David Warde-Farley a écrit :
> On 21-Sep-09, at 10:53 AM, David Cournapeau wrote:
>
>> 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
>
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
>> limit
Hi,
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?
Best,
Romain
Hi,
In our project we define a class derived from numpy.float64 (and we add units)
and I noticed that instance creation was very slow. I found out that creating a
float64 object is fast, but creating an object from the derived class is almost
10 times slower, even if that class doesn't do anything
expertise.
Best,
Romain
Romain Brette a écrit :
> Sturla Molden a écrit :
>> Thus, here is my plan:
>>
>> 1. a special context-manager class
>> 2. immutable arrays inside with statement
>> 3. lazy evaluation: expressions build up a parse tree
>> 4. dynamic c
Sturla Molden a écrit :
> Thus, here is my plan:
>
> 1. a special context-manager class
> 2. immutable arrays inside with statement
> 3. lazy evaluation: expressions build up a parse tree
> 4. dynamic code generation
> 5. evaluation on exit
>
There seems to be some similarity with what we want t
David Warde-Farley cs.toronto.edu> writes:
> It did inspire some of our colleagues in Montreal to create this,
> though:
>
> http://code.google.com/p/cuda-ndarray/
>
> I gather it is VERY early in development, but I'm sure they'd love
> contributions!
>
Hi David,
That does look quite
Ian Mallett gmail.com> writes:
>
>
> On Wed, Aug 5, 2009 at 11:34 AM, Charles R Harris
gmail.com> wrote:
>
>
>
> It could be you could slip in a small mod that would do what you want.
> I'll help, if you want. I'm good with GPUs, and I'd appreciate the numerical
power it would afford.
Tha
Charles R Harris gmail.com> writes:
>
> What sort of functionality are you looking for? It could be you could slip in
a small mod that would do what you want. In the larger picture, the use of GPUs
has been discussed on the list several times going back at least a year. The
main problems with us
Hi everyone,
I was wondering if you had any plan to incorporate some GPU support to numpy, or
perhaps as a separate module. What I have in mind is something that would mimick
the syntax of numpy arrays, with a new dtype (gpufloat), like this:
from gpunumpy import *
x=zeros(100,dtype='gpufloat') #
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