Scalable Interconnection Network Models for Rapid Performance Prediction of HPC Applications, Kishwar Ahmed, Jason Liu, Stephan Eidenbenz, and Joe Zerr. In Proceedings of the 18th International Conference on High Performance Computing and Communications (HPCC 2016), December 2016. [paper] [slides]
Abstract
Performance Prediction Toolkit (PPT) is a simulator mainly developed at Los Alamos National Laboratory to facilitate rapid and accurate performance prediction of large-scale scientific applications on existing and future HPC architectures. In this paper, we present three interconnect models for performance prediction of large-scale HPC applications. They are based on interconnect topologies widely used in HPC systems: torus, dragonfly, and fat-tree. We conduct extensive validation tests of our interconnect models, in particular, using configurations of existing HPC systems. Results show that our models provide good accuracy for predicting the network behavior. We also present a performance study of a parallel computational physics application to show that our model can accurately predict the parallel behavior of large-scale applications.
Bibtex
@INPROCEEDINGS{Ahmed2016:scale-intercon,
author={K. Ahmed and J. Liu and S. Eidenbenz and J. Zerr},
booktitle={Proceedings of the IEEE 18th International Conference on High Performance Computing and Communications (HPCC)},
title={Scalable Interconnection Network Models for Rapid Performance Prediction of HPC Applications},
year={2016},
pages={1069-1078},
doi={10.1109/HPCC-SmartCity-DSS.2016.0151},
month={Dec},}