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.

翻译：对于科学软件，尤其是用于大规模模拟的软件，实现良好性能并高效利用可用硬件资源至关重要。在系统不断变化且软件持续演进的情况下，定期进行基准测试以确保硬件和软件的高效使用非常重要。然而，当参数、求解器和硬件架构存在众多选项时，这一过程很快就会变得极为繁琐。我们提出了一种持续基准测试策略，该策略可自动在高性能计算集群上对新代码变更进行基准测试。这使得追踪每次代码变更对性能的影响及其演变过程成为可能。