Topology Aware Performance Prediction of Collective Communication Algorithms on Multi-Dimensional Mesh/Torus

Hironobu Sugiyama, Yoshiyuki Morie, Takeshi Nanri

Abstract


As the scale of computers becomes large, technologies for appropriate selection of collective communication algorithm have become important. Especially, on Multi Dimensional Mesh/Torus topology, which is popularly used on large-scale supercomputers, this selection is one of the most important issues for achieving sufficient scalability. Currently, the authors are developing a method that combines performance prediction models and runtime measurement to enable efficient algorithm selection. This paper introduces a performance prediction model of algorithms on Multi-Dimensional Mesh/Torus topology. This model considers effects of collisions on links to increase the preciseness of the prediction. Some experiments have been done to show the advantages of the proposed model. 


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