Generating Performance Models for Irregular Applications

Abstract

Many applications have irregular behavior — e.g., input-dependent solvers, irregular memory accesses, or unbiased branches — that cannot be captured using today's automated performance modeling techniques. We describe new hierarchical critical path analyses for the Palm model generation tool. To obtain a good tradeoff between model accuracy, generality, and generation cost, we combine static and dynamic analysis. To create a model's outer structure, we capture tasks along representative MPI critical paths. We create a histogram of critical tasks with parameterized task arguments and instance counts. To model each task, we identify hot instruction-level paths and model each path based on data flow, data locality, and microarchitectural constraints. We describe application models that generate accurate predictions for strong scaling when varying CPU speed, cache and memory speed, microarchitecture, and (with supervision) input data class. Our models' errors are usually below 8%; and always below 13%.