[Numpy-discussion] could anyone check on a 32bit system?

[Sebastian Berg]

On Tue, 2013-04-30 at 22:20 -0700, Matthew Brett wrote:
> Hi,
> 
> On Tue, Apr 30, 2013 at 9:16 PM, Matthew Brett <matthew.brett at gmail.com> wrote:
> > Hi,
> >
> > On Tue, Apr 30, 2013 at 8:08 PM, Yaroslav Halchenko
> > <lists at onerussian.com> wrote:
> >> could anyone on 32bit system with fresh numpy (1.7.1) test following:
> >>
> >>> wget -nc http://www.onerussian.com/tmp/data.npy ; python -c 'import numpy as np; data1 = np.load("/tmp/data.npy"); print  np.sum(data1[1,:,0,1]) - np.sum(data1, axis=1)[1,0,1]'
> >>
> >> 0.0
> >>
> >> because unfortunately it seems on fresh ubuntu raring (in 32bit build only,
> >> seems ok in 64 bit... also never ran into it on older numpy releases):
> >>
> >>> python -c 'import numpy as np; data1 = np.load("/tmp/data.npy"); print  np.sum(data1[1,:,0,1]) - np.sum(data1, axis=1)[1,0,1]'
> >> -1.11022302463e-16
> >>
> >> PS detected by failed tests of pymvpa
> >
> > Reduced case on numpy 1.7.1, 32-bit Ubuntu 12.04.2
> >
> > In [64]: data = np.array([[ 0.49505185,  0.47212842],
> >        [ 0.53529587,  0.04366172],
> >        [-0.13461665, -0.01664215]])
> >
> > In [65]: np.sum(data[:, 0]) - np.sum(data, axis=0)[0]
> > Out[65]: 1.1102230246251565e-16
> >
> > No difference for single vector:
> >
> > In [4]: data1 = data[:, 0:1]
> >
> > In [5]: np.sum(data1[:, 0]) - np.sum(data1, axis=0)[0]
> > Out[5]: 0.0
> 
> Also true on current numpy trunk:
> 
> In [2]: import numpy as np
> 
> In [3]: np.__version__
> Out[3]: '1.8.0.dev-a8805f6'
> 
> In [4]: data = np.array([[ 0.49505185,  0.47212842],
>    ....:        [ 0.53529587,  0.04366172],
>    ....:        [-0.13461665, -0.01664215]])
> 
> In [5]: np.sum(data[:, 0]) - np.sum(data, axis=0)[0]
> Out[5]: 1.1102230246251565e-16
> 
> Not true on numpy 1.6.1:
> 
> In [2]: np.__version__
> Out[2]: '1.6.1'
> 
> In [3]: data = np.array([[ 0.49505185,  0.47212842],
>    ....:        [ 0.53529587,  0.04366172],
>    ....:        [-0.13461665, -0.01664215]])
> 
> In [4]: np.sum(data[:, 0]) - np.sum(data, axis=0)[0]
> Out[4]: 0.0
> 

Puzzles me, I didn't think calculation order was different in both cases
and optimized for the reduction part.  But maybe check the code, if it
is optimized, it would calculate this more like `res += data[0]; res +=
data[1]; res += data[2]` (for faster memory access), which would
probably kill the extended registers (I don't know this hardware stuff,
so might be wrong). One simple try hinting that this may be going on
would be to data fortran order.

- Sebastian

> Cheers,
> 
> Matthew
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