Path: utzoo!utgpu!news-server.csri.toronto.edu!rpi!usc!wuarchive!uwm.edu!linac!mp.cs.niu.edu!ux1.cso.uiuc.edu!usenet
From: mcdonald@aries.scs.uiuc.edu (Doug McDonald)
Newsgroups: comp.arch
Subject: Re: massive linpack
Message-ID: <1991Jun14.140106.18542@ux1.cso.uiuc.edu>
Date: 14 Jun 91 14:01:06 GMT
References: <4112@ssc-bee.ssc-vax.UUCP> <9106070135.AA02947@ucbvax.Berkeley.EDU> <1991Jun8.055711.13457@marlin.jcu.edu.au>
Sender: usenet@ux1.cso.uiuc.edu (News)
Organization: University of Illinois at Urbana
Lines: 22


In article <4112@ssc-bee.ssc-vax.UUCP> carroll@ssc-vax (Jeff Carroll) writes:
>In article <1991Jun8.055711.13457@marlin.jcu.edu.au> csrdh@marlin.jcu.edu.au (Rowan Hughes) writes:
>>
>>I would have thought  (N**2)*C*E was more appropriate.
>>
>>>         Richard Lethin asks:
>>>Aren't really large problems of interest sparse matrices anyway?  Who
>>>holds the record for massive sparse matrix solves?
>>
>>Yes, large problems (N > 10,000) are typically sparse, and with strong
>>diagonal banding. These can only be solved with iterative methods, and
>>only on vector, or large parallel machines. 
>

Eigenvalue matrix problems in computational chemistry are often sparse,
of size 10000x10000 to 10 million by 10 million, not diagonally banded, and
are frequently done on NON-vector, NON-parallel machines (slowly). The
methods are indeed iterative.


Doug McDonald