Modeling power consumption of 3D MPDATA and the CG method on ARM and Intel multicore architectures

Abstract

We propose an approach to estimate the power consumption of algorithms, as a function of the frequency and number of cores, using only a very reduced set of real power measures. In addition, we also provide the formulation of a method to select the voltage–frequency scaling–concurrency throttling configurations that should be tested in order to obtain accurate estimations of the power dissipation. The power models and selection methodology are verified using two real scientific application: the stencil-based 3D MPDATA algorithm and the conjugate gradient (CG) method for sparse linear systems. MPDATA is a crucial component of the EULAG model, which is widely used in weather forecast simulations. The CG algorithm is the keystone for iterative solution of sparse symmetric positive definite linear systems via Krylov subspace methods. The reliability of the method is confirmed for a variety of ARM and Intel architectures, where the estimated results correspond to the real measured values with the average error being slightly below 5% in all cases.