obtained from task-level parallelism will multiply with the speed-ups obtained from match parallelism. The vehicle for our investigation of task-level parallelism is SPAM, a high-level vision system, implemented as a production system. SPAM is a mature research system with a typical run requiring between 50,000 to 400,000 production firings and an execution time of the order of 10 to 100 cpu hours.
rnWe report very encouraging speed-ups from task-level parallelism in SPAM --- our parallel implementation shows near linear speed-ups of over 12 fold using 14 processors and points the way to substantial (50-100 fold) speed-ups from task-level parallelism. We present a characterization of task-level parallelism in production systems and describe our methodology for selecting and applying a particular approach to parallelize SPAM. Additionally, we report the speed-ups obtained from the use of
大型生产系统(基于规则的系统)继续遭受极慢的执行速度,这限制了它们在实际应用以及研究环境中的实用性。加速这些系统的大多数研究都集中在匹配(或知识搜索)并行性上。尽管在此过程中已实现了良好的加速,但是这些调查显示了从该来源可获得的总加速的限制。这种有限的提速不足以缓解大规模生产系统实施中执行速度慢的问题。随着研究人员开发出越来越称职的生产系统,预计这种大规模系统将会增加。 P> rn
本文中,我们关注于任务级别的并行性,这是通过对生产进行高级分解而获得的。系统。从任务级并行性获得的加速将与从匹配并行性获得的加速相乘。我们研究任务级并行性的工具是SPAM,这是一种作为生产系统实施的高级视觉系统。 SPAM是一个成熟的研究系统,典型运行需要50,000至400,000次生产触发,执行时间约为10至100 cpu小时。 P> rn
我们报告说,从任务级别加快速度非常令人鼓舞SPAM中的并行性---我们的并行实现显示使用14个处理器的线性加速接近12倍,并指出了从任务级并行性大幅提高(50-100倍)加速的道路。我们介绍了生产系统中任务级并行性的特征,并描述了选择和应用特定方法并行化SPAM的方法。此外,我们报告了在此实现中通过使用
School of Computer Science, Carnegie Mellon University, Pittsburgh, Pa;
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