Abstract. Previous studies of application usage show that the perfor-mance of collective communications are critical for high-performance computing. Despite active research in the field, both general and fea-sible solution to the optimization of collective communication problem is still missing. In this paper, we analyze and attempt to improve intra-cluster collective communication in the context of the widely deployed MPI programming paradigm by extending accepted models of point-to-point communica-tion, such as Hockney, LogP/LogGP, and PLogP, to collective opera-tions. We compare the predictions from models against the experimen-tally gathered data and using these results, construct optimal decision function for broadcast collective. We quantitatively compare the quality of the model-based decision functions to the experimentally-optimal one. Additionally, in this work, we also introduce a new form of an optimized tree-based broadcast algorithm, splitted-binary. Our results show that all of the models can provide useful insights into various aspects of the different algorithms as well as their relative perfor-mance. Still, based on our findings, we believe that the complete reliance on models would not yield optimal results. In addition, our experimental ⋆ This material is based upon work supported by the Department of Energy under Contract No. DE-FG02-02ER25536. results have identified the gap parameter as being the most critical for accurate modeling of both the classical point-to-point-based pipeline and our extensions to fan-out topologies.
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