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Title: Determination of performance characteristics of scientific applications on IBM Blue Gene/Q

Abstract

The IBM Blue Gene®/Q platform presents scientists and engineers with a rich set of hardware features such as 16 cores per chip sharing a Level 2 cache, a wide SIMD (single-instruction, multiple-data) unit, a five-dimensional torus network, and hardware support for collective operations. Especially important is the feature related to cores that have four “hardware threads,” which makes it possible to hide latencies and obtain a high fraction of the peak issue rate from each core. All of these hardware resources present unique performance-tuning opportunities on Blue Gene/Q. We provide an overview of several important applications and solvers and study them on Blue Gene/Q using performance counters and Message Passing Interface profiles. We also discuss how Blue Gene/Q tools help us understand the interaction of the application with the hardware and software layers and provide guidance for optimization. Furthermore, on the basis of our analysis, we discuss code improvement strategies targeting Blue Gene/Q. Information about how these algorithms map to the Blue Gene® architecture is expected to have an impact on future system design as we move to the exascale era.

Authors:
 [1];  [2];  [1];  [1];  [3];  [2];  [2];  [2];  [2];  [2]
  1. IBM Research Division, Cambridge, MA (United States)
  2. IBM, Yorktown Heights, NY (United States). Thomas J. Watson Research Center
  3. Univ. of Illinois, Urbana-Champaign, IL (United States). Computer Science Dept.
Publication Date:
Research Org.:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1226973
Report Number(s):
LLNL-JRNL-580932
Journal ID: ISSN 0018-8646
DOE Contract Number:  
AC52-07NA27344
Resource Type:
Journal Article
Journal Name:
IBM Journal of Research and Development
Additional Journal Information:
Journal Volume: 57; Journal Issue: 1/2; Journal ID: ISSN 0018-8646
Publisher:
IEEE
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE

Citation Formats

Evangelinos, C., Walkup, R. E., Sachdeva, V., Jordan, K. E., Gahvari, H., Chung, I. -H., Perrone, M. P., Lu, L., Liu, L. -K., and Magerlein, K. Determination of performance characteristics of scientific applications on IBM Blue Gene/Q. United States: N. p., 2013. Web. doi:10.1147/JRD.2012.2229901.
Evangelinos, C., Walkup, R. E., Sachdeva, V., Jordan, K. E., Gahvari, H., Chung, I. -H., Perrone, M. P., Lu, L., Liu, L. -K., & Magerlein, K. Determination of performance characteristics of scientific applications on IBM Blue Gene/Q. United States. doi:10.1147/JRD.2012.2229901.
Evangelinos, C., Walkup, R. E., Sachdeva, V., Jordan, K. E., Gahvari, H., Chung, I. -H., Perrone, M. P., Lu, L., Liu, L. -K., and Magerlein, K. Wed . "Determination of performance characteristics of scientific applications on IBM Blue Gene/Q". United States. doi:10.1147/JRD.2012.2229901. https://www.osti.gov/servlets/purl/1226973.
@article{osti_1226973,
title = {Determination of performance characteristics of scientific applications on IBM Blue Gene/Q},
author = {Evangelinos, C. and Walkup, R. E. and Sachdeva, V. and Jordan, K. E. and Gahvari, H. and Chung, I. -H. and Perrone, M. P. and Lu, L. and Liu, L. -K. and Magerlein, K.},
abstractNote = {The IBM Blue Gene®/Q platform presents scientists and engineers with a rich set of hardware features such as 16 cores per chip sharing a Level 2 cache, a wide SIMD (single-instruction, multiple-data) unit, a five-dimensional torus network, and hardware support for collective operations. Especially important is the feature related to cores that have four “hardware threads,” which makes it possible to hide latencies and obtain a high fraction of the peak issue rate from each core. All of these hardware resources present unique performance-tuning opportunities on Blue Gene/Q. We provide an overview of several important applications and solvers and study them on Blue Gene/Q using performance counters and Message Passing Interface profiles. We also discuss how Blue Gene/Q tools help us understand the interaction of the application with the hardware and software layers and provide guidance for optimization. Furthermore, on the basis of our analysis, we discuss code improvement strategies targeting Blue Gene/Q. Information about how these algorithms map to the Blue Gene® architecture is expected to have an impact on future system design as we move to the exascale era.},
doi = {10.1147/JRD.2012.2229901},
journal = {IBM Journal of Research and Development},
issn = {0018-8646},
number = 1/2,
volume = 57,
place = {United States},
year = {2013},
month = {2}
}