darmstadt.de

Many libraries in the HPC field encapsulate sophisticated algorithms with clear theoretical scalability expectations. However, hardware constraints or programming bugs may sometimes render these expectations inaccurate or even pla-inly wrong. While algorithm engineers have already been advocating the systematic combination of analytical perfor-mance models with practical measurements for a very long time, we go one step further and show how this compari-son can become part of automated testing procedures. The most important applications of our method include initial validation, regression testing, and benchmarking to com-pare implementation and platform alternatives. Advancing the concept of performance assertions, we verify asymptotic scaling trends rather than precise analytical expressions, re-lieving the developer from the burden of having to specify and maintain very fine-grained and potentially non-portable expectations. In this way, scalability validation can be con-tinuously applied throughout the whole development cycle with very little effort. Using MPI as an example, we show how our method can help uncover non-obvious limitations of both libraries and underlying platforms