Multicore processors and graphics processing unit accelerators for parallel retrieval of aerosol optical depth from satellite data: Implementation, performance, and energy efficiency

Abstract

Quantitative retrieval is a growing area in remote sensing due to the rapid development of remote instruments and retrieval algorithms. The aerosol optical depth (AOD) is a significant optical property of aerosol which is involved in further applications such as the atmospheric correction of remotely sensed surface features, monitoring of volcanic eruptions or forest fires, air quality, and even climate changes from satellite data. The AOD retrieval can be computationally expensive as a result of huge amounts of remote sensing data and compute-intensive algorithms. In this paper, we present two efficient implementations of an AOD retrieval algorithm from the moderate resolution imaging spectroradiometer (MODIS) satellite data. Here, we have employed two different high performance computing architectures: multicore processors and a graphics processing unit (GPU). The compute unified device architecture C (CUDA-C) has been used for the GPU implementation for NVIDIA's graphic cards and open multiprocessing (OpenMP) for thread-parallelism in the multicore implementation. We observe for the GPU accelerator, a maximal overall speedup of 68.x for the studied data, whereas the multicore processor achieves a reasonable 7.x speedup. Additionally, for the largest benchmark input dataset, the GPU implementation also shows a great advantage in terms of energy efficiency with an overall consumption of 3.15 kJ compared to 58.09 kJ on a CPU with 1 thread and 38.39 kJ with 16 threads. Furthermore, the retrieval accuracy of all implementations has been checked and analyzed. Altogether, using the GPU accelerator shows great advantages for an application in AOD retrieval in both performance and energy efficiency metrics. Nevertheless, the multicore processor provides the easier programmability for the majority of today's programmers. Our work exploits the parallel implementations, the performance, and the energy efficiency features of GPU accelerators and multicore processors. With this paper, we attempt to give suggestions to geoscientists demanding for efficient desktop solutions.