Abstract

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.

Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call