Conference Paper

Autotuning multigrid with PetaBricks.

DOI: 10.1145/1654059.1654065 Conference: Proceedings of the ACM/IEEE Conference on High Performance Computing, SC 2009, November 14-20, 2009, Portland, Oregon, USA
Source: DBLP

ABSTRACT Algorithmic choice is essential in any problem domain to re- alizing optimal computational performance. Multigrid is a prime example: not only is it possible to make choices at the highest grid resolution, but a program can switch techniques as the problem is recursively attacked on coarser grid levels to take advantage of algorithms with dierent scaling behav- iors. Additionally, users with dierent convergence criteria must experiment with parameters to yield a tuned algorithm that meets their accuracy requirements. Even after a tuned algorithm has been found, users often have to start all over when migrating from one machine to another. We present an algorithm and autotuning methodology that address these issues in a near-optimal and ecient man- ner. The freedom of independently tuning both the algo- rithm and the number of iterations at each recursion level re- sults in an exponential search space of tuned algorithms that have dierent accuracies and performances. To search this


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Jun 4, 2014