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Title: ExaSAT: An exascale co-design tool for performance modeling

Abstract

One of the emerging challenges to designing HPC systems is understanding and projecting the requirements of exascale applications. In order to determine the performance consequences of different hardware designs, analytic models are essential because they can provide fast feedback to the co-design centers and chip designers without costly simulations. However, current attempts to analytically model program performance typically rely on the user manually specifying a performance model. Here we introduce the ExaSAT framework that automates the extraction of parameterized performance models directly from source code using compiler analysis. The parameterized analytic model enables quantitative evaluation of a broad range of hardware design trade-offs and software optimizations on a variety of different performance metrics, with a primary focus on data movement as a metric. Finally, we demonstrate the ExaSAT framework’s ability to perform deep code analysis of a proxy application from the Department of Energy Combustion Co-design Center to illustrate its value to the exascale co-design process. ExaSAT analysis provides insights into the hardware and software trade-offs and lays the groundwork for exploring a more targeted set of design points using cycle-accurate architectural simulators.

Authors:
 [1];  [1];  [1];  [1];  [1];  [1];  [1]
  1. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Computational Research Division
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
OSTI Identifier:
1407281
Grant/Contract Number:  
AC02-05CH11231
Resource Type:
Accepted Manuscript
Journal Name:
International Journal of High Performance Computing Applications
Additional Journal Information:
Journal Volume: 29; Journal Issue: 2; Journal ID: ISSN 1094-3420
Publisher:
SAGE
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING

Citation Formats

Unat, Didem, Chan, Cy, Zhang, Weiqun, Williams, Samuel, Bachan, John, Bell, John, and Shalf, John. ExaSAT: An exascale co-design tool for performance modeling. United States: N. p., 2015. Web. doi:10.1177/1094342014568690.
Unat, Didem, Chan, Cy, Zhang, Weiqun, Williams, Samuel, Bachan, John, Bell, John, & Shalf, John. ExaSAT: An exascale co-design tool for performance modeling. United States. doi:10.1177/1094342014568690.
Unat, Didem, Chan, Cy, Zhang, Weiqun, Williams, Samuel, Bachan, John, Bell, John, and Shalf, John. Mon . "ExaSAT: An exascale co-design tool for performance modeling". United States. doi:10.1177/1094342014568690. https://www.osti.gov/servlets/purl/1407281.
@article{osti_1407281,
title = {ExaSAT: An exascale co-design tool for performance modeling},
author = {Unat, Didem and Chan, Cy and Zhang, Weiqun and Williams, Samuel and Bachan, John and Bell, John and Shalf, John},
abstractNote = {One of the emerging challenges to designing HPC systems is understanding and projecting the requirements of exascale applications. In order to determine the performance consequences of different hardware designs, analytic models are essential because they can provide fast feedback to the co-design centers and chip designers without costly simulations. However, current attempts to analytically model program performance typically rely on the user manually specifying a performance model. Here we introduce the ExaSAT framework that automates the extraction of parameterized performance models directly from source code using compiler analysis. The parameterized analytic model enables quantitative evaluation of a broad range of hardware design trade-offs and software optimizations on a variety of different performance metrics, with a primary focus on data movement as a metric. Finally, we demonstrate the ExaSAT framework’s ability to perform deep code analysis of a proxy application from the Department of Energy Combustion Co-design Center to illustrate its value to the exascale co-design process. ExaSAT analysis provides insights into the hardware and software trade-offs and lays the groundwork for exploring a more targeted set of design points using cycle-accurate architectural simulators.},
doi = {10.1177/1094342014568690},
journal = {International Journal of High Performance Computing Applications},
number = 2,
volume = 29,
place = {United States},
year = {2015},
month = {2}
}

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