Re: [Numpy-discussion] numpy vbench-marking, compiler comparison
Have you tried on an Intel CPU? I have both a i5 quad core and an i7 octo core where I could run it over the weekend. One may expect some compiler magic taking advantage of the advanced features, specially the i7. /David On Nov 25, 2013 8:16 PM, Julian Taylor jtaylor.deb...@googlemail.com wrote: On 25.11.2013 02:32, Yaroslav Halchenko wrote: On Tue, 15 Oct 2013, Nathaniel Smith wrote: What do you have to lose? btw -- fresh results are here http://yarikoptic.github.io/numpy-vbench/ . I have tuned benchmarking so it now reflects the best performance across multiple executions of the whole battery, thus eliminating spurious variance if estimate is provided from a single point in time. Eventually I expect many of those curves to become even cleaner. On another note, what do you think of moving the vbench benchmarks into the main numpy tree? We already require everyone who submits a bug fix to add a test; there are a bunch of speed enhancements coming in these days and it would be nice if we had some way to ask people to submit a benchmark along with each one so that we know that the enhancement stays enhanced... On this positive note (it is boring to start a new thread, isn't it?) -- would you be interested in me transfering numpy-vbench over to github.com/numpy ? as of today, plots on http://yarikoptic.github.io/numpy-vbench should be updating 24x7 (just a loop, thus no time guarantee after you submit new changes). Besides benchmarking new benchmarks (your PRs would still be very welcome, so far it was just me and Julian T) and revisions, that process also goes through a random sample of existing previously benchmarked revisions and re-runs the benchmarks thus improving upon the ultimate 'min' timing performance. So you can see already that many plots became much 'cleaner', although now there might be a bit of bias in estimates for recent revisions since they hadn't accumulated yet as many of 'independent runs' as older revisions. using the vbench I created a comparison of gcc and clang with different options. Cliffnotes: * gcc -O2 performs 5-10% better than -O3 in most benchmarks, except in a few select cases where the vectorizer does its magic * gcc and clang are very close in performance, but the cases where a compiler wins by a large margin its mostly gcc that wins I have collected some interesting plots on this notebook: http://nbviewer.ipython.org/7646615 ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] MKL + CPU, GPU + cuBLAS comparison
Probably a loaded question but is there a significant performance difference between using MKL (or OpenBLAS) on multi-core cpu's and cuBLAS on gpu's. Does anyone have recent experience or link to an independent benchmark? ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] MKL + CPU, GPU + cuBLAS comparison
On Tue, 26 Nov 2013 01:02:40 -0800 Dinesh Vadhia dineshbvad...@hotmail.com wrote: Probably a loaded question but is there a significant performance difference between using MKL (or OpenBLAS) on multi-core cpu's and cuBLAS on gpu's. Does anyone have recent experience or link to an independent benchmark? Using Numpy (Xeon 5520 2.2GHz): In [1]: import numpy In [2]: shape = (450,450,450) In [3]: start=numpy.random.random(shape).astype(complex128) In [4]: %timeit result = numpy.fft.fftn(start) 1 loops, best of 3: 10.2 s per loop Using FFTw (8 threads (2x quad cores): In [5]: import fftw3 In [7]: result = numpy.empty_like(start) In [8]: fft = fftw3.Plan(start, result, direction='forward', flags=['measure'], nthreads=8) In [9]: %timeit fft() 1 loops, best of 3: 887 ms per loop Using CuFFT (GeForce Titan): 1) with 2 transfers: In [10]: import pycuda,pycuda.gpuarray as gpuarray,scikits.cuda.fft as cu_fft,pycuda.autoinit In [11]: cuplan = cu_fft.Plan(start.shape, numpy.complex128, numpy.complex128) In [12]: d_result = gpuarray.empty(start.shape, start.dtype) In [13]: d_start = gpuarray.empty(start.shape, start.dtype) In [14]: def cuda_fft(start): : d_start.set(start) : cu_fft.fft(d_start, d_result, cuplan) : return d_result.get() : In [15]: %timeit cuda_fft(start) 1 loops, best of 3: 1.7 s per loop 2) with 1 transfert: In [18]: def cuda_fft_2(): cu_fft.fft(d_start, d_result, cuplan) return d_result.get() : In [20]: %timeit cuda_fft_2() 1 loops, best of 3: 1.05 s per loop 3) Without transfer: In [22]: def cuda_fft_3(): cu_fft.fft(d_start, d_result, cuplan) pycuda.autoinit.context.synchronize() : In [23]: %timeit cuda_fft_3() 1 loops, best of 3: 202 ms per loop Conclusion: A Geforce Titan (1000€) can be 4x faster than a couple of Xeon 5520 (2x 250€) if your data are already on the GPU. Nota: Plan calculation are much faster on GPU then on CPU. -- Jérôme Kieffer tel +33 476 882 445 ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] MKL + CPU, GPU + cuBLAS comparison
