OSG and GPUs: A tale of two use cases

¹ University of California San Diego, 9500 Gilman Drive, La Jolla, CA,92093
² Information Science Institute, 4676 Admiralty Way #1001, Marina Del Rey , CA,90292
³ University of Nebraska – Lincoln, 1400 R StreetLincoln, Lincoln, NE 68588
⁴ niversity of Wisconsin-Madison, WIPAC, 222 W Washington Ave. Suite 500, Madison WI 53703

^* e-mail: emfajard@ucsd.edu
^** e-mail: rynge@isi.edu
^*** e-mail: dweitzel@unl.edu

Published online: 17 September 2019

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.