```On Feb 28, 2017 2:57 PM, "Sebastian K"
wrote:

Yes it is true the execution time is much faster with the numpy function.

The Code for numpy version:

def createMatrix(n):
Matrix = np.empty(shape=(n,n), dtype='float64')
for x in range(n):
for y in range(n):
Matrix[x, y] = 0.1 + ((x*y)%1000)/1000.0
return Matrix

if __name__ == '__main__':
n = getDimension()
if n > 0:
A = createMatrix(n)
B = createMatrix(n)
C = np.empty(shape=(n,n), dtype='float64')
C = np.dot(A,B)

#print(C)

In the pure python version I am just implementing the multiplication with
three for-loops.

Measured data with libmemusage:
dimension of matrix: 100x100
heap peak pure python3: 1060565
heap peakt numpy function: 4917180

4 megabytes is less than the memory needed just to load numpy :-). Try a
1000x1000 array (or even bigger), and I think you'll see more reasonable
results.

-n
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```

```Thank you! That is the information I needed.

2017-03-01 0:18 GMT+01:00 Matthew Brett :

> Hi,
>
> On Tue, Feb 28, 2017 at 3:04 PM, Sebastian K
>  wrote:
> > Yes you are right. There is no need to add that line. I deleted it. But
> the
> > measured heap peak is still the same.
>
> You're applying the naive matrix multiplication algorithm, which is
> ideal for minimizing memory use during the computation, but terrible
> for speed-related stuff like keeping values in the CPU cache:
>
> https://en.wikipedia.org/wiki/Matrix_multiplication_algorithm
>
> The Numpy version is likely calling into a highly optimized compiled
> routine for matrix multiplication, which can load chunks of the
> matrices at a time, to speed up computation.   If you really need
> minimum memory heap usage and don't care about the order of
> magnitude(s) slowdown, then you might need to use the naive method,
> maybe implemented in Cython / C.
>
> Cheers,
>
> Matthew
> ___
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>
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```

```Hi,

On Tue, Feb 28, 2017 at 3:04 PM, Sebastian K
wrote:
> Yes you are right. There is no need to add that line. I deleted it. But the
> measured heap peak is still the same.

You're applying the naive matrix multiplication algorithm, which is
ideal for minimizing memory use during the computation, but terrible
for speed-related stuff like keeping values in the CPU cache:

https://en.wikipedia.org/wiki/Matrix_multiplication_algorithm

The Numpy version is likely calling into a highly optimized compiled
routine for matrix multiplication, which can load chunks of the
matrices at a time, to speed up computation.   If you really need
minimum memory heap usage and don't care about the order of
magnitude(s) slowdown, then you might need to use the naive method,
maybe implemented in Cython / C.

Cheers,

Matthew
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```

```Yes you are right. There is no need to add that line. I deleted it. But the
measured heap peak is still the same.

2017-03-01 0:00 GMT+01:00 Joseph Fox-Rabinovitz :

