[Numpy-discussion] Numpy question: Best hardware for Numpy?

David Cournapeau cournape@gmail....
Mon Sep 21 09:53:29 CDT 2009


On Mon, Sep 21, 2009 at 8:59 PM, Romain Brette <romain.brette@ens.fr> wrote:
> David Warde-Farley a écrit :
>> On 20-Sep-09, at 2:17 PM, Romain Brette wrote:
>>
>>> Would anyone have thoughts about what the best hardware would be for
>>> Numpy? In
>>> particular, I am wondering about Intel Core i7 vs Xeon. Also, I feel
>>> that the
>>> limiting factor might be memory speed and cache rather than
>>> processor speed.
>>> What do you think?
>>
>>
>> So, there are several different chips that bear the Xeon brand, you'd
>> have to look at individual benchmarks. But if you're concerned about
>> linear algebra performance, I'd say to go with the desktop version and
>> spend some of the money you save on a license for the Intel Math
>> Kernel Library to link NumPy against: http://software.intel.com/en-us/intel-mkl/
>>
>> David
>
> Interesting, I might try Intel MKL. I use mostly element-wise operations
> (e.g. exp(x) or x>x0, where x is a vector), do you think it would make a
> big difference?

It won't make any difference for most operations, at least by default,
as we only support the MKL for BLAS/LAPACK. IF the MKL gives a C99
interface to the math library, it may be possible to tweak the build
process such as to benefit from them.

Concerning the hardware, I have just bought a core i7 (the cheapest
model is ~ 200$ now, with 4 cores and 8 Mb of shared cache), and the
thing flies for floating point computation. My last computer was a
pentium 4 so I don't have a lot of reference, but you can compute ~
300e6 exp (assuming a contiguous array), and ATLAS 3.8.3 built on it
is extremely fast - using the threaded version, the asymptotic peak
performances are quite impressive. It takes for example 14s to inverse
a 5000x5000 matrix of double.

cheers,

David


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