Accelerated serverless computing based on GPU virtualization
Abstract
[EN] 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. ; The work presented in this article has been partially funded by a research grant from the regional government of the Comunitat Valenciana (Spain), co-funded by the European Union ERDF funds (European Regional Development Fund) of the Comunitat Valenciana 2014-2020, with reference IDIFEDER/2018/032 (High-Performance Algorithms for the Modeling, Simulation and early Detection of diseases in Personalized Medicine). The authors would also like to thank the Spanish "Ministerio de Economia, Industria y Competitividad" for the project "BigCLOE" with reference number TIN2016-79951-R and the project ATMOSPHERE, funded jointly by the European Commission under the Cooperation Programme, Horizon 2020 grant agreement No 777154 and the Brazilian Ministerio de Ciencia, Tecnologia e Inovacao (MCI-I), number 51119. D.M.N would like to thank the "Generalitat Valenciana, Spain" for the grant GrisoliaP/2017/071. ; Naranjo-Delgado, DM.; Risco, S.; ...
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