Scalasca

Scalasca
Developer(s)Forschungszentrum Jülich and Technische Universität Darmstadt
Written inC, C++
Operating systemUnix-like
PlatformIA-32, x64, ARM, PowerPC
TypeProfiling
LicenseBSD
Websitewww.scalasca.org

Scalasca is a free and open-source software for measurement, analysis, and optimization of parallel program performance.[1] It is licensed under the BSD-style license.[2]

Scalasca is mostly used for profiling scientific and engineering applications using OpenMP and/or MPI. It supports runtime analysis on supercomputers.[3][4] The application being analysed needs first of all to be "instrumented": MPI usage is instrumented simply by linking the application to the measuring library, while OpenMP usage is instrumented by recompiling from source using Scalasca's modified compiler.[5][6]

References

  1. ^ Geimer, Markus; et al. (25 April 2010). "The Scalasca performance toolset architecture". Concurrency and Computation: Practice and Experience. 22 (6): 702–719. CiteSeerX 10.1.1.183.3213. doi:10.1002/cpe.1556. S2CID 14248376. Retrieved 29 June 2016.
  2. ^ "About". www.scalasca.org. Retrieved 2020-11-14.
  3. ^ Knüpfer, Andreas; Rössel, Christian; Mey, Dieter an; Biersdorff, Scott; Diethelm, Kai; Eschweiler, Dominic; Geimer, Markus; Gerndt, Michael; Lorenz, Daniel (2012). "Score-P: A Joint Performance Measurement Run-Time Infrastructure for Periscope, Scalasca, TAU, and Vampir" (PDF). In Brunst, Holger; Müller, Matthias S.; Nagel, Wolfgang E.; Resch, Michael M. (eds.). Tools for High Performance Computing 2011. Berlin, Heidelberg: Springer. pp. 79–91. doi:10.1007/978-3-642-31476-6_7. ISBN 978-3-642-31476-6. S2CID 18004916.
  4. ^ Wolf, Felix; Wylie, Brian J. N.; Ábrahám, Erika; Becker, Daniel; Frings, Wolfgang; Fürlinger, Karl; Geimer, Markus; Hermanns, Marc-André; Mohr, Bernd (2008). "Usage of the SCALASCA toolset for scalable performance analysis of large-scale parallel applications". In Resch, Michael; Keller, Rainer; Himmler, Valentin; Krammer, Bettina; Schulz, Alexander (eds.). Tools for High Performance Computing. Berlin, Heidelberg: Springer. pp. 157–167. doi:10.1007/978-3-540-68564-7_10. ISBN 978-3-540-68564-7.
  5. ^ "Scalable performance analysis of large-scale parallel applications" (PDF). Retrieved 2020-11-14.
  6. ^ "Performance Analysis with Scalasca" (PDF). Retrieved 2020-11-14.


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