S cala E xtrap

Abstract

Performance modeling for scientific applications is important for assessing potential application performance and systems procurement in high-performance computing (HPC). Recent progress on communication tracing opens up novel opportunities for communication modeling due to its lossless yet scalable trace collection. Estimating the impact of scaling on communication efficiency still remains nontrivial due to execution-time variations and exposure to hardware and software artifacts. This work contributes a fundamentally novel modeling scheme. We synthetically generate the application trace for large numbers of nodes via extrapolation from a set of smaller traces. We devise an innovative approach for topology extrapolation of single program, multiple data (SPMD) codes with stencil or mesh communication. Experimental results show that the extrapolated traces precisely reflect the communication behavior and the performance characteristics at the target scale for both strong and weak scaling applications. The extrapolated trace can subsequently be (a) replayed to assess communication requirements before porting an application, (b) transformed to autogenerate communication benchmarks for various target platforms, and (c) analyzed to detect communication inefficiencies and scalability limitations. To the best of our knowledge, rapidly obtaining the communication behavior of parallel applications at arbitrary scale with the availability of timed replay, yet without actual execution of the application, at this scale, is without precedence and has the potential to enable otherwise infeasible system simulation at the exascale level.