Accelerating Geospatial Applications on Hybrid Architectures
Chenggang Lai, Miaoqing Huang, Xuan Shi, Haihang You · 2013
Accelerators have become critical in the process to develop supercomputers with exascale computing capability. In this work, we examine the potential of two latest acceleration technologies, Nvidia K20 Kepler GPU and Intel Many Integrated Core (MIC) Architecture, for accelerating geospatial applications. We first apply a set of benchmarks under 3 different configurations, i.e, MPI+CPU, MPI+GPU, and MPI+MIC. This set of benchmarks include embarrassingly parallel application, loosely communicating application, and intensely communicating application. It is found that the straightforward MPI implementation on MIC cores can achieve the same amount of performance speedup as hybrid MPI+GPU implementation when the same number of processors are used. Further, we demonstrate the potentials of hardware accelerators for advancing the scientific research using an urban sprawl simulation application. The parallel implementation of the urban sprawl simulation using 16 Tesla M2090 GPUs can realize a 155× speedup compared with the single-node implementation, while achieving a good strong scalability.