Introducing Speculative Optimizations in Task Dataflow with Language Extensions and Runtime Support

Nathaniel Azuelos, Yoav Etsion, I. Keidar, A. Zaks, E. Ayguadé
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引用次数: 2

Abstract

We argue that speculation leads to increased parallelism in the coarse-grain dataflow paradigm. To do so, we present a framework for adding speculation in a popular and well-established framework. We specify a limited set of additions to the OmpSs language and changes required in its supporting runtime environment. These modifications enable speculation across the system in a flexible way. We evaluate our implementation using a simple benchmark leading to a promising 10% speedup.
在具有语言扩展和运行时支持的任务数据流中引入推测优化
我们认为,推测导致了粗粒度数据流范式中并行性的增加。为此,我们提出了一个框架,在一个流行和完善的框架中添加投机。我们指定了一组有限的对OmpSs语言的添加和对其支持的运行时环境所需的更改。这些修改使整个系统能够以灵活的方式进行推测。我们使用一个简单的基准测试来评估我们的实现,结果是有希望的10%的加速。
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