Re: [python-uk] C is it faster than numpy
> > Hope this was vaguely interesting... > I certainly learnt something, thanks for the time you put into the explanation Michael! ___ python-uk mailing list python-uk@python.org https://mail.python.org/mailman/listinfo/python-uk
Re: [python-uk] C is it faster than numpy
Hi,
On Fri, 25 Feb 2022 at 16:22, BELAHCENE Abdelkader <
abdelkader.belahc...@enst.dz> wrote:
> Hi,
> What do you mean?
> the python and C programs are not equivalent?
>
>
The python and C programs are NOT equivalent. (surface level they are: they
both calculate the triangular number of N, N times, inefficiently and
slowly, but the way they do it is radically different)
Let's specifically compare the num.py and the C versions. The pure python
version *should* always be slower, so it's irrelevant here. (NB, I show a
version below where the pure python version is quicker than both :-) )
The num.py version:
* It creates an array containing the numbers 1 to n. It then call's
num.py's sum function with that array n times.
The C version :
* Calls a function n times. That function has a tight loop using a long
that sums all the numbers from 1 to n.
These are *very* different operations. The former can be vectorised, and
while I don't use num.py, and while I'm not a betting person I would bet a
Mars bar that the num.py version will throw the array at a SIMD
implementation. A cursory look at the source to numpy does indeed show that
this is the case (fx: grabs a mars bar :) ), and not only that it's
optimised for a wide variety of CPU architectures.
https://github.com/numpy/numpy/tree/main/numpy/core/src/common/simd
If you don't know what that means, take a look at this --
https://en.wikipedia.org/wiki/Streaming_SIMD_Extensions numpy however
supports multiple versions of SIMD - including things like this:
https://en.wikipedia.org/wiki/AVX-512 - which is pretty neat.
Upshot - numpy will /essentially/ just throw the entire array at the CPU
and say "add these, give me the result". And it'll be done. (OK there's a
bit more to it, but that's the principle)
By contrast your naive C version has to repeatedly create stack frames,
push arguments onto it, pop arguments allocate memory on the stack, etc.
It's also single threaded, and has no means of telling the CPU "add all
these together, and I don't care how", so it literally is one after the
other. The actual work it's doing to get the same answer is simply much
greater and many many more clock cycles.
If you took the same approach as numpy it's *possible* you *might* get
something similarly fast. (But you'd have a steep learning curve and you'd
likely only optimise for one architecture...)
Aside: What size is the *value* the C version is creating? Well it's just
(n * n+1 ) /2 - primary school triangular number stuff. So 50K
is 1250025000 - which is a 31 bit number. So the C version will fail on a
32 bit machine as soon as you try over 65536 as the argument... (On a
64bit machine admittedly the fall over number is admittedly higher... :-) )
C and C++ *are* faster than pure python. That's pretty much always going to
be the case ( *except* for a specialising python compiler that compiles a
subset of python to either C/C++/Rust or assembler). However, python +
highly optimised C/C++/etc libraries will likely outperform naive C/C++
code - as you've ably demonstrated here.
Why? Because while on the surface the two programs are vaguely similar -
calculate the triangular number of N, N times. In practice, the way that
they do it is so different, you get dramatically different results.
As a bonus - you can get faster than both your num.py and C versions with
pure python, by several orders of magnitude. You can then squeeze out a few
more percent out of it. Output from a pure python version of a piece of
code that performs the same operation - calculates the triangle number of
N, N times:
michael@conceptual:~$ ./triangles.py 1500
time using pure python: 1.81 sec
time using pure python & memoisation: 1.7 sec
Note that's 15 million, not 50,000 - and it took the same time for my
machine (recent core i7) as numpy did for you on your machine for just
50,000. That's not because I have a faster machine (I expect I don't).
