CHARACTERIZING LARGE-SCALE HPC APPLICATIONS THROUGH TRACE EXTRAPOLATION

Abstract

The analysis and understanding of large-scale application behavior is critical for effectively utilizing existing HPC resources and making design decisions for upcoming systems. In this work we utilize the information about the behavior of an MPI application at a series of smaller core counts to characterize its behavior at a much larger core count. Our methodology first captures the application's behavior via a set of features that are important for both performance and energy (cache hit rates, floating point intensity, ILP, etc.). We then find the best statistical fit from among a set of canonical functions in terms of how these features change across a series of small core counts. The models for a given feature can then be utilized to generate an extrapolated trace of the application at scale. The accuracy of the extrapolated traces is evaluated by calculating the error of the extrapolated trace relative to an actual trace for two large-scale applications, UH3D and SPECFEM3D. The accuracy of the fully extrapolated traces is further evaluated by comparing the results of building performance models using both the extrapolated trace along with an actual trace in order to predict application performance. For these two full-scale HPC applications, performance models built using the extrapolated traces predicted the runtime with absolute relative errors of less than 5%.