A Continuous Benchmarking Infrastructure for High-Performance Computing Applications

Abstract

For scientific software, especially those used for large-scale simulations, achieving good performance and efficiently using the available hardware resources is essential. It is important to regularly perform benchmarks to ensure the efficient use of hardware and software when systems are changing and the software evolves. However, this can become quickly very tedious when many options for parameters, solvers, and hardware architectures are available. We present a continuous benchmarking strategy that automates benchmarking new code changes on high-performance computing clusters. This makes it possible to track how each code change affects the performance and how it evolves.

References in corpus (6)

• Comprehensive comparison of collision models in the lattice Boltzmann framework: Theoretical investigations
• waLBerla: A block-structured high-performance framework for multiphysics simulations
• HeAT -- a Distributed and GPU-accelerated Tensor Framework for Data Analytics
• Advanced Automatic Code Generation for Multiple Relaxation-Time Lattice Boltzmann Methods
• xSDK Foundations: Toward an Extreme-scale Scientific Software Development Kit
• Comparison of refilling schemes in the free-surface lattice Boltzmann method