What's different? Well the pure python version is this - it recognises you
were calculating the triangular number for N and just calculates that
instead :-)
def normaltriangle(n):
return (n*(n+1))/2
... called like this ..
tm1=it.timeit(stmt=lambda: normaltriangle(count), number=count)
print(f"time using pure python: {round(tm1,2)} sec")
The (naive) memoisation version is this:
def memoise(f):
cache = {}
def g(n):
if n in cache:
return cache[n]
else:
X = f(n)
cache[n] = X
return X
return g
@memoise
def triangle(n):
return (n*(n+1))/2
... called like this ...
tm2=it.timeit(stmt=lambda: triangle(count), number=count)
print(f"time using pure python & memoisation: {round(tm2,2)} sec")
Original file containing both:
#!/usr/bin/python3
import timeit as it
def memoise(f):
cache = {}
def g(n):
if n in cache:
return cache[n]
else:
X = f(n)
cache[n] = X
return X
return g
@memoise
def
Re: [python-uk] C is it faster than numpy
Thanks,
But the different is very significant!!!
so?
Le ven. 25 févr. 2022 à 10:58, Edward Hartley a
écrit :
>
> Hi,
> Simply check the original Numpy aka Numerical Python docs where it’s
> comprehensively explained that the library is implemented in C with a thin
> Python wrapper. The docs were written circa ‘98 by the original library
> author. The library was optimised over a long period before being released.
> I’ve seen similar results when replacing an optimised Fortran library with
> C/C++ the key is the effort given to optimisation, and care with avoiding
> unnecessary memory allocation.
> I’ll dig out the reference when I’ve tracked it down.
> Best of Luck
> Ed Hartley
>
> On 25 Feb 2022, at 09:42, Giorgio Zoppi wrote:
>
>
> Well,
> numpy is written in C :) Maybe your C is not the numpy equivalent?
> Best Regards,
> Giorgio
>
> Il giorno ven 25 feb 2022 alle ore 09:03 BELAHCENE Abdelkader <
> abdelkader.belahc...@enst.dz> ha scritto:
>
>> Hi,
>> a lot of people think that C (or C++) is faster than python, yes I agree,
>> but I think that's not the case with numpy, I believe numpy is faster than
>> C, at least in some cases.
>>
>>
>> *Is there another explanation ?Or where can find a doc speaking about
>> the subject?*Thanks a lot
>> Regards
>> Numpy implements vectorization for arrays, or I'm wrong. Anyway here is
>> an example Let's look at the following case:
>> Here is the result on my laptop i3:
>>
>> Labs$ *python3 tempsExe.py 5*
>> sum with Python: 1250025000 and NumPy 1250025000
>> time used Python Sum: * 37.28 sec *
>> time used Numpy Sum: *1.85 sec*
>>
>> Labs$ *./tt5 *
>>
>>
>> * CPU time :7.521730The value : 1250025000 *
>>
>>
>> This is the Python3 program :
>>
>> import timeit as it
>> import numpy as np
>> import sys
>> try :
>> n=eval(sys.argv[1])
>> except:
>> print ("needs integer as argument") ; exit()
>>
>> a=range(1,n+1)
>> b=np.array(a)
>> def func1(): return sum(a)
>> def func2(): return np.sum(b)
>>
>> print(f"sum with Python: {func1()} and NumPy {func2()} ")
>> tm1=it.timeit(stmt=func1, number=n)
>> print(f"time used Python Sum: {round(tm1,2)} sec")
>> tm2=it.timeit(stmt=func2, number=n)
>> print(f"time used Numpy Sum: {round(tm2,2)} sec")
>>
>> and Here the C program:
>> #include
>> #include
>> #include
>> long func1(int n){
>> long r=0;
>> for (int i=1; i<= n;i++) r+= i;
>> return r;
>> }
>> int main(int argc, char* argv[]){
>> clock_t c0, c1;
>> long v,count; int n;
>>if ( argc < 2) {
>> printf("Please give an argument");
>> return -1;
>> }
>> n=atoi(argv[1]);
>> c0 = clock();
>> *for (int j=0;j < n;j++) v=func1(n);*
>> c1 = clock();
>> printf ("\tCPU time :%.2f sec", (float)(c1 - c0)/CLOCKS_PER_SEC);
>> printf("\n\tThe value : %ld\n", v);
>> }
>> ___
>> python-uk mailing list
>> python-uk@python.org
>> https://mail.python.org/mailman/listinfo/python-uk
>>
>
>
> --
> Life is a chess game - Anonymous.
