ADEPT scalability predictor in support of adaptive resource allocation

摘要

Adaptive resource allocation with different numbers of machine nodes provides more flexibility and significantly better potential performance for local job and grid scheduling. With the emergence of parallel computing in every-day life on multi-core systems, such schedulers will likely increase in practical relevance. A major reason why adaptive schedulers are not yet practically used is lacking knowledge of the scalability curves of the applications. Existing white-box approaches for scalability prediction are too expensive to apply them routinely. We present ADEPT, a speedup and runtime prediction tool, which is inexpensive and easy-to-use. ADEPT employs a black-box model and can be practically applied at large scale without user or administrator involvement. ADEPT requires neither program analysis and measurements nor user guesses but makes highly accurate predictions with only few observations of application runtime over different numbers of nodes/cores. ADEPT performs efficient model fitting by introducing an envelope-derivation technique to constrain the search. Additionally, ADEPT is capable of handling deviations from the underlying model by detection and automatic correction of anomalies via a fluctuation metric and by considering specific scalability patterns via multi-phase modeling. ADEPT also performs reliability judgment with potential proposal for placement of additional observations. Using MPI and OpenMP implementations of the NAS benchmarks and seven real applications, we demonstrate the effectiveness and high prediction accuracy of ADEPT for both speedup and runtime prediction, including interpolative and extrapolative cases, and show the capability of ADEPT to successfully handle special cases.