Evaluating scalability of the 2-D FFT on parallel computers

Parallel computers have demonstrated a remarkable potential for achieving high performance at a reasonable cost for many computer vision and image processing (CVIP) applications. A major obstacle to the use of parallel computers is the lack of a universally accepted metric to study the scalability of parallel algorithms and architectures. The authors apply different scalability measures to various 2-D FFT algorithms and target architectures and compare the expected performance to the measured results. A number of algorithms in computer vision and image processing exhibit regular communication patterns similar to the 2-D FFT. The authors can therefore extrapolate the observations to determine which aspects of these measures are relevant to the scalability analysis of other similar image processing algorithms.