> ___
> python-uk mailing list
> python-uk@python.org
> https://mail.python.org/mailman/listinfo/python-uk
>
> ___
> python-uk mailing list
> python-uk@python.org
> https://mail.python.org/mailman/listinfo/python-uk
>
___
python-uk mailing list
python-uk@python.org
https://mail.python.org/mailman/listinfo/python-uk
Re: [python-uk] C is it faster than numpy
Hi,
What do you mean?
the python and C programs are not equivalent?
Le ven. 25 févr. 2022 à 10:42, Giorgio Zoppi a
écrit :
> Well,
> numpy is written in C :) Maybe your C is not the numpy equivalent?
> Best Regards,
> Giorgio
>
> Il giorno ven 25 feb 2022 alle ore 09:03 BELAHCENE Abdelkader <
> abdelkader.belahc...@enst.dz> ha scritto:
>
>> Hi,
>> a lot of people think that C (or C++) is faster than python, yes I agree,
>> but I think that's not the case with numpy, I believe numpy is faster than
>> C, at least in some cases.
>>
>>
>> *Is there another explanation ?Or where can find a doc speaking about
>> the subject?*Thanks a lot
>> Regards
>> Numpy implements vectorization for arrays, or I'm wrong. Anyway here is
>> an example Let's look at the following case:
>> Here is the result on my laptop i3:
>>
>> Labs$ *python3 tempsExe.py 5*
>> sum with Python: 1250025000 and NumPy 1250025000
>> time used Python Sum: * 37.28 sec *
>> time used Numpy Sum: *1.85 sec*
>>
>> Labs$ *./tt5 *
>>
>>
>> * CPU time :7.521730The value : 1250025000 *
>>
>>
>> This is the Python3 program :
>>
>> import timeit as it
>> import numpy as np
>> import sys
>> try :
>> n=eval(sys.argv[1])
>> except:
>> print ("needs integer as argument") ; exit()
>>
>> a=range(1,n+1)
>> b=np.array(a)
>> def func1(): return sum(a)
>> def func2(): return np.sum(b)
>>
>> print(f"sum with Python: {func1()} and NumPy {func2()} ")
>> tm1=it.timeit(stmt=func1, number=n)
>> print(f"time used Python Sum: {round(tm1,2)} sec")
>> tm2=it.timeit(stmt=func2, number=n)
>> print(f"time used Numpy Sum: {round(tm2,2)} sec")
>>
>> and Here the C program:
>> #include
>> #include
>> #include
>> long func1(int n){
>> long r=0;
>> for (int i=1; i<= n;i++) r+= i;
>> return r;
>> }
>> int main(int argc, char* argv[]){
>> clock_t c0, c1;
>> long v,count; int n;
>>if ( argc < 2) {
>> printf("Please give an argument");
>> return -1;
>> }
>> n=atoi(argv[1]);
>> c0 = clock();
>> *for (int j=0;j < n;j++) v=func1(n);*
>> c1 = clock();
>> printf ("\tCPU time :%.2f sec", (float)(c1 - c0)/CLOCKS_PER_SEC);
>> printf("\n\tThe value : %ld\n", v);
>> }
>> ___
>> python-uk mailing list
>> python-uk@python.org
>> https://mail.python.org/mailman/listinfo/python-uk
>>
>
>
> --
> Life is a chess game - Anonymous.
