What is the most cost effective hardware for R?

On Wed, May 9, 2012 at 2:22 PM, John Laing <john.laing at gmail.com> wrote:

For 200,000 analyses at 1.5 seconds each, you're looking at ~83 hours
of computing time. You can buy time from Amazon at roughly $0.08 /
core / hour, so it would cost about $7 to run your analyses in the
cloud. Assuming complete parallelization you could fire up as many
machines as you need to get the work done in as little time as you
want, with the same fixed cost. I think that's a pretty compelling
argument, compared to the hassles of buying and maintaining hardware,
power supply, air conditioning, etc.

?Noticing Hugh's .ac.uk email address you do have to factor in the
hassle of getting something as nebulous as cloud computing past the
red tape. "How much will it cost?" says the bureaucrat. "Depends how
much CPU time I need", says the academic. "So potentially, what's the
most?" says the bureaucrat. "Millions,", says the academic, honestly,
adding "but that would only be if my job scheduling went a bit mad and
grabbed a few thousand Amazon cores and thrashed them for weeks
without me noticing". "Okay", says the bureaucrat, "now, can we send
Amazon a purchase order so that Amazon send us an invoice for this
unknown and potentially unpredictable cost first?". "Oh no", says the
academic, "we need a credit card...".

Maybe there are other ways of paying for Amazon cloud CPUs, I've not
investigated. Anyone in academia happily crunching on EC2?

Barry

I don't work for Amazon, but here is one of their promo pieces on
using 'spot' instances:
http://youtu.be/WD9N73F3Fao

at about 2:15, they cite University of Melbourne and Universitat de
Barcelona as customers...

My interest in all this cloud talk is that I'll be presenting a
tutorial on R in the cloud at R/Finance.
http://www.rinfinance.com/agenda/

It's really easy to use R in the cloud, even if you don't want to move
your data into s3.

-Whit



On Wed, May 9, 2012 at 9:36 AM, Barry Rowlingson
<b.rowlingson at lancaster.ac.uk> wrote:

On Wed, May 9, 2012 at 2:22 PM, John Laing <john.laing at gmail.com> wrote:

For 200,000 analyses at 1.5 seconds each, you're looking at ~83 hours
of computing time. You can buy time from Amazon at roughly $0.08 /
core / hour, so it would cost about $7 to run your analyses in the
cloud. Assuming complete parallelization you could fire up as many
machines as you need to get the work done in as little time as you
want, with the same fixed cost. I think that's a pretty compelling
argument, compared to the hassles of buying and maintaining hardware,
power supply, air conditioning, etc.

?Noticing Hugh's .ac.uk email address you do have to factor in the
hassle of getting something as nebulous as cloud computing past the
red tape. "How much will it cost?" says the bureaucrat. "Depends how
much CPU time I need", says the academic. "So potentially, what's the
most?" says the bureaucrat. "Millions,", says the academic, honestly,
adding "but that would only be if my job scheduling went a bit mad and
grabbed a few thousand Amazon cores and thrashed them for weeks
without me noticing". "Okay", says the bureaucrat, "now, can we send
Amazon a purchase order so that Amazon send us an invoice for this
unknown and potentially unpredictable cost first?". "Oh no", says the
academic, "we need a credit card...".

Maybe there are other ways of paying for Amazon cloud CPUs, I've not
investigated. Anyone in academia happily crunching on EC2?

Barry

Barry, *fortunes* are very auspicious but you are already well represented.

Cheers, Mike.

On Thu, May 10, 2012 at 1:38 AM, Whit Armstrong
<armstrong.whit at gmail.com> wrote:

I don't work for Amazon, but here is one of their promo pieces on
using 'spot' instances:
http://youtu.be/WD9N73F3Fao

at about 2:15, they cite University of Melbourne and Universitat de
Barcelona as customers...

My interest in all this cloud talk is that I'll be presenting a
tutorial on R in the cloud at R/Finance.
http://www.rinfinance.com/agenda/

It's really easy to use R in the cloud, even if you don't want to move
your data into s3.

