A comparative study of parallel programming frameworks for distributed GPU applications

Ruidong Gu, M. Becchi
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引用次数: 6

Abstract

Parallel programming frameworks such as MPI, OpenSHMEM, Charm++ and Legion have been widely used in many scientific domains (from bioinformatics, to computational physics, chemistry, among others) to implement distributed applications. While they have the same purpose, these frameworks differ in terms of programmability, performance, and scalability under different applications and cluster types. Hence, it is important for programmers to select the programming framework that is best suited to the characteristics of their application types (i.e. its computation and communication patterns) and the hardware setup of the target high-performance computing cluster. In this work, we consider several popular parallel programming frameworks for distributed applications. We first analyze their memory model, execution model, synchronization model and GPU support. We then compare their programmability, performance, scalability, and load-balancing capability on homogeneous computing cluster equipped with GPUs.
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分布式GPU应用并行编程框架的比较研究
并行编程框架,如MPI、OpenSHMEM、Charm++和Legion,已经广泛应用于许多科学领域(从生物信息学到计算物理、化学等)来实现分布式应用程序。虽然它们具有相同的目的,但在不同的应用程序和集群类型下,这些框架在可编程性、性能和可伸缩性方面有所不同。因此,对于程序员来说,选择最适合其应用程序类型特征(即其计算和通信模式)和目标高性能计算集群的硬件设置的编程框架是很重要的。在这项工作中,我们考虑了几个流行的分布式应用程序并行编程框架。我们首先分析了它们的内存模型、执行模型、同步模型和GPU支持。然后,我们比较了它们在配备gpu的同构计算集群上的可编程性、性能、可伸缩性和负载平衡能力。
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