Ar­tic­le "Spee­dup and Ef­fi­cien­cy of Com­pu­ta­ti­o­nal Par­al­le­li­za­ti­on: A Uni­fy­ing Ap­proach and Asym­pto­tic Ana­ly­sis" Ac­cep­ted for Pu­bli­ca­ti­on in Jour­nal of Par­al­lel and Dis­tri­bu­ted Com­pu­ting

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The article "Speedup and Efficiency of Computational Parallelization: A Unifying Approach and Asymptotic Analysis" by Guido Schryen was accepted for publication in Journal of Parallel and Distributed Computing.


In high performance computing environments, we observe an ongoing increase in the available number of cores. For example, the current TOP500 list reveals that nine clusters have more than 1 million cores. This development calls for re-emphasizing performance (scalability) analysis and speedup laws as suggested in the literature (e.g., Amdahl’s law and Gustafson’s law), with a focus on asymptotic performance. Understanding speedup and efficiency issues of algorithmic parallelism is useful for several purposes, including the optimization of system operations, temporal predictions on the execution of a program, the analysis of asymptotic properties, and the determination of speedup bounds. However, the literature is fragmented and shows a large diversity and heterogeneity of speedup models and laws. These phenomena make it challenging to obtain an overview of the models and their relationships, to identify the determinants of performance in a given algorithmic and computational context, and, finally, to determine the applicability of performance models and laws to a particular parallel computing setting. In this work, I provide a generic speedup (and thus also efficiency) model for homogeneous computing environments. My approach generalizes many prominent models suggested in the literature and allows showing that they can be considered special cases of a unifying approach. The genericity of the unifying speedup model is achieved through parameterization. Considering combinations of parameter ranges, I identify six different asymptotic speedup cases and eight different asymptotic efficiency cases. Jointly applying these speedup and efficiency cases, I derive eleven scalability cases, from which I build a scalability typology. Researchers can draw upon my suggested typology to classify their speedup model and to determine the asymptotic behavior when the number of parallel processing units increases. Also, the description of two computational experiments demonstrates the practical application of the model and the typology. In addition, my results may be used and extended in future research to address various extensions of my setting.


A pre-print is available on RIS (UPB).