

18 hours, $33K, and 156,314 cores: Amazon cloud HPC hits a “petaflop” - tiernano
http://arstechnica.com/information-technology/2013/11/18-hours-33k-and-156314-cores-amazon-cloud-hpc-hits-a-petaflop/

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ChuckMcM
_" There are whole categories of problems that are pleasantly parallel, and in
those cases the [Linpack maximum] number is really not as important because we
did intentionally make use of the entire 1.2 petaflops because they were all
concurrently executing the workloads," Stowe told Ars. "Maybe there does need
to be a different metric for analytics and big data and genomics—and all these
pleasantly parallel workloads that are becoming more pervasive."_

I wonder if people miss this part. For easily parallel projects, Amazon is
great since any core can make progress regardless of the other cores state or
reachability. But if there is any inter-dependence between the cores (I use
the term engtangling but others have called it co-dependence) the number
becomes limited by the bisection bandwidth between cores. Poorly scheduled
(like randomly scheduling on a core) is the worst case, if you can keep the
bandwidth between entangled cores high, even if the system bandwidth is low,
you can improve things (dramatically some times). But at the end of the day
its the level of sharing between cores that will tell you if you can do your
problem in the 'cloud' or if you are going to need a local data center.

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arjie
Interesting to see that 'flops' has been redefined to 'flop' so that 'flops'
now refers to the plural. Also interesting to see that arstechnica hijacks my
back button.

Anyway, the real problem from what I've read in the literature is in problems
that require network IO, but going back now, most of that seems to predate
Amazon's HPC cloud service and even a year ago, some people have found pretty
good scaling there:
[http://www.computer.org/portal/web/computingnow/content?g=53...](http://www.computer.org/portal/web/computingnow/content?g=53319&type=article&urlTitle=massively-
parallel-fluid-simulations-on-amazon-s-hpc-cloud).

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VladRussian2
> including Amazon's cc2.8xlarge instance with 32 cores

they count AWS "vCores" which is 1 HT ( half-core).

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wuschel
Theory is one thing, hands-on experimental scientific evidence another, not
speaking about a possible route of synthesis and purification of polyaromatic
cyclic hydrocarbons.

Does anyone know what problem/model was used for the calculations? I find it
strange that there are many details about the hardware, but no information
about the problem set in the field of organic solar cells.

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res0nat0r
Cycle is a HPC company that configures the software needed for these massively
parallel computations, they don't actually do the protein modeling or any of
the scientific research. That is all supplied from the customer. They
basically setup the environment for the customer to massively "crunch the
numbers."

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cj
What's the advantage of running 150k cores for 18 hours versus far fewer cores
over a period of a few days or weeks? Is efficiency gained with increased
concurrency while processing data in a certain way?

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ceejayoz
If you're going to spend the same amount either way, you might as well get the
results back in a day.

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codex
See also:
[https://news.ycombinator.com/item?id=6720969](https://news.ycombinator.com/item?id=6720969)
for the Google cloud version.

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rlawton
Very cool benchmark.

