Continuous Benchmarking of Numerical Algorithms Implemented in M++ via Gitlab CI/CD and Google Benchmark∗

We present an automated framework for benchmarking numerical algorithms that solve partial differential equations under consistent and reproducible conditions using the parallel finite element software M++. This framework integrates GitLab CI/CD, Google Benchmark, and the HoreKa supercomputing system to enable continuous integration and benchmarking. By incorporating ongoing software development, the framework supports improving performance and reliability, which are vital for various scientific computing applications, including wave propagation, cardiovascular simulations, dislocation dynamics, and uncertainty quantification. These applications motivate the two benchmarking examples presented in this text. We further outline the benchmarking workflow as well as the use of a research database storing comprehensive performance data, facilitating reproducibility for future studies.

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Publication Details

Journal
UBMA
Published
2025-09-25
DOI
https://doi.org/10.25521/maconf.2025.1025
Primary Topic
Model Reduction and Neural Networks
Type
article
Field-Weighted Citation Impact
0.00
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article

Continuous Benchmarking of Numerical Algorithms Implemented in M++ via Gitlab CI/CD and Google Benchmark∗

UBMA
Model Reduction and Neural Networks
article

Continuous Benchmarking of Numerical Algorithms Implemented in M++ via Gitlab CI/CD and Google Benchmark∗

article en

Abstract

We present an automated framework for benchmarking numerical algorithms that solve partial differential equations under consistent and reproducible conditions using the parallel finite element software M++. This framework integrates GitLab CI/CD, Google Benchmark, and the HoreKa supercomputing system to enable continuous integration and benchmarking. By incorporating ongoing software development, the framework supports improving performance and reliability, which are vital for various scientific computing applications, including wave propagation, cardiovascular simulations, dislocation dynamics, and uncertainty quantification. These applications motivate the two benchmarking examples presented in this text. We further outline the benchmarking workflow as well as the use of a research database storing comprehensive performance data, facilitating reproducibility for future studies.

UBMA
Karlsruhe Institute of Technology (DE), Heidelberg University (DE), Heidelberg University (US)
Industry, innovation and infrastructure
Openalex Percentile: Top 48%
Model Reduction and Neural Networks
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Continuous Benchmarking of Numerical Algorithms Implemented in M++ via Gitlab CI/CD and Google Benchmark∗ · UBMA (2025) | TGRS Research Map | TGRS