Copyright Notice:

The documents distributed by this server have been provided by the contributing authors as a means to ensure timely dissemination of scholarly and technical work on a noncommercial basis. Copyright and all rights therein are maintained by the authors or by other copyright holders, notwithstanding that they have offered their works here electronically. It is understood that all persons copying this information will adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

Publications of SPCL

P. Gottschling, T. Hoefler:

 Productive Parallel Linear Algebra Programming with Unstructured Topology Adaption

(. Vol , Nr. , In Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012), presented in Ottawa, Canada, pages 9--16, IEEE Computer Society, ISSN: , ISBN: 978-0-7695-4691-9, May 2012, )


Abstract

Sparse 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 meta-programming introducing a new paradigm: meta-tuning. The library establishes its own domain-specific 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%.

ACM Stats



Documents

download article:
download slides:
 

BibTeX

@inproceedings{gottschling-topomap,
  author={P. Gottschling and Torsten Hoefler},
  title={{Productive Parallel Linear Algebra Programming with Unstructured Topology Adaption }},
  journal={},
  institution={},
  year={2012},
  month={05},
  pages={9--16},
  volume={},
  number={},
  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},
  issn={},
  isbn={978-0-7695-4691-9},
  note={},
}