Tools for High Performance Computing 2009: Proceedings of by Shajulin Benedict, Ventsislav Petkov (auth.), Matthias S.

By Shajulin Benedict, Ventsislav Petkov (auth.), Matthias S. Müller, Michael M. Resch, Alexander Schulz, Wolfgang E. Nagel (eds.)

As an increasing number of structures aid parallelism, parallel programming is gaining momentum. functions can in basic terms leverage the functionality of multi-core processors or snap shots processing devices in the event that they may be able to cut up an issue into smaller ones that may be solved in parallel. The demanding situations rising from the improvement of parallel functions have resulted in the improvement of a good number of instruments for debugging, functionality research and different projects. The lawsuits of the third overseas Workshop on Parallel instruments for prime functionality Computing supply a technical evaluate with a view to aid engineers, builders and computing device scientists come to a decision which instruments are most fitted to improving their present improvement processes.

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Additional resources for Tools for High Performance Computing 2009: Proceedings of the 3rd International Workshop on Parallel Tools for High Performance Computing, September 2009, ZIH, Dresden

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In the near future, we expect to further improve the support for GPU based accelerators through vendor performance APIs. Furthermore, an extended scripting interface based on DBUS [12] will be incorporated into the performance visualizer. A customizable event search engine and the comparison of successive trace runs are active research topics. References 1. : Plasma Physics via Computer Simulation (Series in Plasma Physics). Taylor & Francis, 1 edn. (October 2004) 2. : Scalable parallel trace-based performance analysis.

During the analysis, Scalasca searches for wait states and related performance properties, classifies detected instances by category, and quantifies their significance. The result is a wait-state report similar in structure to the summary report but enriched with higherlevel communication and synchronization inefficiency metrics. Both summary and wait-state reports contain performance metrics for every measured function call path and process/thread which can be interactively examined in the provided analysis report explorer.

4b). Using prototype implementations of these new tools, we evaluated the performance behavior of the SPEC MPI2007 benchmark suite on the IBM SP p690 cluster JUMP, observing a large variety of complex temporal characteristics ranging from gradual changes and sudden transitions of the base-line behavior to both periodically and irregularly occurring peaks, including noise that varies from measurement to measurement [19]. GemsFDTD and insufficiently large data sets for several others. Even those codes that apparently scaled well contained considerable quantities of waiting time, indicating possible opportunities for performance and scalability improvement through more effective work distributions or bindings of processes to processors.

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