Abstract
With the increase of power and reduction of cost of GPU accelerated processors a corresponding interest in their uses in the scientific domain has spurred. OSG users are no different and they have shown an interest in accessing GPU resourcesvia their usual workload infrastructures. Grid sites that have these kinds of resources also want to make them grid available. In this talk, we discuss the software and infrastructure challenges and limitations of the OSG implementations to make GPU’s widely accessible over the grid. Two use cases are considered for this. First: IceCube, a big VO with a well-curated software stack taking advantage of GPUs with OpenCL. Second, a more general approach to supporting the grid use of industry and academia maintained machine learning libraries like Tensorflow, and Keras on the grid using Singularity.
Highlights
In the recent years three trends have sparked the sudden increase of Graphic Processing Unit (GPU) accelerators use in the scientific community
First the cost of GPU has constantly decreased, second there is an increasing availability of data and third a proliferation of third party (Industry and Academia outside physics) Deep Learning libraries that can greatly profit from the parallel computing power of GPU while hiding from the scientist the interactions with the GPU
This demand has led to computing sites to add GPU enabled resources to their clusters and to more scientific communities or Virtual Organizations (VO) to want to seamlessly access these resources as they do with any other computing resource available
Summary
In the recent years three trends have sparked the sudden increase of Graphic Processing Unit (GPU) accelerators use in the scientific community. First the cost of GPU has constantly decreased, second there is an increasing availability of data (either simulated or experimental) and third a proliferation of third party (Industry and Academia outside physics) Deep Learning libraries that can greatly profit from the parallel computing power of GPU while hiding from the scientist the interactions with the GPU. This demand has led to computing sites to add GPU enabled resources to their clusters and to more scientific communities or Virtual Organizations (VO) to want to seamlessly access these resources as they do with any other computing resource available.
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