Exploiting Geometric Partitioning in Task Mapping for Parallel Computers

@article{Deveci2014ExploitingGP,
  title={Exploiting Geometric Partitioning in Task Mapping for Parallel Computers},
  author={Mehmet Deveci and Sivasankaran Rajamanickam and Vitus J. Leung and Kevin T. Pedretti and Stephen L. Olivier and David P. Bunde and {\"U}mit V. Çataly{\"u}rek and Karen D. Devine},
  journal={2014 IEEE 28th International Parallel and Distributed Processing Symposium},
  year={2014},
  pages={27-36}
}
We present a new method for mapping applications' MPI tasks to cores of a parallel computer such that communication and execution time are reduced. We consider the case of sparse node allocation within a parallel machine, where the nodes assigned to a job are not necessarily located within a contiguous block nor within close proximity to each other in the network. The goal is to assign tasks to cores so that interdependent tasks are performed by "nearby" cores, thus lowering the distance… CONTINUE READING

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  • We show that, for the structured finite difference mini-app Mini Ghost, our mapping method reduced execution time 34% on average on 65,536 cores of a Cray XE6. In a molecular dynamics mini-app, Mini MD, our mapping method reduced communication time by 26% on average on 6144 cores. We also compare our mapping with graph-based mappings from the LibTopoMap library and show that our mappings reduced the communication time on average by 15% in MiniGhost and 10% in MiniMD.

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