A case study of OpenCL-based parallel programming for low-power remote sensing applications

Abstract

With the advent of high-performance embedded computing (HPEC) systems, many digital processing algorithms are now implemented by special-purpose massively parallel processors. In this paper, a low-power ARM/GPU co-design architecture is addressed using OpenCL-based parallel programming for implementing complex reconstructive signal processing operations. Such operations are accelerated using data-parallel functions on the GPU and ARM processor, in a HW/SW co-design scheme via OpenCL API calls. Experimental results shows the achieved computational performance and the effectiveness of the OpenCL standard comparing the framework against traditional parallel embedded versions.