Parallel compressive sampling matching pursuit algorithm for compressed sensing signal reconstruction with OpenCL

Abstract

Using the newly computing technology to solve the computing intensive problem in Geo-sciences area.Using OpenCL, the developed parallel algorithm has the capability that can run on different heterogeneous computing platforms, either the NVIDA or AMD GPU-based platform; Using CUDA to develop is not has such crossing-platform capability.In Geo-sciences area, according to our knowledge, there is almost no research that using heterogeneous computing technology to solve the such problem. This work should be helpful to the researchers in this field. Compressive sensing (CS) is a new signal processing method, which was developed recent years. CS can sample signals with a frequency far below the Nyquist frequency. CS can also compress the signals while sampling, which can reduce the usage of resources for signal transmission and storage. However, the reconstruction algorithm used in the corresponding decoder is highly complex and computationally expensive. Thus, in some specific applications, e.g., remote sensing image processing for disaster monitoring, the CS algorithm usually cannot satisfy the time requirements on traditional computing platforms. Various studies have shown that many-core computing platforms such as OpenCL are among the most promising platforms that are available for real-time processing because of their powerful floating-point computing capabilities. In this study, we present the design and implementation of parallel compressive sampling matching pursuit (CoSaMP), which is an OpenCL-based parallel CS reconstruction algorithm, as well as some optimization strategies, such as access efficiency, numerical merge, and instruction optimization. Based on experiments using remote sensing images with different sizes, we demonstrated that the proposed parallel algorithm can achieve speedups of about 41 times and 58 times on AMD HD7350 and NVIDIA K20Xm platforms, respectively, without modifying the application code.