HI GUYS, PLEASE COULD YOU UNSUBSCRIBE ME FROM THESE EMAILS I cant find the link on the bottom Thank-you On 26 November 2013 10:42, Jerome Kieffer jerome.kief...@esrf.fr wrote: On Tue, 26 Nov 2013 01:02:40 -0800 Dinesh Vadhia dineshbvad...@hotmail.com wrote: Probably a loaded question but is there a significant performance difference between using MKL (or OpenBLAS) on multi-core cpu's and cuBLAS on gpu's. Does anyone have recent experience or link to an independent benchmark? Using Numpy (Xeon 5520 2.2GHz): In [1]: import numpy In [2]: shape = (450,450,450) In [3]: start=numpy.random.random(shape).astype(complex128) In [4]: %timeit result = numpy.fft.fftn(start) 1 loops, best of 3: 10.2 s per loop Using FFTw (8 threads (2x quad cores): In [5]: import fftw3 In [7]: result = numpy.empty_like(start) In [8]: fft = fftw3.Plan(start, result, direction='forward', flags=['measure'], nthreads=8) In [9]: %timeit fft() 1 loops, best of 3: 887 ms per loop Using CuFFT (GeForce Titan): 1) with 2 transfers: In [10]: import pycuda,pycuda.gpuarray as gpuarray,scikits.cuda.fft as cu_fft,pycuda.autoinit In [11]: cuplan = cu_fft.Plan(start.shape, numpy.complex128, numpy.complex128) In [12]: d_result = gpuarray.empty(start.shape, start.dtype) In [13]: d_start = gpuarray.empty(start.shape, start.dtype) In [14]: def cuda_fft(start): : d_start.set(start) : cu_fft.fft(d_start, d_result, cuplan) : return d_result.get() : In [15]: %timeit cuda_fft(start) 1 loops, best of 3: 1.7 s per loop 2) with 1 transfert: In [18]: def cuda_fft_2(): cu_fft.fft(d_start, d_result, cuplan) return d_result.get() : In [20]: %timeit cuda_fft_2() 1 loops, best of 3: 1.05 s per loop 3) Without transfer: In [22]: def cuda_fft_3(): cu_fft.fft(d_start, d_result, cuplan) pycuda.autoinit.context.synchronize() : In [23]: %timeit cuda_fft_3() 1 loops, best of 3: 202 ms per loop Conclusion: A Geforce Titan (1000€) can be 4x faster than a couple of Xeon 5520 (2x 250€) if your data are already on the GPU. Nota: Plan calculation are much faster on GPU then on CPU. -- Jérôme Kieffer tel +33 476 882 445 ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] MKL + CPU, GPU + cuBLAS comparison
Jerome, Thanks for the swift response and tests. Crikey, that is a significant difference at first glance. Would it be possible to compare a BLAS computation eg. matrix-vector or matrix-matrix calculation? Thx!___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] MKL + CPU, GPU + cuBLAS comparison
We have such benchmark in Theano: https://github.com/Theano/Theano/blob/master/theano/misc/check_blas.py#L177 HTH Fred On Tue, Nov 26, 2013 at 7:10 AM, Dinesh Vadhia dineshbvad...@hotmail.com wrote: Jerome, Thanks for the swift response and tests. Crikey, that is a significant difference at first glance. Would it be possible to compare a BLAS computation eg. matrix-vector or matrix-matrix calculation? Thx! ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] UNSUBSCRIBE Re: MKL + CPU, GPU + cuBLAS comparison
UNSUBSCRIBE On 26 November 2013 13:37, Frédéric Bastien no...@nouiz.org wrote: We have such benchmark in Theano: https://github.com/Theano/Theano/blob/master/theano/misc/check_blas.py#L177 HTH Fred On Tue, Nov 26, 2013 at 7:10 AM, Dinesh Vadhia dineshbvad...@hotmail.com wrote: Jerome, Thanks for the swift response and tests. Crikey, that is a significant difference at first glance. Would it be possible to compare a BLAS computation eg. matrix-vector or matrix-matrix calculation? Thx! ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] UNSUBSCRIBE Re: MKL + CPU, GPU + cuBLAS comparison
