INSIGHTFUL PERFORMANCE ANALYSIS OF MANY-TASK RUNTIMES THROUGH TOOL ...

297 Pages·2017·13.24 MB·English

INSIGHTFUL PERFORMANCE ANALYSIS OF MANY-TASK RUNTIMES
THROUGH TOOL-RUNTIME INTEGRATION
by
NICHOLAS A. CHAIMOV
A DISSERTATION
Presented to the Department of Computer and Information Science
and the Graduate School of the University of Oregon
in partial fulfillment of the requirements
for the degree of
Doctor of Philosophy
June 2017
DISSERTATION APPROVAL PAGE
Student: Nicholas A. Chaimov
Title: Insightful Performance Analysis of Many-Task Runtimes through Tool-Runtime
Integration
This dissertation has been accepted and approved in partial fulfillment of the
requirements for the Doctor of Philosophy degree in the Department of Computer and
Information Science by:
Allen D. Malony Chair
Boyana R. Norris Core Member
Hank R. Childs Core Member
Gregory Bothun Institutional Representative
and
Scott L. Pratt Dean of the Graduate School
Original approval signatures are on file with the University of Oregon Graduate
School.
Degree awarded June 2017
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© 2017 Nicholas A. Chaimov
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DISSERTATION ABSTRACT
Nicholas A. Chaimov
Doctor of Philosophy
Department of Computer and Information Science
June 2017
Title: Insightful Performance Analysis of Many-Task Runtimes through Tool-Runtime
Integration
Future supercomputers will require application developers to expose much more
parallelism than current applications expose. In order to assist application developers in
structuring their applications such that this is possible, new programming models and
libraries are emerging, the many-task runtimes, to allow for the expression of orders of
magnitude more parallelism than currently existing models.
This dissertation describes the challenges that these emerging many-task
runtimes will place on performance analysis, and proposes deep integration between
runtimes and performance tools as a means of producing correct, insightful, and
actionable performance results. I show how tool-runtime integration can be used to
aid programmer understanding of performance characteristics and to provide online
performance feedback to the runtime for Unified Parallel C (UPC), High Performance
ParalleX (HPX), Apache Spark, the Open Community Runtime, and the OpenMP
runtime.
This dissertation includes previously published co-authored material.
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CURRICULUM VITAE
NAME OF AUTHOR: Nicholas A. Chaimov
GRADUATE AND UNDERGRADUATE SCHOOLS ATTENDED:
University of Oregon, Eugene, OR
Portland State University, Portland, OR
Reed College, Portland, OR
DEGREES AWARDED:
Doctor of Philosophy, Computer and Information Science, 2017, University of
Oregon
Master of Science, Computer and Information Science, 2012, University of
Oregon
Bachelor of Science, Computer and Information Science, 2010, University of
Oregon
Bachelor of Science, Biology, 2007, University of Oregon
AREAS OF SPECIAL INTEREST:
High-Performance Computing
Scientific Computing
Performance Monitoring
PROFESSIONAL EXPERIENCE:
Graduate Research Fellow, Computer and Information Science, University of
Oregon, 2010-2017
Software Engineering Intern, Intel Federal LLC, Summer 2016
Research Assistant, Lawrence Berkeley National Lab, Summer 2015
Research Assistant, Lawrence Berkeley National Lab, Summer 2014
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GRANTS, AWARDS AND HONORS:
Gurdeep Pall Graduate Student Fellowship, University of Oregon, 2016
Student Travel Grant, High Performance Distributed Computing (HPDC),
2016
Member of 1st Place Graduate Team, Eugene Luks Programming Competition,
University of Oregon, 2015
Member of 1st Place Graduate Team, Eugene Luks Programming Competition,
University of Oregon, 2012
Member, Upsilon Pi Epsilon International Honor Society for the Computing
and Information Disciplines, 2010-Present
PUBLICATIONS:
Nicholas Chaimov, Allen Malony, Shane Canon, Costin Iancu, Khaled Z
Ibrahim, and Jay Srinivasan. “Scaling Spark on HPC Systems.” International
Symposium on High-Performance Parallel and Distributed Computing (HPDC).
2016.
Nicholas Chaimov, Allen Malony, Costin Iancu, and Khaled Ibrahim. “Scaling
Spark on Lustre.” Workshop On Performance and Scalability of Storage Systems.
2016.
Nicholas Chaimov, Allen Malony, Khaled Ibrahim, Costin Iancu, Shane
Canon, and Jay Srinivasan. “Performance Evaluation of Apache Spark on
Cray XC Systems.” Cray Users Group. 2016.
Md Abdullah Shahneous Bari, Nicholas Chaimov, Abid M Malik, Kevin
A Huck, Barbara Chapman, Allen D. Malony, Osman Sarood. “ARCS:
Adaptive Runtime Configuration Selection for Power-Constrained
OpenMP Applications.” IEEE International Conference on Cluster Computing
(CLUSTER). 2016.
