Article MPI support in the Prism programming environment Share on Authors: Steve Sistare Sun Microsystems, Inc. Sun Microsystems, Inc.View Profile , Erica Dorenkamp Sun Microsystems, Inc. Sun Microsystems, Inc.View Profile , Nick Nevin Sun Microsystems, Inc. Sun Microsystems, Inc.View Profile , Eugene Loh Sun Microsystems, Inc. Sun Microsystems, Inc.View Profile Authors Info & Claims SC '99: Proceedings of the 1999 ACM/IEEE conference on SupercomputingJanuary 1999 Pages 22–eshttps://doi.org/10.1145/331532.331554Online:01 January 1999Publication History 6citation285DownloadsMetricsTotal Citations6Total Downloads285Last 12 Months1Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Implementors of message-passing libraries have focused on optimizing point-to-point protocols and have largely ignored the performance of collective operations. In addition, algorithms for collectives have been tuned to run well on networks of uni-processor machines, ignoring the performance that may be gained on large-scale SMP’s in wide-spread use as compute nodes. This is unfortunate, because the high backplane bandwidths and shared-memory capabilities of large SMP’s are a perfect match for the requirements of collectives. We present new algorithms for MPI collective operations that take advantage of the capabilities of fat-node SMP’s and provide models that show the characteristics of the old and new algorithms. Using the SunTM MPI library, we present results on a 64-way StarfireTM SMP and a 4-node cluster of 8-way Sun EnterpriseTM 4000 nodes that show performance improvements ranging typically from 2x to 5x for the collectives we studied.