An empirical approach for efficient all-to-all personalized communication on Ethernet switched clusters

All–to–all personalized communication (AAPC) is one of the most commonly used communication patterns in parallel applications. Developing an efficient AAPC routine is difficult since many system parameters can affect the performance of an AAPC algorithm. In this paper, we investigate an empirical approach for automatically generating efficient AAPC routines for Ethernet switched clusters. This approach applies when the application execution environment is decided, and it allows efficient customized AAPC routines to be created. Experimental results show that the empirical approach generates routines that consistently achieve high performance on clusters with different network topologies. In many cases, the automatically generated routines out-perform conventional AAPC implementations to a large degree. Keywords: All–to–all personalized communication, Ethernet switched cluster, empirical technique, cluster computing, MPI. Technical areas: MPI, cluster computing