Parallelizing Air Shower Simulation for Background Characterization in IceCube

TL;DR

This paper introduces a parallelized simulation framework for IceCube's air shower background modeling, significantly improving computational efficiency and resource utilization for neutrino background characterization.

Contribution

It presents a novel client-server parallelization approach that enhances GPU utilization and speeds up air shower simulations by a factor of 20.

Findings

GPU utilization exceeds 90%

Simulation speed improved by a factor of 20

More predictable memory usage

Abstract

The IceCube Neutrino Observatory is a cubic kilometer neutrino telescope located at the Geographic South Pole. For every observed neutrino event, there are over $10^6$ background events caused by cosmic ray air shower muons. In order to properly separate signal from background, it is necessary to produce Monte Carlo simulations of these air showers. Although to-date, IceCube has produced large quantities of background simulation, these studies still remain statistics limited. The first stage of simulation requires heavy CPU usage while the second stage requires heavy GPU usage. Processing both of these stages on the same node will result in an underutilized GPU but using different nodes will encounter bandwidth bottlenecks. Furthermore, due to the power-law energy spectrum of cosmic rays, the memory footprint of the detector response often exceeded the limit in unpredictable ways. This proceeding presents new client-server code which parallelizes the first stage onto multiple CPUs on the same node and then passes it on to the GPU for photon propagation. This results in GPU utilization of greater than 90% as well as more predictable memory usage and an overall factor of 20 improvement in speed over previous techniques.