减少显式并行程序中的任务创建和终止开销

Jisheng Zhao, J. Shirako, V. K. Nandivada, Vivek Sarkar
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引用次数: 26

摘要

为了满足多核程序员的需求,任务并行编程系统出现了激增。当前的生产任务并行系统包括Cilk++、英特尔线程构建块、Java并发、。net任务并行库、OpenMP 3.0,以及当前的研究任务并行语言包括Cilk、Chapel、Fortress、X10和Habanero-Java (HJ)。程序员希望在代码中表达其算法固有的所有并行性,以实现向前可伸缩性和可移植性,但是这样做所带来的开销在当今的系统中可能非常大。在本文中,我们解决了减少由于过多的任务创建和终止而导致的程序开销总量的问题。我们引入了一个转换框架来优化任务并行程序的finish, forall和next语句。我们的方法包括消除冗余的任务创建和终止操作,以及将终止操作(finish)的强度降低到更轻量级的同步(next)。实验结果在三种平台上得到:双插槽128线程(16核)Niagara T2系统,四插槽16路Intel Xeon SMP和四插槽32路Power7 SMP。结果表明,在每个平台上,与未优化并行代码相关的最大加速分别为66.7 x、11.25 x和23.1 x,几何平均值分别为4.6 x、2.1 x和6.4×performance。本研究中的原始基准是用中等粒度的并行性编写的;对于使用细粒度并行性编写的程序,可以期望有更大的相对改进。然而,即使对于本文研究的中粒度并行基准测试,转换框架所获得的显著改进也强调了本文中介绍的编译器优化的重要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Reducing task creation and termination overhead in explicitly parallel programs
There has been a proliferation of task-parallel programming systems to address the requirements of multicore programmers. Current production task-parallel systems include Cilk++, Intel Threading Building Blocks, Java Concurrency, .Net Task Parallel Library, OpenMP 3.0, and current research task-parallel languages include Cilk, Chapel, Fortress, X10, and Habanero-Java (HJ). It is desirable for the programmer to express all the parallelism intrinsic to their algorithm in their code for forward scalability and portability, but the overhead incurred by doing so can be prohibitively large in today's systems. In this paper, we address the problem of reducing the total amount of overhead incurred by a program due to excessive task creation and termination. We introduce a transformation framework to optimize task-parallel programs with finish, forall and next statements. Our approach includes elimination of redundant task creation and termination operations as well as strength reduction of termination operations (finish) to lighter-weight synchronizations (next). Experimental results were obtained on three platforms: a dual-socket 128-thread (16-core) Niagara T2 system, a quad-socket 16-way Intel Xeon SMP and a quad-socket 32-way Power7 SMP. The results showed maximum speedup of 66.7×, 11.25× and 23.1× respectively on each platform and 4.6×, 2.1× and 6.4×performance improvements respectively in geometric mean related to non-optimized parallel codes. The original benchmarks in this study were written with medium-grained parallelism; a larger relative improvement can be expected for programs written with finer-grained parallelism. However, even for the medium-grained parallel benchmarks studied in this paper, the significant improvement obtained by the transformation framework underscores the importance of the compiler optimizations introduced in this paper.
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