GPU Enabled Serverless Computing Framework

T. Jun, Daeyoun Kang, Dohyeun Kim, Daeyoung Kim
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引用次数: 25

Abstract

A new form of cloud computing, serverless computing, is drawing attention as a new way to design micro-services architectures. In a serverless computing environment, services are developed as service functional units. The function development environment of all serverless computing framework at present is CPU based. In this paper, we propose a GPU-supported serverless computing framework that can deploy services faster than existing serverless computing framework using CPU. Our core approach is to integrate the open source serverless computing framework with NVIDIA-Docker and deploy services based on the GPU support container. We have developed an API that connects the open source framework to the NVIDIA-Docker and commands that enable GPU programming. In our experiments, we measured the performance of the framework in various environments. As a result, developers who want to develop services through the framework can deploy high-performance micro services and developers who want to run deep learning programs without a GPU environment can run code on remote GPUs with little performance degradation.
支持GPU的无服务器计算框架
一种新的云计算形式,无服务器计算,作为一种设计微服务架构的新方式,正引起人们的注意。在无服务器计算环境中,服务是作为服务功能单元开发的。目前所有无服务器计算框架的功能开发环境都是基于CPU的。在本文中,我们提出了一个gpu支持的无服务器计算框架,它可以比现有的使用CPU的无服务器计算框架更快地部署服务。我们的核心方法是将开源无服务器计算框架与NVIDIA-Docker集成,并基于GPU支持容器部署服务。我们已经开发了一个API,将开源框架连接到NVIDIA-Docker和支持GPU编程的命令。在我们的实验中,我们测量了框架在各种环境中的性能。因此,希望通过框架开发服务的开发人员可以部署高性能的微服务,而希望在没有GPU环境的情况下运行深度学习程序的开发人员可以在远程GPU上运行代码,而性能几乎没有下降。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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