Parallel Computing Method of Commonly Used Interpolation Algorithms for Remote Sensing Images

Abstract

Parallel computing is a common method to accelerate remote sensing image processing. The paper briefly describes six commonly used interpolation functions and studies three commonly used parallel computing methods of the corresponding nine interpolation algorithms in remote sensing image processing. Firstly, two kinds of general parallel interpolation algorithms (for CPU and GPU respectively) were designed. Then, in two typical application scenarios (data-intensive and computingintensive), four computing methods (one serial method and three parallel methods) of these interpolation algorithms were tested. Finally, the acceleration effects of all parallel algorithms were compared and analyzed. On the whole, the acceleration effect of parallel interpolation algorithm is better in computer-intensive scenario. In CPU-oriented methods, the speedup of all parallel interpolation algorithms mainly depends on the number of physical cores of CPU, while in GPU-oriented methods, a speedup is greatly affected by the computation complexity of an algorithm and the application scenario. GPU has a better acceleration effect on the interpolation algorithms with bigger computation complexity, and has more advantages in the computing-intensive scenarios. In most cases, GPU-based interpolation is ideal for efficient interpolation.

Application of Parallel Computing in the Analysis of Usability Tests, Under the Mouse Tracking Approach

“…Consequently, computational efficiency becomes a challenge in the analysis of mouse tracking tests. Based on the above, it is convenient to take advantage of the benefits in image analysis in different application contexts provided by parallel computing [26], which corresponds to a computing approach that involves the simultaneous execution of multiple tasks or processes, with the objective of improving computational efficiency and performance. Thus, instead of performing one task at a time, as in sequential computing, parallel computing divides complex problems into smaller tasks that can be executed simultaneously on multiple processors or cores [27], [28].…”