Capturing Parallel Performance Dynamics
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Beschreibung
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.
A straightforward approach is profiling every iteration of the main loop separately. As shown
by our analysis of a representative set of scientific codes, such measurements provide a wealth
of new data that often leads to invaluable new insights. However, the introduction of the time
dimension makes the amount of data collected proportional to the number of iterations, and
memory usage and file sizes grow considerably. To counter this problem, a low-overhead online
compression algorithm was developed that requires only a fraction of the memory and file
sizes needed for an uncompressed measurement. By exploiting similarities between different
iterations, the lossy compression algorithm allows all the relevant temporal patterns of the
performance behavior to be reconstructed.
While standard, direct instrumentation, which is assumed by the initial version of the compression
algorithm, results in fairly low overhead with many scientific codes, in some cases
the high frequency of events (e.g., tiny C++ member function calls) makes such measurements
impractical. To overcome this problem, a sampling-based methodology could be used instead,
where the amount of measurement overhead becomes a function of the sampling frequency,
independent of the function-call frequency. However, sampling alone is insufficient for our
purposes, as it does not provide access to the communication metrics the compression
algorithm heavily depends on. Therefore, a hybrid solution was developed that seamlessly
integrates both types of measurement techniques in a single unified measurement, using
direct instrumentation for message passing constructs, while sampling the rest of the code.
Finally, the compression algorithm was adapted to the hybrid profiling approach, avoiding the
overhead of pure direct instrumentation.
Evaluation of the above methodologies shows that our semantics-based compression algorithm
provides a very good approximation of the original data with very little measurement dilation,
while the hybrid combination of sampling and direct instrumentation fulfills its purpose
by showing the expected reduction of measurement dilation in cases unsuitable for direct
instrumentation. Beyond testing with standardized benchmark suites, the usefulness of these
techniques was demonstrated by their key role in gaining important new insights into the
performance characteristics of real-world applications
Autor*in
Péter Szebenyi Zoltán
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