Scattering characteristics of space target in the visible spectrum, which can be used in target detection, target identification, and space docking, is calculated in this paper. Algorithm of scattering characteristics of space target is introduced. In the algorithm, space target is divided into thousands of triangle facets. In order to obtain scattering characteristics of the target, calculation of each facet will be needed. For each facet, calculation will be executed in the spectrum of 400-760 nanometers at intervals of 1 nanometer. Thousands of facets and hundreds of bands of each facet will cause huge calculation, thus the calculation will be very time-consuming. Taking into account the high parallelism of the algorithm, Graphic Processing Units (GPUs) are used to accelerate the algorithm. The acceleration reaches 300 times speedup on single Femi-generation NVDIA GTX 590 as compared to the single-thread CPU version of code on Intel(R) Xeon(R) CPU E5-2620. And a speedup of 412x can be reached when a Kepler-generation NVDIA K20c is used.
The autoregressive modeling image interpolation scheme is noticeably closer to ideal interpolation aiming at obtaining a
high-resolution (HR) image from its low-resolution (LR) version than conventional methods. The basic idea is to first
estimate the covariance of HR image from the covariance of the LR image and then adjust the covariance coefficients of
HR image according to a feedback mechanism that takes into account the mutual influence between the estimated
missing pixels in a local window. In spite of its impressive performance, the time-consuming computation is usually the
bottleneck of the method when it is applied in time-critical scenario. Graphics Processing Units (GPUs) are attractive
candidates to expedite the computation process. In this paper, an efficient GPU-based massively parallel version of the
autoregressive modeling image interpolation scheme was proposed. Because all pixels which need to be interpolated
have no dependence, each estimated pixel is assigned to independent thread in our parallel interpolation scheme.
Experimental results show that we reached a speedup of 21.2x when I/O transfer time was taken into account, with
respect to the original single-threaded C CPU code with the -O2 compiling optimization.