> ___
> python-uk mailing list
> python-uk@python.org
> https://mail.python.org/mailman/listinfo/python-uk
>
___
python-uk mailing list
python-uk@python.org
https://mail.python.org/mailman/listinfo/python-uk
Re: [python-uk] C is it faster than numpy
This is a fascinating subject. Firstly: a lot of people think that C (or C++) is faster than python, yes I agree, > if you look at raw execution speed, then this is absolutely correct. The reasons for this are many and complex, but people who use this as a standalone reason to avoid Python almost always don't actually understand the subject too well. If you want to read more about this, I would recommend searching for "python glue language" for example: https://numpy.org/devdocs/user/c-info.python-as-glue.html > *Is there another explanation ?* > Yes, the people who wrote numpy implemented sum() 'better' than you did. That's not a put-down or anything, just an interesting reason why Python as a glue language works so well. Lots of very smart people spend a lot of time making numpy fast, because lots of people rely on it being fast (partly because raw Python is a bit too slow to do these sorts of things natively). In C/C++ it's easy to say: 'it's just a for-loop, let's write it the simple way'. 9 times out of 10, the for loop will be much faster than you ever need it to be in either language, but if you really need speed for whatever reason, you're going to have to reach for a library. Numpy's popularity means that everyone knows to just use it for this sort of thing (interestingly, if you have a GPU, using something like pytorch might be much faster here, depending on data transfer overheads). In C/C++ there are many many libraries that can do this sort of thing, but none have the same general appeal as Numpy (in my opinion). This means that with Python, you write the simple bits in a nice dynamic, easy to write language, and the hard bits get farmed out to libraries like numpy, and you benefit from some really awesome optimized code that make your code faster than the naive equivalent 'fast' C implementation. For example the following are all (i believe) different array sum implementations for different CPU vector features across different architectures/extensions: https://github.com/numpy/numpy/blob/b97e7d585de4b80ae8202c1028c0dc03f5dde4ef/numpy/core/src/common/simd/avx512/arithmetic.h#L353 https://github.com/numpy/numpy/blob/b97e7d585de4b80ae8202c1028c0dc03f5dde4ef/numpy/core/src/common/simd/sse/arithmetic.h#L327 https://github.com/numpy/numpy/blob/b97e7d585de4b80ae8202c1028c0dc03f5dde4ef/numpy/core/src/common/simd/neon/arithmetic.h#L283 It's absolutely possible to make C/C++ perform at the same speed as numpy, but you will have to invest a lot of time/effort in learning about performance programming to get there (this can be a really useful skill to learn, if you're interested!) Some anecdotes: 1. I was using a C data logger library for some data logger device that performed awfully, so I re-wrote it, feature-for-feature in python, and it was 100x faster. Why? Not because Python is faster than C, just that somewhere the original implementation had some bug/issue that was causing slowness (It was related to sqlite transaction handling, but the C code made it very hard to spot the issue, whereas the python was much easier to reason about). Nothing about what this library was doing required high-performance code, so using Python was not a speed issue at all. 2. I worked on a very large data processing application for a fortune 500 co in Python, one part of the input pipeline was never going to be fast enough for our volumes in pure python, so we wrote a custom module in Cython (A hybrid language that compiles to a C/C++ extension), this small module allowed the entire system to perform up to spec, and (along with a numpy-like library) meant we could implement a lot of custom business logic in Python, rather than in less expressive languages, and generally out-perform similar Java/other language systems. Steve ___ python-uk mailing list python-uk@python.org https://mail.python.org/mailman/listinfo/python-uk
Re: [python-uk] C is it faster than numpy
Also interestingly, you can see the language breakdown on the right hand
side of https://github.com/numpy/numpy/ :
Python 62.5%
C 35.3%
C++ 1.0%
Cython 0.9%
Shell 0.2%
Fortran 0.1%
Much of the Python code is tests.
On Fri, Feb 25, 2022 at 9:57 AM Edward Hartley wrote:
>
> Hi,
> Simply check the original Numpy aka Numerical Python docs where it’s
> comprehensively explained that the library is implemented in C with a thin
> Python wrapper. The docs were written circa ‘98 by the original library
> author. The library was optimised over a long period before being released.
