Parallel implementation of synthetic aperture radar on high performance computing platforms

We show a high throughput implementation of SAR on high performance computing (HPC) platforms. In our implementation, the processors are divided into two groups of size M and N. The first group consisting of M processors computes the FDC (frequency domain convolution) in range dimension, and the second group of N processors computes the FDC in azimuth dimension. M and N are determined by the computational requirements of FDC in range and azimuth dimensions respectively. The key contribution of this paper is the development of a general high-throughput M-to-N communication algorithm. The M-to-N communication algorithm is a basic communication primitive used in many signal processing applications when a software task pipeline is employed to obtain high throughput performance. Our algorithm reduces the number of communication steps to 1g(N/M+1)+n(k-1), where k⩾2 and n=[1g_k M]. Implementation results on the IBM SP2 and the Cray T3D based on the MITRE real-time benchmarks are presented. The results show that, given an image of size 1K×1K, the minimum number of processors required for processing the SAR benchmarks can be reduced by 50% by using the proposed communication algorithm