Several Topics - Nov. 19, 2013

[glen herrmannsfeldt]

In comp.lang.fortran E.D.G. <edgrsprj at ix.netcom.com> wrote:
>>> "E.D.G." <edgrsprj at ix.netcom.com> wrote in message 
>>> news:ro-dnch2dPtbRhnPnZ2dnUVZ_rSdnZ2d at earthlink.com...
> Posted by E.D.G. on November 19, 2013
 
> 1.  PERL PDL CALCULATION SPEED VERSUS PYTHON AND FORTRAN
 
(snip)

>       This program translation project has become one of the most 
> surprisingly successful programming projects I have worked on to date.  A 
> considerable amount of valuable information has been sent to me by E-mail in 
> addition to all of the information posted to the Newsgroups.
 
>       The original posts actually discussed calculation speed matters 
> involving Perl and Python.  And responses indicated that there were ways to 
> develop routines that could dramatically accelerate Python calculations. 
> But it did not sound like there were any for Perl.

In general, language processors can be divided into two categories
called compilers and interpreters.  Compilers generate instructions for
the target processors. Interpreters generate (usually) an intermediate
representation which is then interpreted by a program to perform the
desired operations. That latter tends to be much slower, but more
portable.

There are a few langauges that allow dynamic generation of code, which
often makes compilation impossible, and those languages tend to be
called 'interpreted langauges'. 

Some years ago when working with perl programs that ran too slow, we
found a perl to C translator. Surprisingly, the result ran just as slow!
It turns out that the perl to C translator generates a C program
containing the intermediate code and the interpreter, and so runs just
the same speed.

More recently, there are JIT systems which generate the intermediate
code, but then at the appropriate time (Just In Time) compile that to
machine code and execute it. This is common for Java, and more recently
for languages like Matlab.

-- glen