> I’ve seen similar results when replacing an optimised Fortran library with
> C/C++ the key is the effort given to optimisation, and care with avoiding
> unnecessary memory allocation.
> I’ll dig out the reference when I’ve tracked it down.
> Best of Luck
> Ed Hartley
>
> On 25 Feb 2022, at 09:42, Giorgio Zoppi wrote:
>
>
> Well,
> numpy is written in C :) Maybe your C is not the numpy equivalent?
> Best Regards,
> Giorgio
>
> Il giorno ven 25 feb 2022 alle ore 09:03 BELAHCENE Abdelkader <
> abdelkader.belahc...@enst.dz> ha scritto:
>
>> Hi,
>> a lot of people think that C (or C++) is faster than python, yes I agree,
>> but I think that's not the case with numpy, I believe numpy is faster than
>> C, at least in some cases.
>>
>>
>> *Is there another explanation ?Or where can find a doc speaking about
>> the subject?*Thanks a lot
>> Regards
>> Numpy implements vectorization for arrays, or I'm wrong. Anyway here is
>> an example Let's look at the following case:
>> Here is the result on my laptop i3:
>>
>> Labs$ *python3 tempsExe.py 5*
>> sum with Python: 1250025000 and NumPy 1250025000
>> time used Python Sum: * 37.28 sec *
>> time used Numpy Sum: *1.85 sec*
>>
>> Labs$ *./tt5 *
>>
>>
>> * CPU time :7.521730The value : 1250025000 *
>>
>>
>> This is the Python3 program :
>>
>> import timeit as it
>> import numpy as np
>> import sys
>> try :
>> n=eval(sys.argv[1])
>> except:
>> print ("needs integer as argument") ; exit()
>>
>> a=range(1,n+1)
>> b=np.array(a)
>> def func1(): return sum(a)
>> def func2(): return np.sum(b)
>>
>> print(f"sum with Python: {func1()} and NumPy {func2()} ")
>> tm1=it.timeit(stmt=func1, number=n)
>> print(f"time used Python Sum: {round(tm1,2)} sec")
>> tm2=it.timeit(stmt=func2, number=n)
>> print(f"time used Numpy Sum: {round(tm2,2)} sec")
>>
>> and Here the C program:
>> #include
>> #include
>> #include
>> long func1(int n){
>> long r=0;
>> for (int i=1; i<= n;i++) r+= i;
>> return r;
>> }
>> int main(int argc, char* argv[]){
>> clock_t c0, c1;
>> long v,count; int n;
>>if ( argc < 2) {
>> printf("Please give an argument");
>> return -1;
>> }
>> n=atoi(argv[1]);
>> c0 = clock();
>> *for (int j=0;j < n;j++) v=func1(n);*
>> c1 = clock();
>> printf ("\tCPU time :%.2f sec", (float)(c1 - c0)/CLOCKS_PER_SEC);
>> printf("\n\tThe value : %ld\n", v);
>> }
>> ___
>> python-uk mailing list
>> python-uk@python.org
>> https://mail.python.org/mailman/listinfo/python-uk
>>
>
>
> --
> Life is a chess game - Anonymous.
> ___
> python-uk mailing list
> python-uk@python.org
> https://mail.python.org/mailman/listinfo/python-uk
>
> ___
> python-uk mailing list
> python-uk@python.org
> https://mail.python.org/mailman/listinfo/python-uk
>
___
python-uk mailing list
python-uk@python.org
https://mail.python.org/mailman/listinfo/python-uk
Re: [python-uk] C is it faster than numpy
Hi,
Simply check the original Numpy aka Numerical Python docs where it’s
comprehensively explained that the library is implemented in C with a thin
Python wrapper. The docs were written circa ‘98 by the original library author.
The library was optimised over a long period before being released. I’ve seen
similar results when replacing an optimised Fortran library with C/C++ the key
is the effort given to optimisation, and care with avoiding unnecessary memory
allocation.
I’ll dig out the reference when I’ve tracked it down.
