Performance Prediction of Acoustic Wave Numerical Kernel on Intel Xeon Phi Processor
Résumé
Fast and accurate seismic processing workflow is a critical component for oil and gas exploration. In order to understand complex geological structures, the numerical kernels used mainly arise from the discretization of Partial Differential Equations (PDEs) and High Performance Computing methods play a major in seismic imaging. This leads to continuous efforts to adapt the softwares to support the new features of each architecture design and maintain performance level. In this context, predicting the performance on target processors is critical. This is particularly true regarding the high number of parameters to be tuned both at the hardware and the software levels (architectural features, compiler flags, memory policies, multithreading strategies). This paper focuses on the use of Machine Learning to predict the performance of acoustic wave numerical kernel on Intel Xeon Phi many-cores architecture. Low-level hardware counters (e.g. cache-misses and TLB misses) on a limited number of executions are used to build our predictive model. Our results show that performance can be predicted by simulations of hardware counters with high accuracy.