arXiv subjects

Gregor Corbin

Publications and source records attributed to Gregor Corbin.

4 recordsLinked to original sources

arXiv2025

Performance measurements of modern Fortran MPI applications with Score-P

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.

cs.DC↗

arXiv2025

Denoising Application Performance Models with Noise-Resilient Priors

As parallel codes are scaled to larger computing systems, performance models play a crucial role in identifying potential bottlenecks. However, constructing these models analytically is often challenging. Empirical models based on performance measurements provide a practical alternative, but measurements on high-performance computing (HPC) systems are frequently affected by noise, which can lead to misleading predictions. To mitigate the impact of noise, we introduce application-specific dynamic priors into the modeling process. These priors are derived from noise-resilient measurements of computational effort, combined with domain knowledge about common algorithms used in communication routines. By incorporating these priors, we effectively constrain the model's search space, eliminating complexity classes that capture noise rather than true performance characteristics. This approach keeps the models closely aligned with theoretical expectations and substantially enhances their predictive accuracy. Moreover, it reduces experimental overhead by cutting the number of repeated measurements by half.

cs.PF↗

arXiv2019

Asymptotic preserving $P_N$ methods for haptotaxis equations

The so-called haptotaxis equation is a special class of transport equation that arises from models of biological cell movement along tissue fibers. This equation has an anisotropic advection-diffusion equation as its macroscopic limit. An up to second-order accurate asymptotic preserving method is developed for the haptotaxis equation in space dimension up to three. For this the micro-macro decomposition proposed by Lemou and Mieussens is generalized in the context of finite-volume schemes on staggered grids. The spurious modes that arise from this discretization can be eliminated by combining flux evaluations from different points in the right way. The velocity space is discretized by an arbitrary-order linear moment system ($P_N$).

math.NA↗

arXiv2018

Higher-order models for glioma invasion: from a two-scale description to effective equations for mass density and momentum

Starting from a two-scale description involving receptor binding dynamics and a kinetic transport equation for the evolution of the cell density function under velocity reorientations, we deduce macroscopic models for glioma invasion featuring partial differential equations for the mass density and momentum of a population of glioma cells migrating through the anisotropic brain tissue. The proposed first and higher order moment closure methods enable numerical simulations of the kinetic equation. Their performance is then compared to that of the diffusion limit. The approach allows for DTI-based, patient-specific predictions of the tumor extent and its dynamic behavior.

math.NA↗