Value influence analysis for message passing applications

People who develop, debug, and optimize applications are most effective when they understand how those applications function. Value influence tracking is an on-line code analysis approach that provides a data-centric perspective on how a value contributes to later computation. Early work on value influence tracking focused on single-process applications. Building upon this early work, we have designed support for performing value influence tracking analyses with applications that use common MPI point-to-point and collective communication operations. In this paper, we describe the design and implementation of an approach for propagating value influence data between the processes of an MPI application that uses these types of operations. To demonstrate and evaluate our approach, we present case studies of using our value influence tracking implementation with the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) and the Model for Prediction Across Scales (MPAS) ocean climate model running on the Keeneland Initial Delivery System (KIDS) Linux cluster. We also discuss how to extend our approach to support MPI one-sided operations and non-blocking collective communication operations.