Computer Science Colloquium: Prof. Allen D. Malony

Advances in the TAU Performance System and the Need for Dynamic Performance Introspection, In Situ Analysis, and Adaptation

Abstract: Since the beginning of ``high-performance'' parallel computing, observing
and analyzing performance for purposes of finding bottlenecks and
identifying opportunities for improvement has been at the heart of
delivering the performance potential of next-generation scalable systems.
Interestingly, it is the ever-changing parallel computing landscape that is
the main driver of requirements for parallel performance technology and the
improvements necessary beyond the current state-of-the-art.  Indeed, the
development and application of our TAU Performance System over many years
largely follows an evolutionary path of addressing measurement and analysis
problems in new parallel machines and programming environments.

However, the outlook to future parallel systems with high degrees of
concurrency, heterogeneous components, dynamic runtime environments,
asynchronous execution, and power constraints suggests a new perspective
will be needed on the role of performance observation and analysis in
respect to tool technology integration and performance optimization
methods.  The reliance on post-mortem analysis of application-level
("1st person") performance measurements is prohibitive for
exascale-class machines because of the performance data volume, the
primitive basis for performance data attribution, and the fundamental
problem of performance variation that will exist.  Instead, it will be
important to provide introspection support across the exascale software
stack to understand how system ("3rd person") resources are used during
execution.  Furthermore, the opportunity to couple a global performance
introspection capability (a "performance backplane") with online
performance decision analytics inspires the concept of an autonomic
performance system that can feed back policy-based decisions to guide
the computation to better states of execution.

The talk will explore these issues by giving a brief retrospective on
performance tool evolution, setting the stage for current research
projects where a new performance perspective is being pursued.  It will
also speculate on what might be included in next-generation parallel
systems hardware, specifically to make the exascale machines more
performance-aware and dynamically-adaptive.

Speaker Bio: Allen D. Malony is a Professor in the Department of Computer and
Information Science at the University of Oregon (UO) where he directs
parallel computing research projects, notably the TAU parallel
performance system project.  He has extensive experience in performance
benchmarking and characterization of high-performance computing systems,
and has developed performance evaluation tools for a range of parallel
machines during the last 30 years.  Malony is also interested in
computational and data science.  He is the Director of the new Oregon
Advanced Computing Institute for Science and Society (OACISS) at UO.
Malony was awarded the NSF National Young Investigator award, was a
Fulbright Research Scholar to The Netherlands and Austria, and received
the Alexander von Humboldt Research Award for Senior U.S. Scientists by
the Alexander von Humboldt Foundation.  Last year he was the
Fulbright-Tocqueville Distinguished Chair to France.  Recently, he was
award the Fulbright for the Future by the Franco-Americaine Fulbright
Commission.  Malony is the CEO of ParaTools, Inc., which he founded with
Dr. Sameer Shende in 2004.  ParaTools SAS is a French company they
started in 2014, and is wholly-owned company by ParaTools, Inc.
ParaTools specializes in performance analysis and engineering, HPC
applications and optimization, and parallel software, hardware, and
tools.

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