Scalable Interconnection Network Models for Rapid Performance Prediction of HPC Applications

@article{Ahmed2016ScalableIN,
  title={Scalable Interconnection Network Models for Rapid Performance Prediction of HPC Applications},
  author={Kishwar Ahmed and Jason Liu and Stephan Eidenbenz and Joe Zerr},
  journal={2016 IEEE 18th International Conference on High Performance Computing and Communications; IEEE 14th International Conference on Smart City; IEEE 2nd International Conference on Data Science and Systems (HPCC/SmartCity/DSS)},
  year={2016},
  pages={1069-1078}
}
Performance Prediction Toolkit (PPT) is a simulator mainly developed at Los Alamos National Laboratory to facilitate rapid and accurate performance prediction of large-scale scientific applications on existing and future HPC architectures. In this paper, we present three interconnect models for performance prediction of large-scale HPC applications. They are based on interconnect topologies widely used in HPC systems: torus, dragonfly, and fat-tree. We conduct extensive validation tests of our… CONTINUE READING
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