Best of Luck
Ed Hartley
> On 25 Feb 2022, at 09:42, Giorgio Zoppi wrote:
>
>
> Well,
> numpy is written in C :) Maybe your C is not the numpy equivalent?
> Best Regards,
> Giorgio
>
>> Il giorno ven 25 feb 2022 alle ore 09:03 BELAHCENE Abdelkader
>> ha scritto:
>> Hi,
>> a lot of people think that C (or C++) is faster than python, yes I agree,
>> but I think that's not the case with numpy, I believe numpy is faster than
>> C, at least in some cases.
>> Is there another explanation ?
>> Or where can find a doc speaking about the subject?
>> Thanks a lot
>> Regards
>> Numpy implements vectorization for arrays, or I'm wrong. Anyway here is an
>> example Let's look at the following case:
>> Here is the result on my laptop i3:
>>
>> Labs$ python3 tempsExe.py 5
>> sum with Python: 1250025000 and NumPy 1250025000
>> time used Python Sum: 37.28 sec
>> time used Numpy Sum: 1.85 sec
>>
>> Labs$ ./tt5
>> CPU time :7.521730
>> The value : 1250025000
>>
>>
>> This is the Python3 program :
>>
>> import timeit as it
>> import numpy as np
>> import sys
>> try :
>> n=eval(sys.argv[1])
>> except:
>> print ("needs integer as argument") ; exit()
>>
>> a=range(1,n+1)
>> b=np.array(a)
>> def func1(): return sum(a)
>> def func2(): return np.sum(b)
>>
>> print(f"sum with Python: {func1()} and NumPy {func2()} ")
>> tm1=it.timeit(stmt=func1, number=n)
>> print(f"time used Python Sum: {round(tm1,2)} sec")
>> tm2=it.timeit(stmt=func2, number=n)
>> print(f"time used Numpy Sum: {round(tm2,2)} sec")
>>
>> and Here the C program:
>> #include
>> #include
>> #include
>> long func1(int n){
>> long r=0;
>> for (int i=1; i<= n;i++) r+= i;
>> return r;
>> }
>> int main(int argc, char* argv[]){
>> clock_t c0, c1;
>> long v,count; int n;
>>if ( argc < 2) {
>> printf("Please give an argument");
>> return -1;
>> }
>> n=atoi(argv[1]);
>> c0 = clock();
>> for (int j=0;j < n;j++) v=func1(n);
>> c1 = clock();
>> printf ("\tCPU time :%.2f sec", (float)(c1 - c0)/CLOCKS_PER_SEC);
>> printf("\n\tThe value : %ld\n", v);
>> }
>> ___
>> python-uk mailing list
>> python-uk@python.org
>> https://mail.python.org/mailman/listinfo/python-uk
>
>
> --
> Life is a chess game - Anonymous.
> ___
> python-uk mailing list
> python-uk@python.org
> https://mail.python.org/mailman/listinfo/python-uk
___
python-uk mailing list
python-uk@python.org
https://mail.python.org/mailman/listinfo/python-uk
Re: [python-uk] C is it faster than numpy
Yeah it uses BLAS, you can read about it here
https://markus-beuckelmann.de/blog/boosting-numpy-blas.html
On Fri, 25 Feb 2022, 09:54 SW, wrote:
> I think it uses fortran (or used to?) but yes, still close to the metal.
>
> Thanks,
> S
>
> On 25/02/2022 10:41, Giorgio Zoppi wrote:
>
> Well,
> numpy is written in C :) Maybe your C is not the numpy equivalent?
> Best Regards,
> Giorgio
>
> Il giorno ven 25 feb 2022 alle ore 09:03 BELAHCENE Abdelkader <
> abdelkader.belahc...@enst.dz> ha scritto:
>
>> Hi,
>> a lot of people think that C (or C++) is faster than python, yes I agree,
>> but I think that's not the case with numpy, I believe numpy is faster than
>> C, at least in some cases.
