Topic 2: Performance Prediction and Evaluation

Parallel computing enables solutions to computational problems that are impossible on sequential systems due to their limited performance. To meet this objective, it is critical that users can both measure performance on a given system and predict the performance for other systems. Achieving high performance on parallel computer systems is the product of an intimate combination of hardware architecture (processor, memory, interconnection network), system software, runtime environment, algorithms, and application design. Performance evaluation is the science of understanding these factors that contribute to the overall expression of parallel performance on real machines and on systems yet to be realized. Benchmarking and performance characterization methodologies and tools provide an empirical foundation for performance evaluation. Performance prediction techniques provide a means to model performance behaviors and properties as system, algorithm, and software features change, particularly in the context of large-scale parallelism. These two areas are closely related since most prediction requires data to be gathered from measured runs of a program, to identify application signatures or to understand the performance characteristics of current machines.

Authors and Affiliations

1. Topic Chairs,  

   Jesús Labarta, Bernd Mohr, Allan Snavely & Jeffrey Vetter

Editors and Affiliations

1. ZIH, TU Dresden, Germany

   Wolfgang E. Nagel

2. Fakultät Mathematik, Institut für wissenschaftliches Rechnen, TU Dresden, 01062, Dresden, Germany

   Wolfgang V. Walter

3. Database Technology Group, Technische Universität Dresden, Germany

   Wolfgang Lehner

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