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Monte Carlo optimisation auto-tuning on a multi-GPU cluster

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1 Author(s)
Paukste, A. ; Fac. of Math. & Inf., Vilnius Univ., Vilnius, Lithuania

In this paper we investigate Monte Carlo optimisation of the fitness function on a multi-GPU cluster. Our main goal is to develop auto-tuning techniques for the GPU cluster. Monte Carlo or random sampling is a technique to optimise a fitness function by giving random values to function parameters. When execution of the fitness function requires a high amount of computational power Monte Carlo sampling becomes both very time and computational power consuming. A developer who is not familiar with the application, hardware, and the CUBA runtime cannot determine the optimal execution parameters. This makes GPU auto-tuning well suited to achieving better performance and reducing computing time.

Published in:

Parallel Distributed and Grid Computing (PDGC), 2012 2nd IEEE International Conference on

Date of Conference:

6-8 Dec. 2012