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Machine independent AND and OR parallel execution of logic programs. II. Compiled execution

机译:与机器无关的逻辑程序的AND和OR并行执行。二。编译执行

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For pt.I. see ibid., p. 170-80. In pt.I, we presented a binding environment for the AND and OR parallel execution of logic programs. This environment was instrumental in rendering a compiler for the AND and OR parallel execution of logic programs machine independent. In this paper, we describe a compiler based on the Reduce-OR process model (ROPM) for the parallel execution of Prolog programs, and provide performance of the compiler on five parallel machines: the Encore Multimax, the Sequent Symmetry, the NCUBE 2, the Intel i860 hypercube and a network of Sun workstations. The compiler is part of a machine independent parallel Prolog development system built on top of a run time environment for parallel programming called the Chare kernel, and runs unchanged on these multiprocessors. In keeping with the objectives behind the ROPM, the compiler supports both on and independent AND parallelism in Prolog programs and is suitable for execution on both shared and nonshared memory machines. We discuss the performance of the Prolog compiler in some detail and describe how grain size can be used to deliver performance that is within 10% of the underlying sequential Prolog compiler on one processor, and scale linearly with increasing number of processors on problems exhibiting sufficient parallelism. The loose coupling between parallel and sequential components makes it possible to use the best available sequential compiler as the sequential component of our compiler.
机译:对于第一点参见同上,p。 170-80。在第一篇中,我们介绍了用于逻辑程序的AND和OR并行执行的绑定环境。该环境有助于渲染独立于逻辑程序的AND和OR并行执行的编译器。在本文中,我们描述了一种基于Reduce-OR流程模型(ROPM)的编译器,用于并行执行Prolog程序,并在五台并行计算机上提供编译器的性能:Encore Multimax,Sequent Symmetry,NCUBE 2,英特尔i860超立方体和Sun工作站网络。该编译器是与机器无关的并行Prolog开发系统的一部分,该系统基于称为Chare内核的并行编程的运行时环境而建立,并且在这些多处理器上保持不变。为了符合ROPM的目标,编译器支持Prolog程序中的on和独立AND并行性,并且适合在共享和非共享内存计算机上执行。我们将详细讨论Prolog编译器的性能,并描述如何使用粒度来在一个处理器上提供基本顺序Prolog编译器的10%以内的性能,并在出现足够并行性的问题上随着处理器数量的增加而线性扩展。并行组件和顺序组件之间的松散耦合使得可以使用最佳可用顺序编译器作为编译器的顺序组件。

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