Performance Analysis and Linear Optimization Modeling of All-to-all Collective Communication Algorithms

Abstract

The performance of collective communication operations still represents a critical issue for high performance computing systems. Users of parallel machines need to have a good grasp of how different communication patterns and styles affect the performance of message-passing applications. This paper reports our contribution of the analysis of collective communication algorithms in the context of MPI programming paradigm by extending a standard point- to-point communication model, which is P-LogP. We focus on MPI Alltoall since this function is one of the most communication intensive collective operations known. In order to reduce the gap between the predicted and the measured run-time, all the system parameters are also taken into account with the total performance estimation, by applying the linear regression modeling with the empirical data. Results on InfiniBand clusters show that the final performance prediction models can accurately capture the entire system communication behavior of all algorithms, even for large size messages and large number of processors.