We can't manage your account for you. Click here: http://mail.scipy.org/mailman/listinfo/numpy-discussion to unsunscribe yourself. -paul On Tue, Nov 26, 2013 at 5:42 AM, regikeyz . regi.pub...@gmail.com wrote: UNSUBSCRIBE On 26 November 2013 13:37, Frédéric Bastien no...@nouiz.org wrote: We have such benchmark in Theano: https://github.com/Theano/Theano/blob/master/theano/misc/check_blas.py#L177 HTH Fred On Tue, Nov 26, 2013 at 7:10 AM, Dinesh Vadhia dineshbvad...@hotmail.com wrote: Jerome, Thanks for the swift response and tests. Crikey, that is a significant difference at first glance. Would it be possible to compare a BLAS computation eg. matrix-vector or matrix-matrix calculation? Thx! ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] numpy vbench-marking, compiler comparison
there isn't that much code in numpy that profits from modern x86 instruction sets, even the simple arithmetic loops are strided and thus unvectorizable by the compiler. They have been vectorized manually in 1.8 using sse2 and it is on my todo list to add runtime detected avx support. On 26.11.2013 09:57, Daπid wrote: Have you tried on an Intel CPU? I have both a i5 quad core and an i7 octo core where I could run it over the weekend. One may expect some compiler magic taking advantage of the advanced features, specially the i7. using the vbench I created a comparison of gcc and clang with different options. Cliffnotes: * gcc -O2 performs 5-10% better than -O3 in most benchmarks, except in a few select cases where the vectorizer does its magic * gcc and clang are very close in performance, but the cases where a compiler wins by a large margin its mostly gcc that wins I have collected some interesting plots on this notebook: http://nbviewer.ipython.org/7646615 ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] PyArray_BASE equivalent in python
Hi,
I as the title says, I am looking for a way to set in python the base of
an ndarray to an object.
Use case is porting qimage2ndarray to PySide where I want to do
something like:
In [1]: from PySide import QtGui
In [2]: image =
QtGui.QImage('/home/peter/code/pyTools/sandbox/images/faceDemo.jpg')
In [3]: import numpy as np
In [4]: a = np.frombuffer(image.bits())
-- I would like to do something like:
In [5]: a.base = image
-- to avoid situations such as:
In [6]: del image
In [7]: a
Segmentation fault (core dumped)
The current implementation of qimage2ndarray uses a C function to do
PyArray_BASE(sipRes) = image;
Py_INCREF(image);
But I want to avoid having to install compilers, headers etc on target
machines of my code just for these two lines of code.
Thanks,
P
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Re: [Numpy-discussion] PyArray_BASE equivalent in python
On Tue, Nov 26, 2013 at 11:54 AM, Peter Rennert p.renn...@cs.ucl.ac.uk wrote:
Hi,
I as the title says, I am looking for a way to set in python the base of
an ndarray to an object.
Use case is porting qimage2ndarray to PySide where I want to do
something like:
In [1]: from PySide import QtGui
In [2]: image =
QtGui.QImage('/home/peter/code/pyTools/sandbox/images/faceDemo.jpg')
In [3]: import numpy as np
In [4]: a = np.frombuffer(image.bits())
-- I would like to do something like:
In [5]: a.base = image
-- to avoid situations such as:
In [6]: del image
In [7]: a
Segmentation fault (core dumped)
This is a bug in PySide -- the buffer object returned by image.bits()
needs to hold a reference to the original image. Please report a bug
to them. You will also get a segfault from code that doesn't use numpy
at all, by doing things like:
bits = image.bits()
del image
anything involving the bits object
As a workaround, you can write a little class with an
__array_interface__ attribute that points to the image's contents, and
then call np.asarray() on this object. The resulting array will have
your object as its .base, and then your object can hold onto whatever
references it wants.
-n
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Re: [Numpy-discussion] PyArray_BASE equivalent in python
Brilliant thanks, I will try out the little class approach.
On 11/26/2013 08:03 PM, Nathaniel Smith wrote:
On Tue, Nov 26, 2013 at 11:54 AM, Peter Rennert p.renn...@cs.ucl.ac.uk
wrote:
Hi,
I as the title says, I am looking for a way to set in python the base of
an ndarray to an object.
