The computer science department’s Parallel Processing Laboratory is available solely for the use of computer science majors for instruction and research. To establish regional leadership in parallel processing, Augustana requested an equipped laboratory from the National Science Foundation for academic instruction, together with basic and applied research.
The advent of parallel processing has opened new frontiers in the ability for computers to solve previously intractable problems. Until fairly recently, such parallel processing capabilities were unattainable except through time-sharing arrangements with large universities or government-sponsored research laboratories. Massively parallel computers introduced in the early 1990's are nearly all based on the same, powerful microprocessors that are used in computer workstations.
The Parallel Virtual Machine (PVM) project from Oak Ridge National Laboratories developed a system that enables "a collection of heterogeneous computers to be used as a coherent and flexible concurrent computational resource." This software system can enable the study of parallel algorithms and the simulation of multifarious parallel architectures on a cluster of computer workstations.
The computer science department has established a parallel processing laboratory on its own network switch for the study of parallel algorithms in general, image processing algorithms implemented in parallel, and parallel solutions to computer graphics problems.
The specific research being performed using the equipment is the study of parallel implementation of computer vision and image processing algorithms for the production of real-time, cost-effective image processing systems. Cluster analysis is very computationally intensive, and would benefit greatly by parallel analysis and implementation. This type of unsupervised classification is frequently utilized on data available from the U.S. Geological Survey EROS Data Center, Sioux Falls, S.D., which has a strong tie with Augustana College through a NASA contract to provide logistics and related services to the Data Center. The applications of the unsupervised classification include land cover classification of large raster images data including, for example, the entire Great Plains at a spatial resolution of 1 km, 6 layers of data, over at least 36 time periods.