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Towards extending the SWITCH platform for time-critical, cloud-based CUDA applications: Job scheduling parameters influencing performance

机译：面向时间紧迫的基于云的CUDA应用程序扩展SWITCH平台：影响性能的作业调度参数

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摘要

SWITCH (Software Workbench for Interactive, Time Critical and Highly self-adaptive cloud applications) allows for the development and deployment of real-time applications in the cloud, but it does not yet support instances backed by Graphics Processing Units (GPUs). Wanting to explore how SWITCH might support CUDA (a GPU architecture) in the future, we have undertaken a review of time-critical CUDA applications, discovering that run-time requirements (which we call 'wall time') are in many cases regarded as the most important. We have performed experiments to investigate which parameters have the greatest impact on wall time when running multiple Amazon Web Services GPU-backed instances. Although a maximum of 8 single-GPU instances can be launched in a single Amazon Region, launching just 2 instances rather than 1 gives a 42% decrease in wall time. Also, instances are often wasted doing nothing, and there is a moderately-strong relationship between how problems are distributed across instances and wall time. These findings can be used to enhance the SWITCH provision for specifying Non-Functional Requirements (NFRs); in the future, GPU-backed instances could be supported. These findings can also be used more generally, to optimise the balance between the computational resources needed and the resulting wall time to obtain results. (C) 2019 Elsevier B.V. All rights reserved.
机译：SWITCH（用于交互式，时间紧迫和高度自适应的云应用程序的软件工作台）允许在云中开发和部署实时应用程序，但它尚不支持由图形处理单元（GPU）支持的实例。为了探讨SWITCH将来如何支持CUDA（GPU架构），我们对时间紧迫的CUDA应用程序进行了审查，发现在许多情况下运行时间要求（我们称为“挂墙时间”）被认为是最重要的。我们已经进行了实验，以研究在运行多个Amazon Web Services GPU支持的实例时，哪些参数对挂墙时间影响最大​​。尽管在单个Amazon Region中最多可以启动8个单GPU实例，但仅启动2个实例而不是1个实例，可以节省42％的挂墙时间。此外，实例通常被浪费，无所事事，问题在实例之间的分布方式与实际时间之间存在中等程度的关系。这些发现可用于增强SWITCH对指定非功能需求（NFR）的规定；将来，将支持GPU支持的实例。这些发现也可以更普遍地用于优化所需的计算资源和所需的生成时间之间的平衡。 （C）2019 Elsevier B.V.保留所有权利。

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