>>
>>
>> *Is there another explanation ? Or where can find a doc speaking about
>> the subject? *Thanks a lot
>> Regards
>> Numpy implements vectorization for arrays, or I'm wrong. Anyway here is
>> an example Let's look at the following case:
>> Here is the result on my laptop i3:
>>
>> Labs$ *python3 tempsExe.py 5*
>> sum with Python: 1250025000 and NumPy 1250025000
>> time used Python Sum: * 37.28 sec *
>> time used Numpy Sum: *1.85 sec*
>>
>> Labs$ *./tt5 *
>>
>>
>> * CPU time :7.521730 The value : 1250025000 *
>>
>>
>> This is the Python3 program :
>>
>> import timeit as it
>> import numpy as np
>> import sys
>> try :
>> n=eval(sys.argv[1])
>> except:
>> print ("needs integer as argument") ; exit()
>>
>> a=range(1,n+1)
>> b=np.array(a)
>> def func1(): return sum(a)
>> def func2(): return np.sum(b)
>>
>> print(f"sum with Python: {func1()} and NumPy {func2()} ")
>> tm1=it.timeit(stmt=func1, number=n)
>> print(f"time used Python Sum: {round(tm1,2)} sec")
>> tm2=it.timeit(stmt=func2, number=n)
>> print(f"time used Numpy Sum: {round(tm2,2)} sec")
>>
>> and Here the C program:
>> #include
>> #include
>> #include
>> long func1(int n){
>> long r=0;
>> for (int i=1; i<= n;i++) r+= i;
>> return r;
>> }
>> int main(int argc, char* argv[]){
>> clock_t c0, c1;
>> long v,count; int n;
>>if ( argc < 2) {
>> printf("Please give an argument");
>> return -1;
>> }
>> n=atoi(argv[1]);
>> c0 = clock();
>> *for (int j=0;j < n;j++) v=func1(n);*
>> c1 = clock();
>> printf ("\tCPU time :%.2f sec", (float)(c1 - c0)/CLOCKS_PER_SEC);
>> printf("\n\tThe value : %ld\n", v);
>> }
>> ___
>> python-uk mailing list
>> python-uk@python.org
>> https://mail.python.org/mailman/listinfo/python-uk
>>
>
>
> --
> Life is a chess game - Anonymous.
>
> ___
> python-uk mailing
> listpython-uk@python.orghttps://mail.python.org/mailman/listinfo/python-uk
>
>
> ___
> python-uk mailing list
> python-uk@python.org
> https://mail.python.org/mailman/listinfo/python-uk
>
___
python-uk mailing list
python-uk@python.org
https://mail.python.org/mailman/listinfo/python-uk
Re: [python-uk] C is it faster than numpy
I think it uses fortran (or used to?) but yes, still close to the metal.
Thanks,
S
On 25/02/2022 10:41, Giorgio Zoppi wrote:
Well,
numpy is written in C :) Maybe your C is not the numpy equivalent?
Best Regards,
Giorgio
Il giorno ven 25 feb 2022 alle ore 09:03 BELAHCENE Abdelkader
ha scritto:
Hi,
a lot of people think that C (or C++) is faster than python, yes I
agree, but I think that's not the case with numpy, I believe numpy
is faster than C, at least in some cases.
*Is there another explanation ?
Or where can find a doc speaking about the subject?
