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As we approach the Exascale era, it is important to verify that the existingframeworks and tools will still work at that scale. Moreover, public Cloudcomputing has been emerging as a viable solution for both prototyping andurgent computing. Using the elasticity of the Cloud, we have thus put in placea pre-exascale HTCondor setup for running a scientific simulation in the Cloud,with the chosen application being IceCube's photon propagation simulation. I.e.this was not a purely demonstration run, but it was also used to producevaluable and much needed scientific results for the IceCube collaboration. Inorder to reach the desired scale, we aggregated GPU resources across 8 GPUmodels from many geographic regions across Amazon Web Services, MicrosoftAzure, and the Google Cloud Platform. Using this setup, we reached a peak ofover 51k GPUs corresponding to almost 380 PFLOP32s, for a total integratedcompute of about 100k GPU hours. In this paper we provide the description ofthe setup, the problems that were discovered and overcome, as well as a shortdescription of the actual science output of the exercise.

Running a Pre-Exascale, Geographically Distributed, Multi-Cloud Scientific Simulation