PaRSEC : A programming paradigm exploiting heterogeneity for enhancing scalability
PaRSEC : A programming paradigm exploiting heterogeneity for enhancing scalability
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PaRSEC：一种利用异构性来增强可扩展性的编程范例
DOI：
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发表时间：
2013
期刊：
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通讯作者：
J. Dongarra
中科院分区：
文献类型：
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作者：
G. Bosilca;A. Bouteiller;Anthony Danalis;Mathieu Faverge;T. Hérault;J. Dongarra
新的 HPC 系统设计具有急剧增加的处理器和核心数量、不断增长的异构性和加速器以及越来越不可预测的内存访问时间，需要一种或多种全新的编程范例。这些新方法必须对意外的争用和延迟做出快速反应和适应，并且必须为执行环境提供足够的智能和灵活性，以重新安排执行以提高资源利用率。该领域的一些候选者已经开始出现。在这里，我们提出了一种基于任务并行性的方法，该方法通过将其算法表示为任务流（其间具有数据依赖性）来揭示应用程序的并行性。这种策略允许算法与数据分布和底层硬件解耦，因为算法完全表达为数据流。这种分层提供了架构、算法和数据分布之间关注点的清晰分离。开发人员可以从这种分离中受益，因为他们可以只专注于算法级别，而不受当前和未来硬件趋势编程的限制。
New HPC system designs with steeply escalating processor and core counts, burgeoning heterogeneity and accelerators, and increasingly unpredictable memory access times, call for one or more dramatically new programming paradigms. These new approaches must react and adapt quickly to unexpected contentions and delays, and they must provide the execution environment with sufficient intelligence and flexibility to rearrange the execution to improve the resource utilization. Some candidates in this area have already begun to emerge. Here we present an approach based on task parallelism, one which reveals the application’s parallelism by expressing its algorithm as a task flow, with data dependencies in-between. This strategy allows the algorithm to be decoupled from the data distribution and the underlying hardware, since the algorithm is entirely expressed as flows of data. This kind of layering provides a clear separation of concerns among architecture, algorithm, and data distribution. Developers benefit from this separation because they can focus solely on the algorithmic level without the constraints involved with programming for current and future hardware trends.