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S cala E xtrap : Trace-based communication extrapolation for SPMD programs

SCALAEXTRAP: Trace-Based Communication Extrapolation for SPMD Programs XING WU and FRANK MUELLER, North Carolina State University Performance modeling for scienti c 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 ef ciency 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 re ‚ect 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 inef ciencies and scalability limitations. To the best of our knowledge, rapidly http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Programming Languages and Systems (TOPLAS) Association for Computing Machinery

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