Improved Functional Imaging Through Network Based Parallel Processing

This paper deals with currently used algorithms for the reconstruction of functional images which run up to 60 hours or more on a single workstation and deal with hundreds of megabyte of data. A parallel implementation with high efficiency and almost linear speedup of a sophisticated iterative algorithm is given and its applicability to other reconstruction methods is shown. Whereas running this application on a high performance parallel computer is straightforward, there are more issues under production conditions as they are enforced by daily routine in a clinic. We adress the topic of fault tolerant parallelizing and batch queuing of programs which are typically written in a high level language like IDL or MATLAB and show how load balancing can preserve the ownership of workstations in a network of workstations (NOW) which is used for distributed computing during office hours.