Path: utzoo!utgpu!news-server.csri.toronto.edu!rpi!zaphod.mps.ohio-state.edu!wuarchive!uunet!stanford.edu!agate!darkstar!ararat!karplus
From: karplus@ararat.ucsc.edu (Kevin Karplus)
Newsgroups: comp.benchmarks
Subject: Re: performance vector approaches
Message-ID: <15082@darkstar.ucsc.edu>
Date: 26 Apr 91 20:37:42 GMT
Expires: 25 May 91 23:00:00 GMT
References: <1991Apr25.174542.100@skyler.mavd.honeywell.com>
Sender: usenet@darkstar.ucsc.edu
Reply-To: karplus@ce.ucsc.edu (Kevin Karplus)
Lines: 22

The idea of trying to reduce a huge set of numbers to a smaller
set that characterize them fairly accurately is not a new one.
The technique is called factor analysis, and is widely used in the
social sciences to try to make sense out of large, noisy data sets.

To apply factor analysis to computer benchmarks you would need:
	1) many different benchmarks, measuring the same or different
		aspects of performance
	2) many different machines on which the benchmarks have been run.

A factor analysis would try to reduce the number of dimensions from
the number of benchmarks down to a smaller number (probably two or three
dimensions, but this will depend on how many are needed to adequately
explain the results).

The hard part is to come up with an explanation of what the "factors"
mean.  Sometimes they correlate particulalry well with one of the
original dimensions, and so can be roughly equated with whatever that
dimension measured.  Having a large number of "pure" tests in the
benchmark set will make explaining factors somewhat easier.

Kevin Karplus