CCL:Summary Choice of computer for G03

As promised here is a summary to the following question
 >I'm looking for the ideal computer for running G03. I was wondering what
 >people on the list, when buying a new computer dedicated to Gaussian03,
 >would go fo? Would people go for cpu speed, cache memory, RAM or hard disk.
 >Where would the emphasise be? And what OS would you opt for. I'm sure these
 >questions have been asked for before so if any body can point me to a URL
 >they know, okay.
 >Thanks for your help and time.
 The answers;
 I don't believe you will get definite answers to your question. I've seen
 people on this list using computers of all kinds, single or multi
 processors, Linux, Windows, Mac, etc... running Gaussian. Maybe the most
 important question is what kind of simulations do you want to do on this
 computer?
 Of course, I'd go for the best computer possible, ant that means best CPU,
 with large cache,  maximum RAM and a large HD. Especially, if you plan on
 doing ab-initio calculations on large molecules, you will need lots of HD
 space, and maybe consider a 62 bit processor.
 As for OS, I'd definetely go for Linux, unless you choose a multi-processor
 computer for which Linux isn't available.
 HTH,
           Gustavo Seabra.
 Ian, I have grown fond of Mac's again, since OS X is the only operating
 system where I can do both professional development in C and Python
 alongside Word - all without an emulator and/or dual booting.  The prices
 for dual processors are comparable to what Dell has, if not a little cheaper
 (list prices, YMMV).
 Whatever you get, there are a number of things to consider:
 1)	People forget that there are things like memory bandwidth and
 disk/IO bandwidth.  Thus, they purchase the fastest processor(s) they can,
 and wonder why their jobs don't run as fast as they'd expected.  I'd plan
 for 1 job per 1 or 2 processors for maximum efficiency.  If you have to use
 integral files, I'd investigate fast disks, SCSI, etc.
 2)	Compilers make a HUGE difference.  gcc/g77 are way slower than
 machine-specific compilers (Intel/PC or IBM/Mac).  While it costs a little
 $$, getting 20-50% faster run-time is a cheap upgrade.  Some compilers also
 have difficulties with some parallel libraries, or don't offer them - check
 with Gaussian if there's any good or bad purchases.
 3)	The safe recommendation is that one should get as much memory and
 disk as possible.  See (1) for kinds and/or speeds of disk.  Regarding
 cache, I don't know how particular portions of Gaussian relate to cache
 hits/misses - they should know this.  Of course, running more than a single
 job on the box is quite a bit worse re: cache than differences with a single
 job.  And I think people now feel that machines should NEVER page (some turn
 off the option in Linux), so lots and lots of RAM is required.
 4)	Do you want to be your own system manager?  Do you have access to
 one who is competent?  If not, maybe Linux will be a bother - nothing's real
 good out of the box as I understand things.  I run off-the-shell OS X to
 success.
 5)	Do you need any kind of compatibility with PC/Mac/Linux/...?  Does
 the machine have to have a graphics head or is it merely a server - the
 latter is WAY easier to configure and maintain.  I'm not sure how PC's
 communicate with
 Mac/Linux/Unix, but the latter three seem to talk to each other well.
 Hope this helps...
 Joe
 I've used various versions of Gaussian on various machines at supercomputing
 centers for many years (at least 18 years).  My current favorite machine is
 SGI's itantuim/altix machine.  It is shared memory so gaussian speed-up is
 nearly linear (at least up to 8 processors, as many as I have tried), and
 the 64 bit system makes file size limitations no longer an issue.  I have
 been able to optimizations/frequency calculations on molecules with nearly
 150 atoms, including Re atoms, using good quality DFT methods, as well as
 MP2 calculations that choked other machines I have used.
 Steve Williams
 I hope that helps
 Ian Hovell