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P. Gottschling and T. Hoefler:
  Productive Parallel Linear Algebra Programming with Unstructured Topology Adaption
(In Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012), presented in Ottawa, Canada, pages 916, IEEE Computer Society, ISBN: 9780769546919, May 2012)
AbstractSparse linear algebra is a key component of many
scientific computations such as computational fluid dynamics,
mechanical engineering or the design of new materials to mention
only a few. The discretization of complex geometries in unstructured meshes leads to sparse matrices with irregular patterns.
Their distribution in turn results in irregular communication
patterns within parallel operations.
In this paper, we show how sparse linear algebra can be
implemented effortless on distributed memory architectures. We
demonstrate how simple it is to incorporate advanced partitioning, network topology mapping, and data migration techniques
into parallel HPC programs.
For this purpose, we developed a linear algebra library — Parallel Matrix Template Library 4 — based on generic and
metaprogramming introducing a new paradigm: metatuning.
The library establishes its own domainspecific language embedded in C++. The simplicity of software development is not paid
by lower performance. Moreover, the incorporation of topology
mapping demonstrated performance improvements up to 29%.
Documentsdownload article:   BibTeX  @inproceedings{gottschlingtopomap, author={P. Gottschling and T. Hoefler}, title={{Productive Parallel Linear Algebra Programming with Unstructured Topology Adaption }}, year={2012}, month={May}, pages={916}, booktitle={Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012)}, location={Ottawa, Canada}, publisher={IEEE Computer Society}, isbn={9780769546919}, source={http://www.unixer.de/~htor/publications/}, } 