> For one thing, `C = np.empty(shape=(n,n), dtype='float64')` allocates 10^4
> extra elements before being immediately discarded.
>
> -Joe
>
> On Tue, Feb 28, 2017 at 5:57 PM, Sebastian K  com> wrote:
>
>> Yes it is true the execution time is much faster with the numpy function.
>>
>>  The Code for numpy version:
>>
>> def createMatrix(n):
>> Matrix = np.empty(shape=(n,n), dtype='float64')
>> for x in range(n):
>> for y in range(n):
>> Matrix[x, y] = 0.1 + ((x*y)%1000)/1000.0
>> return Matrix
>>
>>
>>
>> if __name__ == '__main__':
>> n = getDimension()
>> if n > 0:
>> A = createMatrix(n)
>> B = createMatrix(n)
>> C = np.empty(shape=(n,n), dtype='float64')
>> C = np.dot(A,B)
>>
>> #print(C)
>>
>> In the pure python version I am just implementing the multiplication with
>> three for-loops.
>>
>> Measured data with libmemusage:
>> dimension of matrix: 100x100
>> heap peak pure python3: 1060565
>> heap peakt numpy function: 4917180
>>
>>
>> 2017-02-28 23:17 GMT+01:00 Matthew Brett :
>>
>>> Hi,
>>>
>>> On Tue, Feb 28, 2017 at 2:12 PM, Sebastian K
>>>  wrote:
>>> > For example a very simple algorithm is a matrix multiplication. I can
>>> see
>>> > that the heap peak is much higher for the numpy version in comparison
>>> to a
>>> > pure python 3 implementation.
>>> > The heap is measured with the libmemusage from libc:
>>> >
>>> >   heap peak
>>> >   Maximum of all size arguments of malloc(3), all
>>> products
>>> >   of nmemb*size of calloc(3), all size arguments of
>>> >   realloc(3), length arguments of mmap(2), and new_size
>>> >   arguments of mremap(2).
>>>
>>> Could you post the exact code you're comparing?
>>>
>>> I think you'll find that a naive Python 3 matrix multiplication method
>>> is much, much slower than the same thing with Numpy, with arrays of
>>> any reasonable size.
>>>
>>> Cheers,
>>>
>>> Matthew
>>> ___
>>> NumPy-Discussion mailing list
>>> NumPy-Discussion@scipy.org
>>> https://mail.scipy.org/mailman/listinfo/numpy-discussion
>>>
>>
>>
>> ___
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>> https://mail.scipy.org/mailman/listinfo/numpy-discussion
>>
>>
>
> ___
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>
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```

```For one thing, `C = np.empty(shape=(n,n), dtype='float64')` allocates 10^4
extra elements before being immediately discarded.

-Joe

On Tue, Feb 28, 2017 at 5:57 PM, Sebastian K  wrote:

> Yes it is true the execution time is much faster with the numpy function.
>
>  The Code for numpy version:
>
> def createMatrix(n):
> Matrix = np.empty(shape=(n,n), dtype='float64')
> for x in range(n):
> for y in range(n):
> Matrix[x, y] = 0.1 + ((x*y)%1000)/1000.0
> return Matrix
>
>
>
> if __name__ == '__main__':
> n = getDimension()
> if n > 0:
> A = createMatrix(n)
> B = createMatrix(n)
> C = np.empty(shape=(n,n), dtype='float64')
> C = np.dot(A,B)
>
> #print(C)
>
> In the pure python version I am just implementing the multiplication with
> three for-loops.
>
> Measured data with libmemusage:
> dimension of matrix: 100x100
> heap peak pure python3: 1060565
> heap peakt numpy function: 4917180
>
>
> 2017-02-28 23:17 GMT+01:00 Matthew Brett :
>
>> Hi,
>>
>> On Tue, Feb 28, 2017 at 2:12 PM, Sebastian K
>>  wrote:
>> > For example a very simple algorithm is a matrix multiplication. I can
>> see
>> > that the heap peak is much higher for the numpy version in comparison
>> to a
>> > pure python 3 implementation.
>> > The heap is measured with the libmemusage from libc:
>> >
>> >   heap peak
>> >   Maximum of all size arguments of malloc(3), all
>> products
>> >   of nmemb*size of calloc(3), all size arguments of
>> >   realloc(3), length arguments of mmap(2), and new_size
>> >   arguments of mremap(2).
>>
>> Could you post the exact code you're comparing?
>>
>> I think you'll find that a naive Python 3 matrix multiplication method
>> is much, much slower than the same thing with Numpy, with arrays of
>> any reasonable size.
>>
>> Cheers,
>>
>> Matthew
>> ___
>> NumPy-Discussion mailing list
>> NumPy-Discussion@scipy.org
>> https://mail.scipy.org/mailman/listinfo/numpy-discussion
>>
>
>
> ___
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> https://mail.scipy.org/mailman/listinfo/numpy-discussion
>
>
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```

```Yes it is true the execution time is much faster with the numpy function.

The Code for numpy version:

def createMatrix(n):
Matrix = np.empty(shape=(n,n), dtype='float64')
for x in range(n):
for y in range(n):
Matrix[x, y] = 0.1 + ((x*y)%1000)/1000.0
return Matrix

if __name__ == '__main__':
n = getDimension()
if n > 0:
A = createMatrix(n)
B = createMatrix(n)
C = np.empty(shape=(n,n), dtype='float64')
C = np.dot(A,B)

#print(C)

In the pure python version I am just implementing the multiplication with
three for-loops.

Measured data with libmemusage:
dimension of matrix: 100x100
heap peak pure python3: 1060565
heap peakt numpy function: 4917180

2017-02-28 23:17 GMT+01:00 Matthew Brett :

> Hi,
>
> On Tue, Feb 28, 2017 at 2:12 PM, Sebastian K
>  wrote:
> > For example a very simple algorithm is a matrix multiplication. I can see
> > that the heap peak is much higher for the numpy version in comparison to
> a
> > pure python 3 implementation.
> > The heap is measured with the libmemusage from libc:
> >
> >   heap peak
> >   Maximum of all size arguments of malloc(3), all
> products
> >   of nmemb*size of calloc(3), all size arguments of
> >   realloc(3), length arguments of mmap(2), and new_size
> >   arguments of mremap(2).
>
> Could you post the exact code you're comparing?
>
> I think you'll find that a naive Python 3 matrix multiplication method
> is much, much slower than the same thing with Numpy, with arrays of
> any reasonable size.
>
> Cheers,
>
> Matthew
> ___
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> NumPy-Discussion@scipy.org
> https://mail.scipy.org/mailman/listinfo/numpy-discussion
>
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```

```Hi,

On Tue, Feb 28, 2017 at 2:12 PM, Sebastian K
wrote:
> For example a very simple algorithm is a matrix multiplication. I can see
> that the heap peak is much higher for the numpy version in comparison to a
> pure python 3 implementation.
> The heap is measured with the libmemusage from libc:
>
>   heap peak
>   of nmemb*size of calloc(3), all size arguments of
>   realloc(3), length arguments of mmap(2), and new_size
>   arguments of mremap(2).

Could you post the exact code you're comparing?

I think you'll find that a naive Python 3 matrix multiplication method
is much, much slower than the same thing with Numpy, with arrays of
any reasonable size.

Cheers,

Matthew
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```

