IMCSim: Parameterized Performance Prediction for Implicit Monte Carlo Codes, Gopinath Chennupathi, Stephan Eidenbenz, Alex Long, Olena Tkachenko, Joseph Zerr, and Jason Liu. In Proceedings of the 2018 Winter Simulation Conference (WSC 2018), December 2018. (To appear).
Abstract
Monte Carlo techniques to radiation transport play a significant role in modeling complex astrophysical phenomena. In this paper, we design an application model (IMCSim) of an Implicit Monte Carlo (IMC) particle code using the Performance Prediction Toolkit (PPT), a discrete-event simulation-based modeling framework for predicting code performance on a large range of parallel platforms. We present validation results for IMCSim. We then use the fast parameter scanning that such a high-level loop-structure model of a complex code enables to predict optimal IMC parameter settings for interconnect latency hiding. We find that variations in interconnect bandwidth have a significant effect on optimal parameter values. Our results suggest potential value using IMCSim as a pre-step to substantial IMC runs to quickly identify optimal parameter values for the specific hardware platform on which IMC runs.
Bibtex
@inproceedings{imcsim,
title = {IMCSim: Parameterized Performance Prediction for Implicit Monte Carlo Codes},
author = {Chennupathi, Gopinath and Eidenbenz, Stephan and Long, Alex and Tkachenko, Olena and Zerr, Joseph and Liu, Jason},
booktitle = {Proceedings of the 2018 Winter Simulation Conference (WSC 2018)},
month = {December},
year = {2018}
}