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Exploiting controlled-grained parallelism in message-driven stream programs

机译：在消息驱动的流程序中利用可控粒度并行性

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With the increasing amount of parallelism obtainable on multicore platforms, stream programming has been proposed as an effective solution for exposing distributed parallelization. Nonetheless, a pressing demand of scheduling task and data parallelism in stream programming exists that can accomplish robust multicore performance in the face of varying application characteristics. This paper addresses the problem of scheduling task and data parallelism in stream programming. We present StreamMDE, an asynchronous concurrency stream programming framework which offers a novel parallel programming model for scheduling task and data parallelism in the message-driven execution paradigm. A key property of this framework is exposing controlled-grained parallelism, which allows us to control the granularity of task and data parallelism in stream graph. Our empirical evaluation of StreamMDE shows that higher efficiency of mixed task and data parallelism in stream programming can be exploited with the appropriate granularity control. The framework bridges the gap between the parallel scale and the architecture of stream programs and facilitates in designing and coding stream features in different schedules.
机译：随着在多核平台上可获得的并行性的增加，已经提出了流编程作为公开分布式并行化的有效解决方案。但是，在流编程中迫切需要调度任务和数据并行性，以面对变化的应用程序特性可以实现强大的多核性能。本文解决了流编程中调度任务和数据并行性的问题。我们提出了StreamMDE，这是一个异步并发流编程框架，它提供了一种新颖的并行编程模型，用于在消息驱动的执行范例中调度任务和数据并行性。该框架的一个关键特性是公开可控粒度的并行性，这使我们能够控制流图中任务和数据并行性的粒度。我们对StreamMDE的经验评估表明，通过适当的粒度控制，可以在流编程中提高混合任务的效率和数据并行性。该框架弥合了并行规模和流程序架构之间的鸿沟，并有助于设计和编码不同时间表中的流功能。

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