Python is far from a top performer according to benchmark test...

[Carl]

"Nine Language Performance Round-up: Benchmarking Math & File I/O"
http://www.osnews.com/story.php?news_id=5602

I think this is an unfair comparison! I wouldn't dream of developing a
numerical application in Python without using prebuilt numerical libraries
and data objects such as dictionaries and lists.

I have been experimenting with numerical algorithms in Python with a heavy
use of the Numeric module. My experience is that Python is quite fast in
comparison with (and sometimes as fast as) traditional languages such as C
or C++.

The greatest advantage of Python is the great increase in productivity and
the generation of a much smaller number of bugs due to the very clean and
compact structure Python invites you to produce. Sometimes it amazes me how
fast I can produce a working algorithm in Python. The step from an
algorithmic outline on a paper to a working code is very short. The
interactive nature of the Python console invites numerical experimentation
and data exploration. This wasn't mentioned in the article, what a pity!

Carl

[Irmen de Jong]

Carl wrote:

"Nine Language Performance Round-up: Benchmarking Math & File I/O"
http://www.osnews.com/story.php?news_id=5602

This benchmark is beaten to pulp in the discussion about it on slashdot.
It's a real stupid benchmark (as most benchmarks are) IMNSHO.

I mean, using python's arbitrary precision long object for 'math'
and comparing it to the other languages' long /machine types/... come on.
And thats just one of the flaws.

--Irmen

[Krzysztof Stachlewski]

"Carl" <ph***********@chello.se> wrote in message
news:ryELb.238$tK2.228@amstwist00...

I have been experimenting with numerical algorithms in Python with a heavy
use of the Numeric module. My experience is that Python is quite fast in
comparison with (and sometimes as fast as) traditional languages such as C
or C++.

With "heavy use of Numeric module" you were calling functions
written in C. So how can you say that Python is fast,
when C code is doing all the work.

Stach

[Carl]

Krzysztof Stachlewski wrote:

"Carl" <ph***********@chello.se> wrote in message
news:ryELb.238$tK2.228@amstwist00...

I have been experimenting with numerical algorithms in Python with a
heavy use of the Numeric module. My experience is that Python is quite
fast in comparison with (and sometimes as fast as) traditional languages
such as C or C++.

With "heavy use of Numeric module" you were calling functions
written in C. So how can you say that Python is fast,
when C code is doing all the work.

Stach

Well, I guess you are right!

What I meant was that when I run my Python algorithm it runs "almost as fast
as" when I run similar code in C or C++. This is of course due to the
highly efficient Numeric module. However, the point is that Python is a
viable language from a numerical perspective.

Carl

[Samuel Walters]

|Thus Spake Krzysztof Stachlewski On the now historical date of Fri, 09
Jan 2004 22:13:58 +0100|

"Carl" <ph***********@chello.se> wrote in message
news:ryELb.238$tK2.228@amstwist00...

I have been experimenting with numerical algorithms in Python with a
heavy use of the Numeric module. My experience is that Python is quite
fast in comparison with (and sometimes as fast as) traditional languages
such as C or C++.

With "heavy use of Numeric module" you were calling functions written in
C. So how can you say that Python is fast, when C code is doing all the
work.

Because python works best as a glue layer coordinating outside libraries
and functionality. I think the true strength of python comes from a
one-two punch of "Fast and pretty implementation with easy interface to
lower level tools." When python is not the right tool, we code our
solution with the right tool and then use python to glue all the right
tools together. For numerical processing, C is the right tool, but python
is not. Therefore, noone tried to use the wrong tool, they just used the
right tool and gave it python bindings so that python could act as a
coordinator.

I read the benchmark and I think it doesn't measure python in it's target
area. That's like taking a world-class marathon runner and wondering why
he doesn't compete well in a figure-skating event.

Sam Walters.
--
Never forget the halloween documents.
http://www.opensource.org/halloween/
""" Where will Microsoft try to drag you today?
Do you really want to go there?"""

[JanC]

"Krzysztof Stachlewski" <st***@fr.USUN.pl> schreef:

With "heavy use of Numeric module" you were calling functions
written in C. So how can you say that Python is fast,
when C code is doing all the work.

I think all (or at least most) of the tested compilers, VMs, etc. were
written in C/C++, and thus are using libraries written in C/C++...

--
JanC

"Be strict when sending and tolerant when receiving."
RFC 1958 - Architectural Principles of the Internet - section 3.9

[Iwan van der Kleyn]

Carl wrote:

I think this is an unfair comparison! I wouldn't dream of developing a
numerical application in Python without using prebuilt numerical libraries
and data objects such as dictionaries and lists.
Well, that may be true. And many applications spend most of their time
in fast libraries anyway (GUI/DB/app servers) etc. Nice examples are Boa
Constructor and Eric3 which are fully implemented in Python and run
comfortably fast, thanks to the WxWindows and Qt libraries.

