Compile-Time Estimation of Communication Costs for Data Parallel Programs

Most of the current compiler projects for distributed memory architectures leave the critical and time-consuming problem of finding performance-efficient data distributions and profitable program transformations for a given parallel program almost entirely to the programmer. Performance estimators provide critical performance information to both programmers and parallelizing compilers, the most crucial part of which involves determining the communication overhead induced by a program. In this paper, we present a very practical approach to the problem of compile-time estimation of communication costs for regular codes that includes analytical methods to model the number of messages exchanged, data volume transferred, transfer time, and network contention. In order to achieve high estimation accuracy, our estimator aggressively exploits compiler analysis and optimization information. It is assumed that machine parameters and problem size are known at compile time. We conducted a variety of experiments to validate the estimation accuracy and the ability to support both the programmer and compiler in the effort of performance tuning of parallel programs. We believe that our approach can be automatically applied to a large class of regular codes