改进OpenMP中任务嵌套和依赖关系的集成

Josep M. Pérez, Vicencc Beltran, Jesús Labarta, E. Ayguadé
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引用次数: 34

摘要

OpenMP 4.0的任务模型既支持嵌套,也支持定义兄弟任务之间的依赖关系。将许多代码与任务并行化的一种自然方法是,首先为高级函数指定任务,然后用额外的子任务进一步细化这些任务。然而,这种自顶向下的方法有一些缺点,因为将嵌套与依赖相结合通常需要额外的措施来强制跨嵌套级别的依赖的正确协调。例如,大多数非叶子任务需要在其代码末尾包含一个任务等待。虽然这些措施强制执行正确的顺序,但作为副作用,它们也限制了并行性的发现。在本文中,我们扩展了OpenMP任务模型,以提高嵌套和依赖关系的集成。我们的建议建立在公式、嵌套和依赖关系的基础上,并从它们各自的优势中获益。一方面,它鼓励采用自顶向下的方法来并行化代码,这种方法也支持任务的并行实例化。另一方面,它允许运行时以精细的粒度控制依赖关系,而到目前为止,这只能使用单个依赖域。我们的建议是通过增加OpenMP任务指令来实现的,以确保与当前代码的向后兼容性。我们已经用这些扩展实现了一个新的运行时,并用它来评估对几个基准测试的影响。我们的初步发现表明,我们的扩展在三个方面提高了性能。首先,它们暴露了更多的并行性。其次,它们揭示了跨嵌套级别的依赖关系,这允许运行时做出更好的调度决策。第三,它们允许并行实例化具有它们之间依赖关系的任务。
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Improving the Integration of Task Nesting and Dependencies in OpenMP
The tasking model of OpenMP 4.0 supports both nesting and the definition of dependences between sibling tasks. A natural way to parallelize many codes with tasks is to first taskify the high-level functions and then to further refine these tasks with additional subtasks. However, this top-down approach has some drawbacks since combining nesting with dependencies usually requires additional measures to enforce the correct coordination of dependencies across nesting levels. For instance, most non-leaf tasks need to include a taskwait at the end of their code. While these measures enforce the correct order of execution, as a side effect, they also limit the discovery of parallelism. In this paper we extend the OpenMP tasking model to improve the integration of nesting and dependencies. Our proposal builds on both formulas, nesting and dependencies, and benefits from their individual strengths. On one hand, it encourages a top-down approach to parallelizing codes that also enables the parallel instantiation of tasks. On the other hand, it allows the runtime to control dependencies at a fine grain that until now was only possible using a single domain of dependencies. Our proposal is realized through additions to the OpenMP task directive that ensure backward compatibility with current codes. We have implemented a new runtime with these extensions and used it to evaluate the impact on several benchmarks. Our initial findings show that our extensions improve performance in three areas. First, they expose more parallelism. Second, they uncover dependencies across nesting levels, which allows the runtime to make better scheduling decisions. And third, they allow the parallel instantiation of tasks with dependencies between them.
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