Fast Multi-parameter Performance Modeling

Alexandru Calotoiu, David Beckingsale, Christopher W. Earl, Torsten Hoefler, Ian Karlin, Martin Schulz, Felix Wolf

Abstract: Tuning large applications requires a clever exploration of the design and configuration space. Especially on supercomputers, this space is so large that its exhaustive traversal via performance experiments becomes too expensive, if not impossible. Manually creating analytical performance models provides insights into optimization opportunities but is extremely laborious if done for applications of realistic size. If we must consider multiple performance-relevant parameters and their possible interactions, a common requirement, this task becomes even more complex. We build on previous work on automatic scalability modeling and significantly extend it to allow insightful modeling of any combination of application execution parameters. Multi-parameter modeling has so far been outside the reach of automatic methods due to the exponential growth of the model search space. We develop a new technique to traverse the search space rapidly and generate insightful performance models that enable a wide range of uses from performance predictions for balanced machine design to performance tuning.