[Numpy-discussion] Numpy question: Best hardware for Numpy?
Mon Sep 21 10:30:04 CDT 2009
Just because I have a ruler handy.... :)
On my laptop with qx9300, I invert that 5000, 5000 double (float64)
matrix in 14.67s.
Granted my cpu cores were all at about 75 degrees during that process..
On Mon, Sep 21, 2009 at 4:53 PM, David Cournapeau <email@example.com> wrote:
> On Mon, Sep 21, 2009 at 8:59 PM, Romain Brette <firstname.lastname@example.org> 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/
>> 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.
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