I should perhaps clarify:
The numpy version (which I have nothing to do with) was really written
as a teaching aid, so it is way more object oriented than necessary
for this problem.
It is not really a fair test as I didn't write the code so did not
know how they really compared - which is why I labelled the line
"Python" rather numpy . However the "IDL Loop" code actually uses
loops for _everything_ not a single vectorised operation and the
remarkable fact is that the Python code is a factor of 2 slower even....
J.
On 5 May 2010, at 10:18, Timothy Pickering wrote:
> a few comments:
>
> - if you do 'from numpy import *', you shouldn't also 'import
> math'. those math operators are also provided by numpy for use with
> numpy objects.
>
> - you might get better performance using the sort and interpolate
> routines that numpy provides.
>
> - you're looping through numpy objects using python loops (e.g.
> Cluster.get_acc()). that's like looping through pdl objects with
> perl loops. sloooowwww!
>
> - it would be interesting to see how a direct translation of the
> perldl implementation into numpy would do. basically, take out the
> OO overhead and just write it as a script like perldl and some of
> the others.
>
> in my experience numpy and pdl are pretty similar in speed and quite
> fast when used properly. but in either case if you slip and use
> interpreted loops instead of the vector ops, you'll get hammered.
>
> tim
>
> On May 5, 2010, at 1:01 AM, Jarle Brinchmann wrote:
>
>> Hi folks,
>>
>> I saw some mention at some point about comparisons to other
>> interpreted languages etc, so I thought this might be a fun little
>> tidbit to share:
>>
>> Simon Portegies-Zwart and his group at our department (Leiden) has
>> started an effort to gather N-body codes in various languages to
>> compare. Their web-site is at http://nbabel.org and you can find
>> source-codes and some background information there. One day during
>> Easter I decided to code up the algorithm in a few interpreted
>> languages so I wrote an IDL, PDL and R version of the N-body code.
>> They are all on the web site and none of them are really optimised
>> neither wrt. speed nor length (I did not, intentionally, use PP or
>> link to C routines in IDL or R) - they are really quick hacks.
>>
>> Feel free to improve on the efforts there! However what I thought
>> was interesting is the attached plot - I ran the codes with varying
>> numbers of particles on my trusty Mac laptop and compared execution
>> time (a bit iffy since the overheads are quite different, I used
>> wall-clock timing more or less) for the interpreted codes.
>>
>> What is interesting is that PDL came out as the fastest, closely
>> followed by IDL - I also wrote a version in IDL using loops which
>> is shown as well but I did not post the code on the web-page. I
>> didn't try with pure Perl - would be interesting. What is also
>> intriguing is that the numpy implementation on the web page is
>> quite a lot slower than any of the others. I didn't write that code
>> but it does use vectorisation, so if this is typical for numpy (I
>> think not) it would be bad.
>>
>> Anyway, feel free to improve on this/implement a PDL::PP version
>> for instance - or interface to OpenGL to wow with an evolving
>> stellar cluster with simultaneous 3D graphics. They also seem to
>> have a soft spot for short codes so if you feel particularly keen:
>> see how short you can make the PDL implementation (it can be
>> dramatically shortened from what I put on).
>>
>> Cheers,
>> Jarle.
>>
>>
>> <relative_execution_time.pdf>
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>
> --
> +-------------------------------------------------------------------+
> | T. E. Pickering, Ph.D. | Southern African Large Telescope |
> | SALT Astronomer | SAAO |
> | [email protected] (520) 305-9823 | Observatory Road |
> | [email protected] +27(0)214606284 | 7925 Observatory, South Africa |
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