Performance measurements of modern Fortran MPI applications with Score-P

TL;DR

This paper details the development and implementation of full support for MPI Fortran 2008 bindings in Score-P, enabling comprehensive performance measurement for modern Fortran MPI applications.

Contribution

It introduces a complete set of MPI wrappers in Fortran for Score-P, matching C support and facilitating safe, standard-compliant performance analysis of Fortran MPI programs.

Findings

Successfully integrated MPI F08 wrappers into Score-P

Enabled performance measurement for Neko and EPIC codes

Achieved full support for MPI 4.1 features in Fortran

Abstract

Version 3.0 of the Message-Passing Interface (MPI) standard, released in 2012, introduced a new set of language bindings for Fortran 2008. By making use of modern language features and the enhanced interoperability with C, there was finally a type safe and standard conforming method to call MPI from Fortran. This highly recommended use mpi_f08 language binding has since then been widely adopted among developers of modern Fortran applications. However, tool support for the F08 bindings is still lacking almost a decade later, forcing users to recede to the less safe and convenient interfaces. Full support for the F08 bindings was added to the performance measurement infrastructure Score-P by implementing MPI wrappers in Fortran. Wrappers cover the latest MPI standard version 4.1 in its entirety, matching the features of the C wrappers. By implementing the wrappers in modern Fortran, we can provide full support for MPI procedures passing attributes, info objects, or callbacks. The implementation is regularly tested under the MPICH test suite. The new F08 wrappers were already used by two fluid dynamics simulation codes -- Neko, a spectral finite-element code derived from Nek5000, and EPIC (Elliptical Parcel-In-Cell) -- to successfully generate performance measurements. In this work, we additionally present our design considerations and sketch out the implementation, discussing the challenges we faced in the process. The key component of the implementation is a code generator that produces approximately 50k lines of MPI wrapper code to be used by Score-P, relying on the Python pympistandard module to provide programmatic access to the extracted data from the MPI standard.