Use case is porting qimage2ndarray to PySide where I want to do
something like:
In [1]: from PySide import QtGui
In [2]: image =
QtGui.QImage('/home/peter/code/pyTools/sandbox/images/faceDemo.jpg')
In [3]: import numpy as np
In [4]: a = np.frombuffer(image.bits())
-- I would like to do something like:
In [5]: a.base = image
-- to avoid situations such as:
In [6]: del image
In [7]: a
Segmentation fault (core dumped)
This is a bug in PySide -- the buffer object returned by image.bits()
needs to hold a reference to the original image. Please report a bug
to them. You will also get a segfault from code that doesn't use numpy
at all, by doing things like:
bits = image.bits()
del image
anything involving the bits object
As a workaround, you can write a little class with an
__array_interface__ attribute that points to the image's contents, and
then call np.asarray() on this object. The resulting array will have
your object as its .base, and then your object can hold onto whatever
references it wants.
-n
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Re: [Numpy-discussion] PyArray_BASE equivalent in python
I probably did something wrong, but it does not work how I tried it. I
am not sure if you meant it like this, but I tried to subclass from
ndarray first, but then I do not have access to __array_interface__. Is
this what you had in mind?
from PySide import QtGui
import numpy as np
class myArray():
def __init__(self, shape, bits, strides):
self.__array_interface__ = \
{'data': bits,
'typestr': 'i32',
'descr': [('', 'f8')],
'shape': shape,
'strides': strides,
'version': 3}
image = QtGui.QImage('/home/peter/code/pyTools/sandbox/images/faceDemo.jpg')
b = myArray((image.width(), image.height()), image.bits(),
(image.bytesPerLine(), 4))
b = np.asarray(b)
b.base
#read-write buffer ptr 0x7fd744c4b010, size 1478400 at 0x264e9f0
del image
b
# booom #
On 11/26/2013 08:12 PM, Peter Rennert wrote:
Brilliant thanks, I will try out the little class approach.
On 11/26/2013 08:03 PM, Nathaniel Smith wrote:
On Tue, Nov 26, 2013 at 11:54 AM, Peter Rennert p.renn...@cs.ucl.ac.uk
wrote:
Hi,
I as the title says, I am looking for a way to set in python the base of
an ndarray to an object.
Use case is porting qimage2ndarray to PySide where I want to do
something like:
In [1]: from PySide import QtGui
In [2]: image =
QtGui.QImage('/home/peter/code/pyTools/sandbox/images/faceDemo.jpg')
In [3]: import numpy as np
In [4]: a = np.frombuffer(image.bits())
-- I would like to do something like:
In [5]: a.base = image
-- to avoid situations such as:
In [6]: del image
In [7]: a
Segmentation fault (core dumped)
This is a bug in PySide -- the buffer object returned by image.bits()
needs to hold a reference to the original image. Please report a bug
to them. You will also get a segfault from code that doesn't use numpy
at all, by doing things like:
bits = image.bits()
del image
anything involving the bits object
As a workaround, you can write a little class with an
__array_interface__ attribute that points to the image's contents, and
then call np.asarray() on this object. The resulting array will have
your object as its .base, and then your object can hold onto whatever
references it wants.
-n
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Re: [Numpy-discussion] PyArray_BASE equivalent in python
On Tue, Nov 26, 2013 at 9:37 PM, Peter Rennert p.renn...@cs.ucl.ac.uk
wrote:
I probably did something wrong, but it does not work how I tried it. I
am not sure if you meant it like this, but I tried to subclass from
ndarray first, but then I do not have access to __array_interface__. Is
this what you had in mind?
from PySide import QtGui
import numpy as np
class myArray():
def __init__(self, shape, bits, strides):
You need to pass in the image as well and keep a reference to it.
self.__array_interface__ = \
{'data': bits,
'typestr': 'i32',
'descr': [('', 'f8')],
'shape': shape,
'strides': strides,
'version': 3}
Most of these are wrong. Something like the following should suffice:
class QImageArray(object):
def __init__(self, qimage):
shape = (qimage.height(), qimage.width(), -1)
# Generate an ndarray from the image bits and steal its
# __array_interface__ information.
arr = np.frombuffer(qimage.bits(), dtype=np.uint8).reshape(shape)
self.__array_interface__ = arr.__array_interface__
# Keep the QImage alive.
self.qimage = qimage
--
Robert Kern
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Re: [Numpy-discussion] PyArray_BASE equivalent in python
On Tue, Nov 26, 2013 at 1:37 PM, Peter Rennert p.renn...@cs.ucl.ac.uk wrote:
I probably did something wrong, but it does not work how I tried it. I
am not sure if you meant it like this, but I tried to subclass from
ndarray first, but then I do not have access to __array_interface__. Is
this what you had in mind?
from PySide import QtGui
import numpy as np
class myArray():
def __init__(self, shape, bits, strides):
self.__array_interface__ = \
{'data': bits,
'typestr': 'i32',
'descr': [('', 'f8')],
'shape': shape,
'strides': strides,
'version': 3}
You need this object to also hold a reference to the image object --
the idea is that so long as the array lives it will hold a ref to this
object in .base, and then this object holds the image alive. But...
