Abstract
The performance of the MPI’s collective communications is critical in most MPI-based applications. A general algorithm for a given collective communication operation may not give good performance on all systems due to the differences in architectures, network parameters and the storage capacity of the underlying MPI implementation. Hence, collective communications have to be tuned for the system on which they will be executed. In order to determine the optimum parameters of collective communications on a given system in a time-efficient manner, the collective communications need to be modeled efficiently. In this paper, we discuss various techniques for modeling collective communications.KeywordsMessage SizeSegment SizeCollective CommunicationCollective OperationBroadcast ScheduleThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
