Empirical Mode Decomposition for Modeling of Parallel Applications on Intel Xeon Phi Processors

For modern parallel applications, modeling their general execution characteristics, such as power and time, is difficult due to a great many factors affecting software-hardware interactions, which is also exacerbated by the dearth of measuring and monitoring tools for novel architectures, such as Intel Xeon Phi processors. To address this modeling challenge, the present work proposes to employ the Empirical Mode Decomposition (EMD) method to describe an execution as a series of modes culminating in a single residual trend, for which, in its turn, a model equation is obtained as a non-linear fit. As outcome, an overall energy consumption may be predicted using this model. A real-world quantum-chemistry application GAMESS and a molecular-dynamics proxy application CoMD were considered in the experiments. The results demonstrate that the energy modeled ranges within 10-30% of the measured energy, depending on the length of execution.