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Parallel BRDF-based infrared radiation simulation of aerial targets implemented on Intel Xeon processor and Xeon Phi coprocessor

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Abstract

The infrared (IR) radiance of an aerial target owing to the reflection of the external sources including the sun, atmosphere and the earth’s surface is a key factor to consider in the modeling and simulation of the IR image in the studies of target detection and tracking, guidance and camouflage. Since the radiations of atmosphere and the earth’s surface spread in the whole space and over a wide spectrum, the geometrical shape of targets is complex, and their surfaces are usually non-Lambertian, serial implementation on a CPU platform is time-consuming, and thus, the acceleration of the calculation process is desired in engineering projects. The inherent parallelism that the reflection of radiations incident from different directions in each spectral wavelength can be calculated in parallel in this problem encourages us to accelerate it on multi-core platforms, which are common nowadays. In this work, a dual-socket Intel Xeon E5-2620 nodes running at 2.00 GHz are utilized first. Subsequently, implementations using native and offload modes on the Intel Xeon Phi 5110p coprocessor are described in detail. In both the host-only and Xeon Phi-based implementations, the OpenMP directives are used. Compared to their single-threaded counterpart, the host-only version is 9.7x faster. By increasing the scalability and vectorization, speedups obtained in the native and offload mode implementations were 13.8x and 13.0x, respectively. Our results show that the Xeon Phi’s performance on calculating the target’s reflected radiance of background radiation is promising in the IR image simulation.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant 61775175 and 61571355.

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Correspondence to Jiaji Wu.

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Guo, X., Wu, Z., Wu, J. et al. Parallel BRDF-based infrared radiation simulation of aerial targets implemented on Intel Xeon processor and Xeon Phi coprocessor. J Real-Time Image Proc 16, 49–60 (2019). https://doi.org/10.1007/s11554-017-0739-7

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