-Whit



On Wed, May 9, 2012 at 9:36 AM, Barry Rowlingson
<b.rowlingson at lancaster.ac.uk> wrote:

On Wed, May 9, 2012 at 2:22 PM, John Laing <john.laing at gmail.com> wrote:

For 200,000 analyses at 1.5 seconds each, you're looking at ~83 hours
of computing time. You can buy time from Amazon at roughly $0.08 /
core / hour, so it would cost about $7 to run your analyses in the
cloud. Assuming complete parallelization you could fire up as many
machines as you need to get the work done in as little time as you
want, with the same fixed cost. I think that's a pretty compelling
argument, compared to the hassles of buying and maintaining hardware,
power supply, air conditioning, etc.

 Noticing Hugh's .ac.uk email address you do have to factor in the
hassle of getting something as nebulous as cloud computing past the
red tape. "How much will it cost?" says the bureaucrat. "Depends how
much CPU time I need", says the academic. "So potentially, what's the
most?" says the bureaucrat. "Millions,", says the academic, honestly,
adding "but that would only be if my job scheduling went a bit mad and
grabbed a few thousand Amazon cores and thrashed them for weeks
without me noticing". "Okay", says the bureaucrat, "now, can we send
Amazon a purchase order so that Amazon send us an invoice for this
unknown and potentially unpredictable cost first?". "Oh no", says the
academic, "we need a credit card...".

Maybe there are other ways of paying for Amazon cloud CPUs, I've not
investigated. Anyone in academia happily crunching on EC2?

Barry

-- 
Michael Sumner
Institute for Marine and Antarctic Studies, University of Tasmania
Hobart, Australia
e-mail: mdsumner at gmail.com

On May 9, 2012, at 17:46 , Michael Sumner wrote:

Barry, *fortunes* are very auspicious but you are already well represented.

"..as nebulous as cloud computing..", indeed!

Cheers, Mike.

On Thu, May 10, 2012 at 1:38 AM, Whit Armstrong
<armstrong.whit at gmail.com> wrote:

I don't work for Amazon, but here is one of their promo pieces on
using 'spot' instances:
http://youtu.be/WD9N73F3Fao

at about 2:15, they cite University of Melbourne and Universitat de
Barcelona as customers...

My interest in all this cloud talk is that I'll be presenting a
tutorial on R in the cloud at R/Finance.
http://www.rinfinance.com/agenda/

It's really easy to use R in the cloud, even if you don't want to move
your data into s3.

-Whit



On Wed, May 9, 2012 at 9:36 AM, Barry Rowlingson
<b.rowlingson at lancaster.ac.uk> wrote:

On Wed, May 9, 2012 at 2:22 PM, John Laing <john.laing at gmail.com> wrote:

For 200,000 analyses at 1.5 seconds each, you're looking at ~83 hours
of computing time. You can buy time from Amazon at roughly $0.08 /
core / hour, so it would cost about $7 to run your analyses in the
cloud. Assuming complete parallelization you could fire up as many
machines as you need to get the work done in as little time as you
want, with the same fixed cost. I think that's a pretty compelling
argument, compared to the hassles of buying and maintaining hardware,
power supply, air conditioning, etc.

?Noticing Hugh's .ac.uk email address you do have to factor in the
hassle of getting something as nebulous as cloud computing past the
red tape. "How much will it cost?" says the bureaucrat. "Depends how
much CPU time I need", says the academic. "So potentially, what's the
most?" says the bureaucrat. "Millions,", says the academic, honestly,
adding "but that would only be if my job scheduling went a bit mad and
grabbed a few thousand Amazon cores and thrashed them for weeks
without me noticing". "Okay", says the bureaucrat, "now, can we send
Amazon a purchase order so that Amazon send us an invoice for this
unknown and potentially unpredictable cost first?". "Oh no", says the
academic, "we need a credit card...".

Maybe there are other ways of paying for Amazon cloud CPUs, I've not
investigated. Anyone in academia happily crunching on EC2?

Barry

--
Michael Sumner
Institute for Marine and Antarctic Studies, University of Tasmania
Hobart, Australia
e-mail: mdsumner at gmail.com

--
Peter Dalgaard, Professor,
Center for Statistics, Copenhagen Business School
Solbjerg Plads 3, 2000 Frederiksberg, Denmark
Phone: (+45)38153501
Email: pd.mes at cbs.dk ?Priv: PDalgd at gmail.com