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Nicholas Chaimov, Khaled Ibrahim, Sam Williams and Costin Iancu.
“Reaching Bandwidth Saturation Using Transparent Injection
Parallelization.” International Journal of High Performance Computing
Applications. 2016.
Kevin Huck, Allan Porterfield, Nicholas Chaimov, Hartmut Kaiser, Allen D.
Malony, Thomas Sterling, and Rob Fowler. “An Autonomic Performance
Environment for Exascale.” Supercomputing Frontiers and Innovation 2, no. 3
(2015): 49-66.
Robert Lim, Allen Malony, Boyana Norris, and Nicholas Chaimov.
“Identifying Optimization Opportunities Within Kernel Execution in GPU
Codes.” Euro-Par 2015: Parallel Processing Workshops, pp. 185-196. 2015.
Nicholas Chaimov, Khaled Ibrahim, Sam Williams and Costin Iancu.
“Exploiting Communication Concurrency on High Performance
Computing Systems.” International Workshop on Programming Models and
Applications for Multicores and Manycores (PMAM). 2015.
Nicholas Chaimov, Boyana Norris, and Allen D. Malony. “Toward
Multi-target Autotuning for Accelerators.” International Conference on Parallel
and Distributed Systems (ICPADS). 2014.
Nicholas Chaimov, Boyana Norris, and Allen D. Malony. “Integration and
Synthesis for Automated Performance Tuning: the SYNAPT Project.”
International Workshop on Automatic Performance Tuning (iWAPT). 2014.
Nicholas Chaimov, Scott Biersdorff, and Allen D. Malony. “Tools for
machine-learning-based empirical autotuning and specialization.”
International Journal of High Performance Computing Applications 27.4 (2013):
403-411.
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ACKNOWLEDGEMENTS
I thank my advisor, Prof. Allen Malony, for his help with research, with
identifying research areas, with securing interesting and useful internships, and, most
of all, for convincing me to pursue a PhD. I also thank Prof. Hank Childs and Prof.
Boyana Norris for their help with my research, as well as Sameer Shende and Kevin
Huck of the Performance Research Lab. I thank my collaborators at Lawrence Berkeley
National Lab: Costin Iancu, Khaled Ibrahim, and Sam Williams; and my collaborators
at Intel: Bala Seshasayee, Romain Cledat, Bryan Pawlowski, and Nick Pepperling.
Parts of this document were supported by the DOE Office of Advanced
Scientific Computing Research (contract number DE-AC02-05CH11231). Parts
of this document are based upon work supported by the Department of Energy
Office of Science under Award Number DE-SC0008717. Support for this work was
provided through the Scientific Discovery through Advanced Computing (SciDAC)
program funded by U.S. Department of Energy, Office of Science, Advanced Scientific
Computing Research (and Basic Energy Sciences/Biological and Environmental
Research/High Energy Physics/Fusion Energy Sciences/Nuclear Physics) under
award numbers DE-SC0008638, DE-SC0008704, DE-FG02-11ER26050 and
DE-SC0006925. This research used resources of the National Energy Research
Scientific Computing Center, a DOE Office of Science User Facility supported
by the Office of Science of the U.S. Department of Energy under Contract No.
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DE-AC02-05CH11231. This research used resources of the Oak Ridge Leadership
Computing Facility at the Oak Ridge National Laboratory, which is supported
by the Office of Science of the U.S. Department of Energy under Contract No.
DE-AC05-00OR22725. This work was partially supported by the Intel Parallel
Computing Center at Lawrence Berkeley National Laboratory - Lustre .
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TABLE OF CONTENTS
Chapter Page
I. INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1. Thesis Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2. Dissertation Outline . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.3. Coauthored Material . . . . . . . . . . . . . . . . . . . . . . . . . 11
II. BACKGROUND AND RELATED WORK . . . . . . . . . . . . . . . . . 12
2.1. Current Programming Models . . . . . . . . . . . . . . . . . . . . 13
2.2. Capturing Performance Data . . . . . . . . . . . . . . . . . . . . . 23
2.3. Autotuning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
2.4. Performance Modeling . . . . . . . . . . . . . . . . . . . . . . . . 37
2.5. Performance Diagnosis . . . . . . . . . . . . . . . . . . . . . . . . 43
2.6. Exascale Computing and Future Programming Models . . . . . . . . 53
2.7. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
III. ONLINE COMMUNICATIONS ADAPTATION IN UPC . . . . . . . . . 72
3.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
3.2. Maximizing Message Concurrency . . . . . . . . . . . . . . . . . . 73
3.3. Communication and Concurrency . . . . . . . . . . . . . . . . . . 76
3.4. Runtime Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
3.5. Network Performance and Saturation . . . . . . . . . . . . . . . . . 85
3.6. Parallelizing Injection in Applications . . . . . . . . . . . . . . . . . 95
3.7. Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
3.8. Other Related Work . . . . . . . . . . . . . . . . . . . . . . . . . 107
3.9. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
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