Open Access

ARTICLE

A Revised Satellite Cloud-Derived Wind Inversion Algorithm Based on Computer Cluster

Lili He^{1, 2}, Zhiwei Cai^{1, 2}, Dantong Ouyang^{1, 2}, Changshuai Wang^{1, 2}, Yu Jiang^{1, 2}, Chong Wang^{1, 2, 3}, Hongtao Bai^{1, 2, *}

1 College of Computer Science and Technology, Jilin University, Changchun, 130012, China.
2 Symbol Computation and Knowledge Engineer of Ministry of Education, Jilin University, Changchun, 130012, China.
3 Department of Engineering Mechanics, State Marine Technical University of St. Petersburg, St. Petersburg, 190008, Russia.

* Corresponding Author: Hongtao Bai. Email: .

Computers, Materials & Continua 2020, 64(1), 373-388. https://doi.org/10.32604/cmc.2020.09860

Received 22 January 2020; Accepted 30 March 2020; Issue published 20 May 2020

Abstract

In view of the satellite cloud-derived wind inversion has the characteristics of large scale, intensive computing and time-consuming serial inversion algorithm is very difficult to break through the bottleneck of efficiency. We proposed a parallel acceleration scheme of cloud-derived wind inversion algorithm based on MPI cluster parallel technique in this paper. The divide-and-conquer idea, assigning winds vector inversion tasks to each computing unit, is identified according to a certain strategy. Each computing unit executes the assigned tasks in parallel, namely divide-and-rule the inversion task, so as to reduce the efficiency bottleneck of long inversion time caused by serial time accumulation. In the scheme of parallel acceleration based on MPI cluster, an algorithm based on performance prediction is proposed to effectively implement load balance of MPI clusters. Through the comparative analysis of experiment data using the parallel scheme of this parallel technology framework, it shows that this parallel technology has a certain acceleration effect on the cloud-derived wind inversion algorithm. The speedup of the MPI-based parallel algorithm reaches 14.96, which achieved the expected estimate. At the same time, this paper also proposes an efficiency optimization algorithm for cloud-derived wind inversion. In the case that the inversion of wind vector accuracy loss is minimal, the optimized algorithm execution time can be up to 13 times faster.

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