Final Report: Performance Modeling Activities in PERC2

Progress in Performance Modeling for PERC2 resulted in: • Automated modeling tools that are robust, able to characterize large applications running at scale while simultaneously simulating the memory hierarchies of mul-tiple machines in parallel. • Porting of the requisite tracer tools to multiple platforms. • Improved performance models by using higher resolution memory models that ever before. • Adding control-flow and data dependency analysis to the tracers used in perform-ance tools. • Exploring and developing several new modeling methodologies. • Using modeling tools to develop performance models for strategic codes. • Application of modeling methodology to make a large number of “blind” per-formance predictions on certain mission partner applications, targeting most cur-rently available system architectures. • Error analysis to correct some systematic biases encountered as part of the large-scale blind prediction exercises. • Addition of instrumentation capabilities for communication libraries other than MPI. • Dissemination the tools and modeling methods to several mission partners, in-cluding DoD HPCMO and two DARPA HPCS vendors (Cray and IBM), as well as to the wider HPC community via a series of tutorials.