Parallel Application Performance Prediction Using Analysis Based Models and HPC Simulations, Mohammad Abu Obaida, Jason Liu, Gopinath Chennupati, Nandakishore Santhi, and Stephan Eidenbenz. In Proceedings of the 2018 SIGSIM Principles of Advanced Discrete Simulation (SIGSIM-PADS’18), May 2018. [paper]
Abstract
Parallel application performance models provide valuable insight about the performance in real systems. Capable tools providing fast, accurate, and comprehensive prediction and evaluation of high-performance computing (HPC) applications and system architectures have important value. This paper presents PyPassT, an analysis based modeling framework built on static program analysis and integrated simulation of target HPC architectures. More specifically, the framework analyzes application source code written in C with OpenACC directives and transforms it into an application model describing its computation and communication behavior (including CPU and GPU workloads, memory accesses, and message-passing transactions). The application model is then executed on a simulated HPC architecture for performance analysis. Preliminary experiments demonstrate that the proposed framework can represent the runtime behavior of benchmark applications with good accuracy.
Bibtex
@inproceedings{pads18-hpcpred,
title = {Parallel Application Performance Prediction Using Analysis Based Models and HPC Simulations},
author = {Mohammad Abu Obaida and Jason Liu and Gopinath Chennupati and Nandakishore Santhi and Stephan Eidenbenz},
booktitle = {Proceedings of the 2018 SIGSIM Principles of Advanced Discrete Simulation (SIGSIM-PADS’18)},
pages = {49--59},
month = {May},
year = {2018},
doi = {10.1145/3200921.3200937}
}
Slides