```It would really help to see the code you are using in both cases as well as
some heap usage numbers...

-Joe

On Tue, Feb 28, 2017 at 5:12 PM, Sebastian K  wrote:

> For example a very simple algorithm is a matrix multiplication. I can see
> that the heap peak is much higher for the numpy version in comparison to a
> pure python 3 implementation.
> The heap is measured with the libmemusage from libc:
>
>   *heap peak*
>   Maximum of all *size* arguments of malloc(3)
>   of *nmemb***size* of calloc(3)
> , all *size* arguments of
>   realloc(3)
> , *length* arguments of
> mmap(2) , and *new_size*
>   arguments of mremap(2)
> .
>
> Regards
>
> Sebastian
>
>
> On 28 Feb 2017 11:03 p.m., "Benjamin Root"  wrote:
>
>> You are going to need to provide much more context than that. Overhead
>> compared to what? And where (io, cpu, etc.)? What are the size of your
>> arrays, and what sort of operations are you doing? Finally, how much
>>
>> There can be all sorts of reasons for overhead, and some can easily be
>> mitigated, and others not so much.
>>
>> Cheers!
>> Ben Root
>>
>>
>> On Tue, Feb 28, 2017 at 4:47 PM, Sebastian K <
>>
>>> Hello everyone,
>>>
>>> I'm interested in the numpy project and tried a lot with the numpy
>>> array. I'm wondering what is actually done that there is so much overhead
>>> when I call a function in Numpy. What is the reason?
>>>
>>> Regards
>>>
>>> Sebastian Kaster
>>>
>>> ___
>>> NumPy-Discussion mailing list
>>> NumPy-Discussion@scipy.org
>>> https://mail.scipy.org/mailman/listinfo/numpy-discussion
>>>
>>>
>>
>> ___
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>> https://mail.scipy.org/mailman/listinfo/numpy-discussion
>>
>>
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>
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```

```Thank you for your answer.
For example a very simple algorithm is a matrix multiplication. I can see
that the heap peak is much higher for the numpy version in comparison to a
pure python 3 implementation.
The heap is measured with the libmemusage from libc:

*heap peak*
Maximum of all *size* arguments of malloc(3)
of *nmemb***size* of calloc(3)
, all *size*
arguments of
realloc(3)
, *length*
arguments of mmap(2)
, and *new_size*
arguments of mremap(2)
.

Regards

Sebastian

On 28 Feb 2017 11:03 p.m., "Benjamin Root"  wrote:

> You are going to need to provide much more context than that. Overhead
> compared to what? And where (io, cpu, etc.)? What are the size of your
> arrays, and what sort of operations are you doing? Finally, how much
>
> There can be all sorts of reasons for overhead, and some can easily be
> mitigated, and others not so much.
>
> Cheers!
> Ben Root
>
>
> On Tue, Feb 28, 2017 at 4:47 PM, Sebastian K <
>
>> Hello everyone,
>>
>> I'm interested in the numpy project and tried a lot with the numpy array.
>> I'm wondering what is actually done that there is so much overhead when I
>> call a function in Numpy. What is the reason?
>>
>> Regards
>>
>> Sebastian Kaster
>>
>> ___
>> NumPy-Discussion mailing list
>> NumPy-Discussion@scipy.org
>> https://mail.scipy.org/mailman/listinfo/numpy-discussion
>>
>>
>
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>
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```

```You are going to need to provide much more context than that. Overhead
compared to what? And where (io, cpu, etc.)? What are the size of your
arrays, and what sort of operations are you doing? Finally, how much

There can be all sorts of reasons for overhead, and some can easily be
mitigated, and others not so much.

Cheers!
Ben Root

On Tue, Feb 28, 2017 at 4:47 PM, Sebastian K  wrote:

> Hello everyone,
>
> I'm interested in the numpy project and tried a lot with the numpy array.
> I'm wondering what is actually done that there is so much overhead when I
> call a function in Numpy. What is the reason?
>
> Regards
>
> Sebastian Kaster
>
> ___
> NumPy-Discussion mailing list
> NumPy-Discussion@scipy.org
> https://mail.scipy.org/mailman/listinfo/numpy-discussion
>
>
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```