Airlines use parallel processing to process customer information, forecast demand and decide what fares to charge. The medical community uses parallel processing supercomputers to analyze MRI images and study models of bone implant systems. For example, think of your database as a phone book and a single processor as a person looking through the phone book for several different pieces of information. If you give a copy of the book to 10 different people and divvy up the research, chances are they'll find all the information you need more quickly than one person working alone would. Switching to parallel processing demands a lot more than just adding processors willy-nilly and sitting back to admire the faster results.
Parallel processing requires different platforms and methods of programming software.
Scientists and engineers schooled in single-processor computation need to learn a whole new way of working if parallel processing is to come into its own.