Auto-tuning methodology to represent landform attributes on multicore and multi-GPU systems
Proceedings of the 2013 International Workshop on Programming Models and Applications for Multicores and Manycores
Empirical Installation of Linear Algebra Shared-Memory Subroutines for Auto-Tuning
International Journal of Parallel Programming
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Climate simulations are very computational time consuming tasks which are usually solved in parallel systems. However, to reduce the time needed for the simulations, a set of parameters must be optimally selected. This paper presents a methodology to select such parameters for a particular simulation code (the MM5 mesoescalar model). When the code is installed in a computational system its behaviour when executing the code is characterized by a set of parameters. The values obtained are included in a model of the execution time of the code, and the simulation is carried out at running time with the running configuration with which the lowest theoretical time is obtained. An important reduction in the execution time is achieved. In the experiments the reduction is between 25% and 40%. The methodology proposed could be applied to other problems in which the code to be optimized is considered as a black box.