Accelerated serverless computing based on GPU virtualization

Diana M. Naranjo, Sebastián Risco, Carlos de Alfonso, Alfonso Pérez, Ignacio Blanquer, and Germán Moltó. Accelerated serverless computing based on GPU virtualization. Journal of Parallel and Distributed Computing, 139:32–42, 5 2020.

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Abstract

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.

BibTeX Entry

@article{Diana2020asc,
   abstract = {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.},
   author = {Diana M. Naranjo and Sebastián Risco and Carlos de Alfonso and Alfonso Pérez and Ignacio Blanquer and Germán Moltó},
   doi = {10.1016/j.jpdc.2020.01.004},
   issn = {07437315},
   journal = {Journal of Parallel and Distributed Computing},
   keywords = {GPU Virtualization,GPUs,Serverless Computing},
   month = {5},
   pages = {32-42},
   title = {Accelerated serverless computing based on GPU virtualization},
   volume = {139},
   url = {https://linkinghub.elsevier.com/retrieve/pii/S0743731519303533},
   year = {2020}
}