Parallelization of the Ensemble Empirical Model Decomposition (PEEMD) Method on Multi- and Many-core Processors

Cheung, S.1, B.-W. Shen2, P. Mehrotra1 , J.-L. F. Li3 1 NASA Ames Research Center, 2 UMCP/ESSIC, 3CalTech/JPL The trend in high performance computing systems is towards clusters of multi-core nodes; from an 8 cores/node Intel Xeon Harpertown processor in 2008 to the latest Intel Xeon Ivy Bridge processor with 24 cores/node. In addition hardware vendors are developing many core coprocessors, such as NVIDIA's General Purpose Graphics Processing Unit (GPGPU) and Intel's Xeon Phi, in order to get around the constraints of power and frequency. The hybrid nature of such systems presents a major challenge for software developers, in achieving the desired performance. Applications need to be constructed with multiple levels of parallelization along with hybrid communication regimes in order to exploit the power of such systems. The Ensemble Empirical Model Decomposition (EEMD) method has been applied to signal processing on nonlinear and non-stationary data. Due to the ensemble nature of the algorithm and the geographical decomposition of the problem, we have developed a parallel version of the EEMD method with 4-level parallelization, from the grid decomposition level, to time-series level and to the ensemble level using MPI and OpenMP. The parallel EEMD (PEEMD) is being used to analyze Hurricane Sandy (2012) for better understanding of the multiple scale processes that may have impacted Sandy's movement, intensification and formation. In this presentation, we summarize our experiences with the implementation of the PEEMD focusing on the programmability and usability of different processors and accelerators for multiscale analysis for Hurricane Sandy.