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@PHDTHESIS{szebenyi:2012:dissertation, author = {Szebenyi, Zolt{\'{a}}n}, title = {Capturing Parallel Performance Dynamics}, year = {2012}, school = {RWTH Aachen University}, address = {volume 12 of IAS Series, Forschungszentrum J\"ulich}, note = {{ISBN} 978-3-89336-798-6}, url = {http://hdl.handle.net/2128/4603}, abstract = {Supercomputers play a key role in countless areas of science and engineering, enabling the development of new insights and technological advances never possible before. The strategic importance and ever-growing complexity of the efficient usage of supercomputing resources makes application performance analysis invaluable for the development of parallel codes. Runtime call-path profiling is a conventional, well-known method used for collecting summary statistics of an execution such as the time spent in different call paths of the code. However, these kinds of measurements only give the user a summary overview of the entire execution, without regard to changes in performance behavior over time. The possible causes of temporal changes are quite numerous, ranging from adaptive workload balancing through periodically executed extra work or distinct computational phases to system noise. As present day scientific applications tend to be run for extended periods of time, understanding the patterns and trends in the performance data along the time axis becomes crucial.} }
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