The researcher makes sense of the supercomputer's output data by processing it on a specialized graphics workstation, or experiencing it in a virtual environment . Vector and Parallel Processing All in a Memory Scalable Metacomputing Vector and Parallel Processing Underlying the functional differences in computers are architectural and operational differences as well. Making a metacomputer work requires choosing the best combinations of machines for a given application. Ideally this would be handled automatically through a standardized user interface and software. For time being, though, we'll stick with the older terms.
Vector processors perform numerical calculations much like workers on an assembly line build a car.
A central processor hands the first subunit a piece of data, and that subunit carries out the first mathematical step in the task. The data then gets handed off to the second subunit, and so on, until the calculation is completed. Vector processing is especially well suited for problems that inherently possess well-organized datasets, like calculating how fluids flow . Massively parallel machines can be dramatically faster and tend to possess much greater memory than vector machines, but they tax the programmer, who must figure out how to distribute the workload evenly among the many processors. Technological improvements in microprocessor technology have begun to blur the line between vector and parallel processing.