This paper introduces a platform to support serverless computing for scalable event-driven data processing that features a multi-level elasticity approach combined with virtualization of GPUs. The platform supports the execution of applications based on Docker containers in response to file uploads to a data storage in order to perform the data processing in parallel. This is managed by an elastic Kubernetes cluster whose size automatically grows and shrinks depending on the number of files to be processed. To accelerate the processing time of each file, several approaches involving virtualized access to GPUs, either locally or remote, have been evaluated. A use case that involves the inference based on deep learning techniques on transtoracic echocardiography imaging has been carried out to assess the benefits and limitations of the platform. The results indicate that the combination of serverless computing and GPU virtualization introduce an efficient and cost-effective event-driven accelerated computing approach that can be applied for a wide variety of scientific applications.

本文介绍了一个支持无服务器计算的平台，用于可扩展的事件驱动的数据处理，该平台具有多级弹性方法和GPU虚拟化功能。该平台支持基于Docker容器的应用程序的执行，以响应文件上传到数据存储，以便并行执行数据处理。这由一个弹性的Kubernetes集群管理，该集群的大小会根据要处理的文件数自动增加和缩小。为了加快每个文件的处理时间，已经评估了几种涉及虚拟访问GPU（本地或远程）的方法。已经进行了一个使用案例，该案例涉及基于经耳超声心动图成像的深度学习技术的推论，以评估该平台的优势和局限性。