Multicore Platform Efficiency Across Remote Sensing Applications

A wide range of modern system architectures and platforms targeted for different algorithms and application areas is now available. Even general-purpose systems have advantages in some computation areas and bottlenecks in another. Scientific applications on specific areas, on the other hand, have different requirements for CPU performance, scalability and power consumption. The best practice now is algorithm/architecture co-exploration approach, where scientific problem requirements influence the hardware configuration; on the other hand, algorithm implementation is re factored and optimized in accordance with the platform architectural features. In this research, two typical modules used for multispectral nighttime satellite image processing are studied: • measurement of local perceived sharpness in visible band using the Fourier transform; • cross-correlation in a moving window between visible and infrared bands. Both modules are optimized and studied on wide range of up-to-date testbeds, based on different architectures. Our testbeds include computational nodes based on Intel Xeon E5-2697A v4, Intel Xeon Phi, Texas Instruments Sitara AM5728 dual-core ARM Cortex-A15, and NVIDIA JETSON TX2. The study includes performance testing and energy consumption measurements. The results achieved can be used for assessing serviceability for multispectral nighttime satellite image processing by two key parameters: execution time and energy consumption.