[Tutor] On learning Fortran and C++ for scientific computing

[Vick]

Hi,

 

Thanks

 

Vick

 

-----Original Message-----
From: Laura Creighton [mailto:lac at openend.se] 
Sent: Sunday, 12 April, 2015 15:30
To: Vick
Cc: 'Laura Creighton'; 'William Ray Wing'; webmaster at python.org;
tutor at python.org; lac at openend.se
Subject: Re: [Tutor] On learning Fortran and C++ for scientific computing

 

In a message of Sun, 12 Apr 2015 10:25:54 +0400, "Vick" writes:

>S 0 to 1e+31....> 1/sqrt(.000086 *(1+z)^4 + .282 * (1+z)^3 - .000086

>*(1+z)^2   + .718)  if you try this integration you will get completely

>wrong numbers on computing devices that do not possess ultra-high 

>precision and accuracy.

 

Python is as accurate as you can get, every bit as accurate as C++ or
Fortran.

 

>I had to use mpmath with precision of 250 and integration method called 

>Tanh Sinh Quadrature with accuracy 120 to get the desired results. I 

>checked the answer with Mathematica and they concur. But the 

>calculation is done in about 3 seconds on my laptop with intel core i5 at
2.5 GHz with 4 GB RAM.

 

>So can Fortran crunch 250 digits numbers in an integration formula 

>under 3 seconds with the same computing parameters as above? Or is 

>Python better at it?

 

Some of us who were unhappy with the performance of python wrote pypy.

(I am one of the people, so very much biased here.)  PyPy uses a
just-in-time compiler to get its speed.  If you want to look at the speed of
pypy vs the speed of CPython (which is what we call the particular
implementation(s) of python you are using, because it is written in C) you
can take a look at these pages.   <http://speed.pypy.org/>
http://speed.pypy.org/

 

Click on a benchmark, and see the code, and see how much faster we are than
CPython.  Mathematical formulae are what we are very, very good at.  Indeed
we are often better than some old Fortran library -- so we have users who
are busy rewriting their code in Python in order to use pypy for the
increased speed of mathematical computation.  If all you are doing is pure
math -- and not going out, for instance and trying to graph this thing at
the same time -- then we can probably give you speed which is equivalent to
C.  But this assumes that you can write this code where the heavy
calculating is done inside a loop, which is executed many times, because
that is what PyPy optimises. If all you have is straight in-line
executed-one-time-only code, there will be no speed win for you.

 

Also, the jit takes time to warm up.  It has to set up its own internal
bookkeeping before it can get to work on your code.  So if your code doesn't
take very long to run, it may run slower in pypy -- because the savings your
get are competely swallowed up in the setup costs.

 

However, it is easy to find out.  You can get pypy here:

 <http://pypy.org/download.html> http://pypy.org/download.html

 

It's a perfectly compliant, working python.  The version compatible with

3.2.5 is not as speedy as the one compatible with 2.7.  So just run your
python code with this -- it should run completely unchanged -- and see how
fast it is.

 

If you are still unhappy with the performance, post your code to
<mailto:pypy-dev at python.org> pypy-dev at python.org and we can give you
suggestions on how to tune your python code for better performance under
pypy.  If you expose a place where pypy ought to be fast, but isn't, we will
fix pypy and get you a working faster binary.

 

But I think any more talk about pypy belongs on the pypy-dev mailing list.

 

Laura Creighton