Techniques in Scalable and Effective Parallel Performance Analysis
Thesis 2009
Publication Type: PhD Thesis
Repository URL: 200911_ThesisCheeWai
Abstract
Performance analysis tools are essential to the maintenance of
efficient parallel execution of scientific applications. As
scientific applications are executed on larger and larger parallel
supercomputers, it is clear that performance tools must employ more
advanced techniques to keep up with the increasing data volume and
complexity of the performance information generated by these
applications as a result of scaling. In this thesis, we investigate
the useful techniques in four main thrusts to address various
aspects of this problem. First, we study how some traditional
performance analysis idioms can break down in the face of data from
large processor counts and demonstrate techniques and tools that
restore scalability. Second, we investigate how the volume of
performance data generated can be reduced while keeping the
captured information relevant for analysis and performance problem
detection. Third, we investigate the powerful new performance
analysis idioms enabled by live access to performance information
streams from a running parallel application. Fourth, we demonstrate
how repeated performance hypothesis testing can be conducted, via
simulation techniques, scalably and with significantly reduced
resource consumption. In addition, we explore the benefits of
performance tool integration to the propagation and synergy of
scalable performance analysis techniques in different tools.
TextRef
Chee Wai Lee, "Techniques in Scalable and Effective Parallel Performance Analysis", PhD Thesis, Department of Computer Science, University of Illinois at Urbana-Champaign, December 2009.
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