Matching Application Signatures for Performance Predictions Using a Single Execution

Performance predictions for large problem sizes and processors using limited small scale runs are useful for a variety of purposes including scalability projections, and help in minimizing the time taken for constructing training data for building performance models. In this paper, we present a prediction framework that matches execution signatures for performance predictions of HPC applications using a single small scale application execution. Our framework extracts execution signatures of applications and performs automatic phase identification of different application phases. Application signatures of the different phases are matched with the execution profiles of reference kernels stored in a kernel database. The performance of the reference kernels are then used to predict the performance of the application phases. For phases that do not match significantly, our framework performs static analysis of loops and functions in the application to provide prediction ranges. We demonstrate this integrated set of techniques in our framework with three large scale applications, including GTC, a Particle-in-Cell code for turbulence simulation, Sweep3d, a 3D neutron transport application and SMG2000, a multigrid solver. We show that our prediction ranges are accurate in most cases.