Energy Prediction of OpenMP Applications Using Random Forest Modeling Approach

Abstract

OpenMP, with its extended parallelism features and support for radically changing HPC architectures, spurred to a surge in developing parallel applications among the HPC application developers community, leading to severe energy consumption issues. Consequently, a notion of addressing the energy consumption issue of HPC applications in an automated fashion increased among compiler developers although the underlying optimization search space could increase tremendously. This paper proposes a Random Forest Modeling (RFM) approach for predicting the energy consumption of OpenMP applications in compilers. The approach was tested using OpenMP applications, such as, NAS benchmarks, matrix multiplication, n-body simulations, and stencil applications while tuning the applications based on energy, problem size, and other performance concerns. The proposed RFM approach predicted the energy consumption of code variants with less than 0.699 Mean Square Error (MSE) and 0.998 R2 value when the testing dataset had energy variations between 0.024 joules and 150.23 joules. In addition, the influences of energy variations, number of independent variables used, and the proportion of testing dataset used during the RFM modeling process are discussed.