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Task Granularity Analysis Method Using Static Metrics of Source Code for C Programs

机译:C源程序静态度量的任务粒度分析方法

摘要

In the first phase of our automatic parallelizing translator for C program, a source code is decomposed into a set of tasks of the granularity of a statement level at the minimum. In the next phase, task scheduling which determines statically by which processor these tasks are processed is performed. Since this task scheduling is a combinatorial optimization problem, it is important for it to suppress the number of tasks which constitutes the program. Therefore, useless parallelism is removed using the information about the dependencies among tasks and task cost, and the task granularity analysis is required in order to make task granularity reasonable. However, since it is necessary to analyze the processing time of a task only using the information acquired from a source code, exact cost may be unable to be assigned in the time analysis using the conventional computational complexity analysis. So, in this paper, the method of aiming at the improvement in accuracy of execution time analysis is proposed by applying static metrics of source code.
机译:在我们用于C程序的自动并行翻译器的第一阶段,源代码被分解为一组最小语句级粒度的任务。在下一阶段,执行任务调度,该任务调度将静态确定由哪些处理器处理这些任务。由于此任务调度是组合优化问题,因此,重要的是抑制构成程序的任务数量。因此,使用有关任务之间的依赖关系和任务成本的信息来删除无用的并行性,并且需要进行任务粒度分析以使任务粒度合理。但是,由于仅使用从源代码获取的信息来分析任务的处理时间是必要的,因此在使用传统的计算复杂度分析的时间分析中可能无法分配准确的成本。因此,本文提出了一种通过应用源代码的静态指标来提高执行时间分析的准确性的方法。

著录项

  • 作者

    小林 裕昌; 甲斐 宗徳;

  • 作者单位
  • 年度 2014
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  • 原文格式 PDF
  • 正文语种 JPN
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