Scalable performance measurement infrastructure for parallel codes (Score-P)
Creators
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Feld, Christian
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Jäkel, René
- Lorenz, Daniel
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Wesarg, Bert
- Schmidl, Dirk
- Tschüter, Ronny
- Oleynik, Yury
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Wagner, Michael
- Eschweiler, Dominic
- Spazier, Johannes
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Knüpfer, Andreas
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Shende, Sameer
- Millstein, Suzanne
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Biersdorff, Scott
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Geimer, Markus
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Schlütter, Marc
- Schmitt, Felix
- Ziegenbalg, Johannes
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Zhukov, Ilya
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Dietrich, Robert
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Geyer, Robin
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Saviankou, Pavel
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Knobloch, Michael
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Mijaković, Robert
- Schöne, Robert
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Winkler, Frank
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Ilsche, Thomas
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Hermanns, Marc-André
- Brendel, Ronny
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Oeste, Sebastian
- Herold, Christian
- Sigl, Severin
- Hilbrich, Tobias
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Williams, Bill
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Klotz, Sven
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Corbin, Gregor
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Reuter, Jan André
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Grund, Alexander
- Sander, Maximilian
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Frenzel, Jan
Description
The instrumentation and measurement framework Score-P, together with analysis tools build on top of its output formats, provides insight into massively parallel HPC applications, their communication, synchronization, I/O, and scaling behaviour to pinpoint performance bottlenecks and their causes.
Score-P is a highly scalable and easy-to-use tool suite for profiling (summarizing program execution) and event tracing (capturing events in chronological order) of HPC applications.
The scorep instrumentation command adds instrumentation hooks into a user's application by either prepending or replacing the compile and link commands. C, C++, Fortran, and Python codes as well as contemporary HPC programming models (MPI, threading, GPUs, I/O) are supported.
When running an instrumented application, measurement event data is provided by the instrumentation hooks to the measurement core. There, the events are augmented with high-accuracy timestamps and potentially hardware counters (a plugin-API allows querying additional metric sources). The augmented events are then passed to one or both of the built-in event consumers, profiling and tracing (a plugin-API allows creation of additional event consumers) which finally provide output in the formats CUBE4 and OTF2, respectively. These open and backwards-compatible output formats can be consumed by established analysis tools, e.g., like
- CubeGUI, the performance report explorer for Scalasca and Score-P, a generic tool for displaying a multidimensional performance space,
- Extra-P, an automatic performance-modelling tool that supports the user in the identification of scalability bugs,
- TAU's ParaProf, a portable, scalable performance analysis tool, and PerfExplorer, a framework for parallel performance data mining and knowledge discovery,
- Scalasca Trace Tools, a collection of trace-based performance analysis tools that have been specifically designed for use on large-scale systems featuring hundreds of thousands of CPU cores, automatically identifying potential communication and synchronization bottlenecks and offering guidance in exploring their causes, and
- Vampir, a trace-based framework that enables users to quickly display and analyse arbitrary program behaviour.
Score-P is available under the 3-clause BSD Open Source license.
Version 8.4 is a bugfix release for version 8.0. For features/changes/improvements introduced in the latest version, please see the Changelog file.
Files
Files
(20.8 MB)
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