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05491430 Labarta, Jes\'us Mohr, Bernd Snavely, Allan Vetter, Jeffrey Topic 2: Performance prediction and evaluation. Nagel, Wolfgang E. (ed.) et al., Euro-Par 2006 parallel processing. 12th international Euro-Par conference, Dresden, Germany, August 28--September 1, 2006. Proceedings. Berlin: Springer (ISBN 978-3-540-37783-2/pbk). Lecture Notes in Computer Science 4128, 63 (2006). 2006 Berlin: Springer EN

doi:10.1007/11823285_7

Summary: 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.