DARPA benchmark image processing on SIMD parallel machines
The aim of the paper is to present a comparative analysis of the execution times of low-level vision algorithms on two different SIMD parallel machines. The set of algorithms is part of the DARPA Image Understanding benchmark, a widely-accepted platform for performance comparison of parallel systems in the field of computer vision. The considered computer architectures represent two opposite solutions in terms of granularity in approaching the SIMD paradigm, one with a coarse-grain array of floating-point processors and the other with a fine-grain array of single-bit processing elements. For these reasons, the set of algorithms was implemented on both systems taking into account machine specificities. Some insights into implementation issues and a comparative analysis of the assessed execution times are presented.