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<p class=MsoPlainText>> From: Ian Stokes-Rees
[mailto:ijstokes@hkl.hms.harvard.edu]</p>
<p class=MsoPlainText>> </p>
<p class=MsoPlainText>> Keep us updated on what you discover on this
front. People have been</p>
<p class=MsoPlainText>> trying this kind of thing in different forms for
quite awhile.</p>
<p class=MsoPlainText><o:p> </o:p></p>
<p class=MsoPlainText>Well, my findings so far are: The purchase cost per
core is approx the same, to buy the zillions of atoms versus buying a smaller
number of xeon etc of equal total compute power, but the density and power
consumption of the atoms is significantly lower (an order of magnitude) thus
yielding a lower total cost of ownership. Figures range from 25% to 50%
lower TCO.<o:p></o:p></p>
<p class=MsoPlainText><o:p> </o:p></p>
<p class=MsoPlainText><o:p> </o:p></p>
<p class=MsoPlainText>> >From a different perspective, (and also *very*
dependent on your</p>
<p class=MsoPlainText>> having a good grasp of the workload
characteristics), if this is a high</p>
<p class=MsoPlainText>> value and long term workload you *may* be able to
benefit from GPUs,</p>
<p class=MsoPlainText>> which effectively are hundreds of slower compute
cores accessible from</p>
<p class=MsoPlainText>> the same system image, but will require a small
computational kernel</p>
<p class=MsoPlainText>> which you can port to a GPU environment.</p>
<p class=MsoPlainText><o:p> </o:p></p>
<p class=MsoPlainText><span style='color:black'>One of our guys is currently
exploring the possibility of porting the present jobs to GPU. It may work
out, but it's fundamentally more difficult to program for a GPU, so it's less
versatile for adaptation to changes of your algorithm, or new
requirements. Thanks for the suggestion...<o:p></o:p></span></p>
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