In this paper we examine the Single Program Multiple Data (SPMD) parallel implementation of image classification algorithms on a cluster comprised of personal computers. The small-scale cluster environment employed utilizes two quite different Application Programming Interfaces (APls) for inter-process communications, message passing and virtual shared memory. We quantitatively compare both of these communication approaches in conjunction with a small scale cluster for medical image classification by presenting the SPMD parallelization of three well-known context-independent image classification algorithms:Nearest Mean, Maximum Likelihood and K Nearest Neighbors. These classic approaches are applied to massive medical images and the resulting average speedup using both message passing and virtual shared memory inter-process communications is presented.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.