But I really dont' swallow the argument. In the few years that I'm
working with Python, I've encountered on several occasions performance
bottlenecks. And often a solution through partial implementation in C or
improvements in the algorithm is just not feasible. To paraphrase Kent
Beck: in real life you make your program run, then work and then your
manager says you've run out of time. You just cannot make it run as fast
as you would like it as well.

In other words: it would be nice if Python on average would run faster
so the need for optimisation would lessen. Psyco is a nice option to
have, though not as clear cut as I thought it would be:

http://www-106.ibm.com/developerwork...y/l-psyco.html
The greatest advantage of Python is the great increase in productivity and
the generation of a much smaller number of bugs due to the very clean and
compact structure Python invites you to produce.

So dogma dictates. And I've found it to be true on many occasions, if
not all. BUT, the famed Python Productivity Gain is very difficult to
quantify. And for me that's a BIG but. I'm trying to push Python within
my company. Nicely presented "performance benchmarks" go down well with
management, bevause those are quantities which are supposedly
understood. And Python does not come across very possitively in that
respect.

Regards,

Iwan

[Tim Churches]

On Sat, 2004-01-10 at 08:05, Carl wrote:

"Nine Language Performance Round-up: Benchmarking Math & File I/O"
http://www.osnews.com/story.php?news_id=5602

I think this is an unfair comparison! I wouldn't dream of developing a
numerical application in Python without using prebuilt numerical libraries
and data objects such as dictionaries and lists.

Benchmarks like those reported are nearly worthless, but nevertheless,
it would be interesting to re-run the them using Numeric Python and/or
Pyrex.

I notice that the author of those benchmarks, Christopher W.
Cowell-Shah, has a PhD in philosophy. Perhaps Python's very own
philosophy PhD, David Mertz, might like to repeat the benchmarking
exercise for one of his columns, but including manipulation of more
realistic data structures such as lists, arrays and dictionaries, as
Carl suggests. I'm sure it would be a popular article, and provide a
counterpoint to the good Dr Mertz's previous articles on Psyco.

--

Tim C

PGP/GnuPG Key 1024D/EAF993D0 available from keyservers everywhere
or at http://members.optushome.com.au/tchur/pubkey.asc
Key fingerprint = 8C22 BF76 33BA B3B5 1D5B EB37 7891 46A9 EAF9 93D0

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5bvT3a5t1NQfLnpvPex3KF8=
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[Jeff Epler]

numarray is probably the perfect example of getting extremely good
performance from a few simple constructs coded in C. (numarray-0.8 is
less then 50k lines of C code, including comments and blank lines, so
it's also very simple given the amount of "bang" it provides)

All your important logic is in Python, you're in no way stuck worrying
about buffer lengths, when to call free(), and all those other things
that drive me crazy when I try to write in C.

If I had to execute my Python programs without executing any code
written in "C" behind the scenes, well, I'd be stuck. Of course, the
situation is about the same for any language you care to name, and if
not then substitute "machine code".

Jeff

[Tim Churches]

On Sat, 2004-01-10 at 08:13, Krzysztof Stachlewski wrote:

"Carl" <ph***********@chello.se> wrote in message
news:ryELb.238$tK2.228@amstwist00...

I have been experimenting with numerical algorithms in Python with a heavy
use of the Numeric module. My experience is that Python is quite fast in
comparison with (and sometimes as fast as) traditional languages such as C
or C++.

With "heavy use of Numeric module" you were calling functions
written in C. So how can you say that Python is fast,
when C code is doing all the work.

Well, yes, but the Python VM is also written in C, and every time you
make a call to a dictionary, or list etc, it is C code which is doing
all the work. If you like, you could say that Python is a set of
extremely clever wrappers around a bunch of optimised C code - but that
rather diminishes the achievement of Python. I dare say that the
Microsoft .NET and Visual Basic VMs are also written in C/C++, so you
could say the same things about them - but I don't think that is a
useful perspective.
--

Tim C

PGP/GnuPG Key 1024D/EAF993D0 available from keyservers everywhere
or at http://members.optushome.com.au/tchur/pubkey.asc
Key fingerprint = 8C22 BF76 33BA B3B5 1D5B EB37 7891 46A9 EAF9 93D0

-----BEGIN PGP SIGNATURE-----
Version: GnuPG v1.0.7 (GNU/Linux)

iD8DBQA//yKZeJFGqer5k9ARApbeAKC0SnajS1CQ/b6w9m+BfBLtLMeptwCg9s+y
szgtzQnlsRc3cYBsNf0T7Wc=
=9rbb
-----END PGP SIGNATURE-----

[JanC]

Tim Churches <tc***@optushome.com.au> schreef:

I notice that the author of those benchmarks, Christopher W.
Cowell-Shah, has a PhD in philosophy. Perhaps Python's very own
philosophy PhD, David Mertz, might like to repeat the benchmarking
exercise for one of his columns, but including manipulation of more
realistic data structures such as lists, arrays and dictionaries, as
Carl suggests.

And then don't forget to publish it on /. or nobody sees it... ;-)

--
JanC

"Be strict when sending and tolerant when receiving."
RFC 1958 - Architectural Principles of the Internet - section 3.9

[Peter Hansen]

Iwan van der Kleyn wrote:

In other words: it would be nice if Python on average would run faster
so the need for optimisation would lessen.