*Thanks a lot
Regards
Numpy implements vectorization for arrays, or I'm wrong. Anyway
here is an example Let's look at the following case:
Here is the result on my laptop i3:
Labs$ *python3 tempsExe.py 5*
sum with Python: 1250025000 and NumPy 1250025000
time used Python Sum: *37.28 sec *
time used Numpy Sum: *1.85 sec*
Labs$ *./tt 5 *
* CPU time :7.521730
The value : 1250025000
*
This is the Python3 program :
import timeit as it
import numpy as np
import sys
try :
n=eval(sys.argv[1])
except:
print ("needs integer as argument") ; exit()
a=range(1,n+1)
b=np.array(a)
def func1(): return sum(a)
def func2(): return np.sum(b)
print(f"sum with Python: {func1()} and NumPy {func2()} ")
tm1=it.timeit(stmt=func1, number=n)
print(f"time used Python Sum: {round(tm1,2)} sec")
tm2=it.timeit(stmt=func2, number=n)
print(f"time used Numpy Sum: {round(tm2,2)} sec")
and Here the C program:
#include
#include
#include
long func1(int n){
long r=0;
for (int i=1; i<= n;i++) r+= i;
return r;
}
int main(int argc, char* argv[]){
clock_t c0, c1;
long v,count; int n;
if ( argc < 2) {
printf("Please give an argument");
return -1;
}
n=atoi(argv[1]);
c0 = clock();
*for (int j=0;j < n;j++) v=func1(n);*
c1 = clock();
printf ("\tCPU time :%.2f sec", (float)(c1 -
c0)/CLOCKS_PER_SEC);
printf("\n\tThe value : %ld\n", v);
}
___
python-uk mailing list
python-uk@python.org
https://mail.python.org/mailman/listinfo/python-uk
--
Life is a chess game - Anonymous.
___
python-uk mailing list
python-uk@python.org
https://mail.python.org/mailman/listinfo/python-uk
___
python-uk mailing list
python-uk@python.org
https://mail.python.org/mailman/listinfo/python-uk
Re: [python-uk] C is it faster than numpy
Well,
numpy is written in C :) Maybe your C is not the numpy equivalent?
Best Regards,
Giorgio
Il giorno ven 25 feb 2022 alle ore 09:03 BELAHCENE Abdelkader <
abdelkader.belahc...@enst.dz> ha scritto:
> Hi,
> a lot of people think that C (or C++) is faster than python, yes I agree,
> but I think that's not the case with numpy, I believe numpy is faster than
> C, at least in some cases.
>
>
> *Is there another explanation ?Or where can find a doc speaking about
> the subject?*Thanks a lot
> Regards
> Numpy implements vectorization for arrays, or I'm wrong. Anyway here is an
> example Let's look at the following case:
> Here is the result on my laptop i3:
>
> Labs$ *python3 tempsExe.py 5*
> sum with Python: 1250025000 and NumPy 1250025000
> time used Python Sum: * 37.28 sec *
> time used Numpy Sum: *1.85 sec*
>
> Labs$ *./tt5 *
>
>
> * CPU time :7.521730The value : 1250025000 *
>
>
> This is the Python3 program :
>
> import timeit as it
> import numpy as np
> import sys
> try :
> n=eval(sys.argv[1])
> except:
> print ("needs integer as argument") ; exit()
>
> a=range(1,n+1)
> b=np.array(a)
> def func1(): return sum(a)
> def func2(): return np.sum(b)
>
> print(f"sum with Python: {func1()} and NumPy {func2()} ")
> tm1=it.timeit(stmt=func1, number=n)
> print(f"time used Python Sum: {round(tm1,2)} sec")
> tm2=it.timeit(stmt=func2, number=n)
> print(f"time used Numpy Sum: {round(tm2,2)} sec")
>
> and Here the C program:
> #include
> #include
> #include
> long func1(int n){
> long r=0;
> for (int i=1; i<= n;i++) r+= i;
> return r;
> }
> int main(int argc, char* argv[]){
> clock_t c0, c1;
> long v,count; int n;
>if ( argc < 2) {
> printf("Please give an argument");
> return -1;
> }
> n=atoi(argv[1]);
> c0 = clock();
> *for (int j=0;j < n;j++) v=func1(n);*
> c1 = clock();
> printf ("\tCPU time :%.2f sec", (float)(c1 - c0)/CLOCKS_PER_SEC);
> printf("\n\tThe value : %ld\n", v);
> }
> ___
> python-uk mailing list
> python-uk@python.org
> https://mail.python.org/mailman/listinfo/python-uk
>
--
Life is a chess game - Anonymous.
___
python-uk mailing list
python-uk@python.org
https://mail.python.org/mailman/listinfo/python-uk
