BibTeX Export

@INPROCEEDINGS{Harlacher_ea:2012:DynamicLoadBalancing,
     author = {Harlacher, Daniel and Klimach, Harald and Roller, Sabine and Siebert, Christian and Wolf, Felix},
   keywords = {load balancing, partitioning, scalability, space-filling curve},
      month = may,
      title = {Dynamic Load Balancing for Unstructured Meshes on Space-Filling Curves},
  booktitle = {Proc. of the IEEE 26th International Parallel and Distributed Processing Symposium (IPDPS) Workshops \& PhD Forum, Shanghai, China},
       year = {2012},
      pages = {1655--1663},
  publisher = {IEEE},
       note = {Workshop on Large-Scale Parallel Processing},
       isbn = {978-1-4673-0974-5},
        doi = {10.1109/IPDPSW.2012.207},
   abstract = {Load imbalance is an important impediment on the path towards higher degrees
of parallelism - especially for engineering codes with their highly unstructured
problem domains. In particular, when load conditions change dynamically, efficient
mesh partitioning becomes an indispensable ingredient of scalable design.
However, popular graph-based methods such as those used by ParMetis require
global knowledge, which effectively limits the problem size on distributed-memory
machines. On such architectures, space-filling curves (SFCs) offer a memory-efficient alternative and many sophisticated schemes have already been proposed.
In this paper, we present a simple strategy based on SFCs that is custom-tailored
to the needs of static meshes with dynamically changing computational load.
Exploiting the properties of this class of problems, it is not only easy to
implement but also reduces memory requirements substantially. Moreover,
exclusively relying on MPI collective operations, our load-balancing scheme also
offers portable performance across a broad range of HPC systems. Experimental
evaluation shows excellent scaling behavior for up to 16,384 cores on a Nehalem-Infiniband system and up to 294,912 processes on a Blue Gene/P system.}
}