image = QtGui.QImage('/home/peter/code/pyTools/sandbox/images/faceDemo.jpg')
b = myArray((image.width(), image.height()), image.bits(),
(image.bytesPerLine(), 4))
b = np.asarray(b)
b.base
#read-write buffer ptr 0x7fd744c4b010, size 1478400 at 0x264e9f0
...this isn't promising, since it suggests that numpy is cleverly
cutting out the middle-man when you give it a buffer object, since it
knows that buffer objects are supposed to actually take care of memory
management.
You might have better luck using the raw pointer two-tuple form for
the data field. You can't get these pointers directly from a buffer
object, but numpy will give them to you. So you can use something like
data: np.asarray(bits).__array_interface__[data]
-n
--
Nathaniel J. Smith
Postdoctoral researcher - Informatics - University of Edinburgh
http://vorpus.org
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Re: [Numpy-discussion] PyArray_BASE equivalent in python
Btw, I just wanted to file a bug at PySide, but it might be alright at
their end, because I can do this:
from PySide import QtGui
image = QtGui.QImage('/home/peter/code/pyTools/sandbox/images/faceDemo.jpg')
a = image.bits()
del image
a
#read-write buffer ptr 0x7f5fe0034010, size 1478400 at 0x3c1a6b0
On 11/26/2013 09:37 PM, Peter Rennert wrote:
I probably did something wrong, but it does not work how I tried it. I
am not sure if you meant it like this, but I tried to subclass from
ndarray first, but then I do not have access to __array_interface__.
Is this what you had in mind?
from PySide import QtGui
import numpy as np
class myArray():
def __init__(self, shape, bits, strides):
self.__array_interface__ = \
{'data': bits,
'typestr': 'i32',
'descr': [('', 'f8')],
'shape': shape,
'strides': strides,
'version': 3}
image =
QtGui.QImage('/home/peter/code/pyTools/sandbox/images/faceDemo.jpg')
b = myArray((image.width(), image.height()), image.bits(),
(image.bytesPerLine(), 4))
b = np.asarray(b)
b.base
#read-write buffer ptr 0x7fd744c4b010, size 1478400 at 0x264e9f0
del image
b
# booom #
On 11/26/2013 08:12 PM, Peter Rennert wrote:
Brilliant thanks, I will try out the little class approach.
On 11/26/2013 08:03 PM, Nathaniel Smith wrote:
On Tue, Nov 26, 2013 at 11:54 AM, Peter Rennert
p.renn...@cs.ucl.ac.uk wrote:
Hi,
I as the title says, I am looking for a way to set in python the
base of
an ndarray to an object.
Use case is porting qimage2ndarray to PySide where I want to do
something like:
In [1]: from PySide import QtGui
In [2]: image =
QtGui.QImage('/home/peter/code/pyTools/sandbox/images/faceDemo.jpg')
In [3]: import numpy as np
In [4]: a = np.frombuffer(image.bits())
-- I would like to do something like:
In [5]: a.base = image
-- to avoid situations such as:
In [6]: del image
In [7]: a
Segmentation fault (core dumped)
This is a bug in PySide -- the buffer object returned by image.bits()
needs to hold a reference to the original image. Please report a bug
to them. You will also get a segfault from code that doesn't use numpy
at all, by doing things like:
bits = image.bits()
del image
anything involving the bits object
As a workaround, you can write a little class with an
__array_interface__ attribute that points to the image's contents, and
then call np.asarray() on this object. The resulting array will have
your object as its .base, and then your object can hold onto whatever
references it wants.
-n
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Re: [Numpy-discussion] PyArray_BASE equivalent in python
On Tue, Nov 26, 2013 at 2:55 PM, Peter Rennert p.renn...@cs.ucl.ac.uk wrote:
Btw, I just wanted to file a bug at PySide, but it might be alright at
their end, because I can do this:
from PySide import QtGui
image = QtGui.QImage('/home/peter/code/pyTools/sandbox/images/faceDemo.jpg')
a = image.bits()
del image
a
#read-write buffer ptr 0x7f5fe0034010, size 1478400 at 0x3c1a6b0
That just means that the buffer still has a pointer to the QImage's
old memory. It doesn't mean that following that pointer won't crash.
Try str(a) or something that actually touches the buffer contents...
--
Nathaniel J. Smith
Postdoctoral researcher - Informatics - University of Edinburgh
http://vorpus.org
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