Application Knowledge Required: Performance Modeling for Fun and Profit

In High Performance Computing, resource efficiency is paramount. Expensive systems need to be utilized to the maximum of their capabilities, but deep insight into the bottlenecks of a particular hardware-software combination is often lacking on the users' side. Analytic, first-principles performance models can provide such insight. They are built on simplified descriptions of the machine, the software, and how they interact. This goes, to some extent, against the general trend towards automation in computer science; the individual conducting the analysis does require some knowledge of the application and the hardware in order to make performance engineering a scientific process instead of blindly generating data with tools that are poorly understood. This talk uses examples from parallel high-performance computing to demonstrate how analytic performance models can support scientific thinking in performance engineering: Sparse matrix-vector multiplication, the HPCG benchmark, the CloverLeaf proxy app, and a lattice-Boltzmann solver. Interestingly, the most intriguing insights emerge from the failure of analytic models to accurately predict performance measurements.