Towards Predictive Modeling of Message-Passing Communication

Abstract

Communication has been shown to be a performance bottleneck and a limiting factor of many large parallel applications. As such, predicting the application scalability necessitates a communication performance model.This paper investigates the LogGP communication performance model for predicting message-passing communications when the system con?guration (i.e., number of nodes) is varied. The cost functions for the message-passing operations are based on MVAPICH2 1.0, and the experiments are conducted on the Ranger system using up to 256 nodes connected with an In?niBand net-work. For point-to-point communications, we observe that the LogGP model accurately predicts the communication performance. However, the results for three collective operations, i.e., MPI_Barrier, MPI_Alltoall, and MPI_Bcast, are varying. For MPI_Bcast, the LogGP model is able to predict its scalability up to 256 nodes, and the prediction error is at most a factor of two on 256 nodes. For the remaining collectives, the scalability--- bar that of MPI_Alltoall on small messages (m = 2 bytes) --- is predicted by LogGP, but the prediction error for 256 nodes is 3.5–12 times of the measured performance.