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. In this paper, we discuss an approach in which the collective communications are tuned for a given system by conducting a series of experiments on the system. We also discuss a dynamic topology method that uses the tuned static topology shape, but re-orders the logical addresses to compensate for changing run time variations. A series of experiments were conducted comparing our tuned collective communication operations to various native vendor MPI implementations. The use of the tuned collective communications resulted in about 30%-650% improvement in performance over the native MPI implelementations.
Automatic tuning of collectives for a given system by conducting a series of experiments on the system was discussed in REF .
Automatically tuned collective communications
SC '00
Computer Science