Pipelining and superscalar parallel processing are everywhere. We need research to figure out where we need parallel processing to go, before commercial pressures quietly drive us to parallel computing designs that are far less appropriate. From an academic point of view, this is the hardest lesson to swallow. Parallel databases, multimedia, graphics, etc., are larger, more stable, markets than scientific computing. Notice that nCUBE, TMC, and MasPar have all taken this route to save themselves, and SGI is even trying to recast Cray in this light.
Research needs to be focused on supporting a wider range of languages and applications.
It is now far more important to show that a system can effectively support applications with a wide range of characteristics than to show you get good performance from some particular scientific codes. This page was last modified December 17, 1996.