Using Multiple GPUs to Accelerate MTF Compensation and Georectification of High-Resolution Optical Satellite Images

Abstract

The rapid growth in the volume of data collected by modern high-resolution optical satellites puts pressure on near real-time processing. In this paper, we present our recent work on the acceleration of modulation transfer function compensation (MTFC) and georectification (GR), two of the most time-consuming optical satellite image processing algorithms, using multiple graphic processing units (multi-GPUs). A tailored strip consisting of 10 ZY-3 nadir images and covering most of the disaster area caused by Typhoon Fitow is used for the experiment (ZY-3 is the first high-accuracy civilian stereo-mapping optical satellite of China). Rapid profiling of the algorithms reveals that compensation and rectification take virtually over 99.50% of the total run times of MTFC and GR. To shorten the time, we port these two operations to a multi-GPU system that consists of an Intel Core i7 CPU and three Fermi-architecture NVIDIA GTX 580 GPUs. First, kernel arrangement and initial settings are determined in the early stage for basic single-GPU implementation. Second, three optimization measures, i.e., maximizing memory throughput, optimizing flow control instructions, and overlapping data transfer and kernel execution, are taken to further improve performance. The experiments achieved significant speedup ratios of 102.9 and 184.2 for MTFC and GR, respectively. Next, two multi-GPU strategies, i.e., cooperative processing (CP) and independent processing (IP), are proposed. The experimental results show that IP is the best option if the number of images to be processed is a multiple of the number of GPUs; otherwise, CP is the best choice. In addition, both the Intel Core i7 and the NVIDIA GTX 580 fully support the IEEE 754-2008 floating-point precision standard; hence, correctness of our GPU implementation can be fully guaranteed.