College of Computer Science and Technology, Jilin University, Changchun, 130012, China. Key Laboratory of Symbolic Computation and Knowledge Engineering, Jilin University, Changchun, 130012, China. Department of Engineering Mechanics, State Marine Technical University of St. Petersburg, St. Petersburg, 190008, Russia.
Abstract:
Cloud-derived wind refers to the wind field data product reversely derived through satellite remote sensing cloud images. Satellite cloud-derived wind inversion has the characteristics of large scale, computationally intensive and long time. The most widely used cloud-derived serial--tracer cloud tracking method is the maximum cross-correlation coefficient (MCC) method. In order to overcome the efficiency bottleneck of the cloud-derived serial MCC algorithm, we proposed a parallel cloud-derived wind inversion algorithm based on GPU framework in this paper, according to the characteristics of independence between each wind vector calculation. In this algorithm, each iteration is considered as a thread of GPU cores, and each thread block array of GPU allocates n*32 threads, and the many thread blocks are allocated to the thread grid. The parameters of the algorithm are passed from CPU to GPU global memory and the storage spaces are previously created on the GPU device before the functions of algorithm are executed. The test results of multiple sets of different inversion models on the NVIDIA Geforce GT and the 4-core 8-thread Core i7-3770 CPU show that the algorithm significantly improves the inversion efficiency. The acceleration ratio is up to 112, and the parallel experiment acceleration ratio is also impressive.
