[Beowulf] dollars-per-teraflop : any lists like the Top500?
Lux, Jim (337C)
james.p.lux at jpl.nasa.gov
Wed Jun 30 06:25:06 PDT 2010
On 6/29/10 10:30 PM, "Rahul Nabar" <rpnabar at gmail.com> wrote:
> The Top500 list has many useful metrics but I didn't see any $$ based
> metrics there. Are there any lists that document the $-per-teraflop
> (apologies to international members!) of any of the systems in the
> Supercomputer / Beowulf world? Googling "dollars per teraflop" didn't
> give me anything useful.
> I'm speculating, one reason could be that sites are loath to disclose
> their exact $ purchase prices etc. But on the other hand for most of
> the publicly owned systems this should be accessible information
It's harder than you think to come up with a "cost" Is it just the hardware
purchase cost? Or do you count the assembly cost? What about infrastructure
mods to hold all those racks?
And assuming you *do* get a number, how do you compare it fairly. For
instance, if you had 100 discount PCs put on the gym floor by volunteer
labor vs buying an already integrated rack?
Do you could integration support?
As far as publically funded ones go.. You might wind up with a big
accounting challenge to go through hundreds of invoices and contracts. In
California, for instance, the CA Public Records Act says you can go and ask
for pretty much any record that doesn't have personally identifiable
information. But that's a long way from getting a nice "here's how much the
supercomputer cost". You might have budgets and invoices from dozens of
firms to go through and find stuff.
> I was just thinking that this might be an interesting
> parameter to track. I was also curious as to when systems become
> larger is there an economy of scale in the Beowulf world? i.e. for
> something like Jaguar or Kraken is the $/teraflop much lower than what
> it is for my tiny 100-node system. Another question could be: Is it
> cheaper to assemble 100 Teraflops of capacity in the US or WU or China
I think it *is* interesting, and would be useful, because managers are
always having to make decisions about make vs buy, or when to buy (do we buy
now and get started, or wait 1 year, when the machines are faster for the
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