Hi David
David Mathog wrote:
Although the folks now using CUDA are likely most interested in crunch
per unit time (time efficiency), perhaps some of you have measurements
and can comment on the energy efficiency of GPU vs. CPU computing? That
is, which uses the fewest kilowatts per unit of computation. My guess
Using theoretical rather than "actual" performance, unless you get the
same code doing the same computation on both units:
1 GPU ~ 960 GFLOP single precision, ~100 GFLOP double precision @ 160W
1 CPU ~ 4x (3 GHz x 4 DP flops/cycle) = 48 GFLOP double precision @ 75W
You can argue about these numbers a bit, but these are reasonably close.
This said, any application will not likely be 100% efficient. So
ignoring the efficiency issue, its close to a wash for double precision
FP calculation. Not even close for single precision or integer
calculations though, with the GPU providing far more computing power per
watt than the CPU.
is that the most energy efficient solution is still CPU based, because
Intel and AMD have both worked hard on that issue. Seems like this sort
of information would be useful when comparing CPU and GPU based
solutions, in order to determine the overall cost efficiency (total
crunch /(purchase price + operating expense).
NVidia has recently been marketing these as "replaced a large cluster
with a much smaller one". Then we have seen infrastructure cost
comparisons. These make sense if your applications map onto the Cuda
systems very well (GPU-HMMer), and you can distribute computations
across many of them (MPI-HMMer) at once to realize these performance deltas.
Not all applications will work this way though, so YMMV. And your power
too...
Thanks,
David Mathog
[email protected]
Manager, Sequence Analysis Facility, Biology Division, Caltech
--
Joseph Landman, Ph.D
Founder and CEO
Scalable Informatics LLC,
email: [email protected]
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