Path: utzoo!utgpu!news-server.csri.toronto.edu!cs.utexas.edu!swrinde!zaphod.mps.ohio-state.edu!ub!uhura.cc.rochester.edu!rochester!pt.cs.cmu.edu!gandalf.cs.cmu.edu!lindsay From: lindsay@gandalf.cs.cmu.edu (Donald Lindsay) Newsgroups: comp.arch Subject: Re: Vector vs Cache/Superscalar Message-ID: <13012@pt.cs.cmu.edu> Date: 12 May 91 05:57:59 GMT References: <2646@fornax.UUCP> Organization: Carnegie Mellon Lines: 20 In article <2646@fornax.UUCP> bremner@cs.sfu.ca (David Bremner) writes: >>I think this is partly because the vector model of parallelism is >>so rigid; optimization for the superscalars involves a bigger bag >>of tricks. >I think the vector model is actually quite a bit nicer. There's yet another model - the data parallel one. By this, I mean that one says e.g. "a=5", and 64,000 a's are set. This is obviously the natural programming style of SIMDs like the Connection Machine. - or so I thought, until I heard about the CM Fortran 1.0 compiler from Thinking Machines. It uses the Fortran-90 style of vector operations, e.g. x(1:100:2) = y(1:50) + z(1:50) and (for scientific problems) it seems to be generating faster code than ever before. TMI is claiming that they commonly see 5 GFLOPS, wheras data-parallel Paris was getting more like 1.5 GFLOPS. -- Don D.C.Lindsay Carnegie Mellon Robotics Institute