Parallel Application Signature for Performance Prediction : Ph. D. Thesis in High Perfomance Computing

In order to measure the performance of a parallel machine, a set of application kernels as benchmarks have often been used. However, it is not always possible to characterize the performance using only benchmarks, given the fact that each one usually reflects a narrow set of kernel applications at best. Computers show different performance indices for different applications as they run them. Accurate prediction of parallel applications’ performance is becoming increasingly complex and the time required to run it thoroughly is an onerous requirement; especially if we want to predict for different systems. In production clusters, where throughput and efficiency of use are fundamental, it is important to be able to predict which system is more appropriate for an application, or how long a scheduled application will take to run, in order to have the foresight that will allow us to make better use of the resources available.
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