I disagree with the above. My opinion has long been that Python runs
adequately fast and that few people should need to spend much time on
optimization. Maybe that sort of view should be put to the test.

This is my "straw poll" question:

Do you spend a "significant" amount of time actually optimizing your
Python applications? (Significant is here defined as "more than five
percent of your time", which is for example two hours a week in a
40-hour work week.)

Note the distinction between "actually optimizing" and "worrying about
optimization" and such things. If you pause briefly during coding and
rewrite a line to use a more efficient idiom, I don't consider that to be
"optimization" for purposes of this question. Optimization would require
roughly (a) noticing that performance was inadequate or actual profiling
your code, and (b) rewriting specifically to get adequate performance.
Algorithmic improvements that you would make regardless of implementation
language do not qualify, and wasting time optimizing a script that you
run once a year so it takes ten seconds instead of fifteen also does not
qualify because you certainly didn't need to do it...

Yes or no answers suffice, but feel free to follow up with a paragraph
qualifying your answer (or quantifying it!). :-)

-Peter

[Paul Rubin]

Peter Hansen <pe***@engcorp.com> writes:

This is my "straw poll" question:

Do you spend a "significant" amount of time actually optimizing your
Python applications? (Significant is here defined as "more than five
percent of your time", which is for example two hours a week in a
40-hour work week.)
Yes, absolutely.
Algorithmic improvements that you would make regardless of implementation
language do not qualify, and wasting time optimizing a script that you
run once a year so it takes ten seconds instead of fifteen also does not
qualify because you certainly didn't need to do it...

Sometimes I'll take the time to implement a fancy algorithm in Python
where in a faster language I could use brute force and still be fast
enough. I'd count that as an optimization.

[Dave Brueck]

Iwan:

The greatest advantage of Python is the great increase in productivity and
the generation of a much smaller number of bugs due to the very clean and
compact structure Python invites you to produce.

So dogma dictates. And I've found it to be true on many occasions, if
not all. BUT, the famed Python Productivity Gain is very difficult to
quantify. And for me that's a BIG but. I'm trying to push Python within
my company. Nicely presented "performance benchmarks" go down well with
management, bevause those are quantities which are supposedly
understood.

Understood, perhaps, but very often irrelevent. That being the case, using
performance benchmarks to argue your case is a weak approach.

If you're talking to management, talk to them about something they care about,
like money. For most programs it's hard to translate performance improvements
into money: e.g. it's hard to assert that by doubling the speed of your
spell-checker implementation that sales will increase. There of course are
exceptions, but even then there's no guarantee that management would still
prefer performance above other factors if given a choice.

It's much more powerful to speak about reduced time to market, for example. Or
the ability to compete against companies with legions of programmers. Or the
decreased time it takes to turn ideas into implemented features (especially
when it's your competitor that came up with the idea). Or a lower cost of
changing directions technologically. Etc.

-Dave

[Dave Brueck]

Peter wrote:

Iwan van der Kleyn wrote:

In other words: it would be nice if Python on average would run faster
so the need for optimisation would lessen.

I disagree with the above. My opinion has long been that Python runs
adequately fast and that few people should need to spend much time on
optimization. Maybe that sort of view should be put to the test.

This is my "straw poll" question:

Do you spend a "significant" amount of time actually optimizing your
Python applications? (Significant is here defined as "more than five
percent of your time", which is for example two hours a week in a
40-hour work week.)

Yay, straw poll! ;-)

I program in Python full-time and each spend approximately zero hours
optimizing. In the past two years I can think of two instances in which I went
into heavy optimization mode: one was for a web server that needed to handle
hundreds of requests per second and the other was a log processor that needed
to parse and process several gigabytes of log data per hour.

In the server I added a tiny C extension to make use of the Linux sendfile API,
all the other optimizations were algorithmic. In the log processor all the
optimizations ended up being either algorithmic or doing fewer dumb things
(like recomputing cacheable data).

[Krzysztof Stachlewski]

"JanC" <us*********@janc.invalid> wrote in message
news:Xn******************@213.119.4.35...

"Krzysztof Stachlewski" <st***@fr.USUN.pl> schreef:

I think all (or at least most) of the tested compilers, VMs, etc. were
written in C/C++, and thus are using libraries written in C/C++...

Well, yes.
Whether or not you can say that a piece of code
is *really* implemented in a chosen language and not
in "the language that this language is implemented in" ;-)
is a matter of scale.

I just think that the Numeric package
is not the best example of the speed of Python itself.

Stach

[Mike C. Fletcher]

Dave Brueck wrote:

Peter wrote:

....

This is my "straw poll" question:

Do you spend a "significant" amount of time actually optimizing your
Python applications? (Significant is here defined as "more than five
percent of your time", which is for example two hours a week in a
40-hour work week.)

Yay, straw poll! ;-)

Indeed, yay ;) .

I am often found optimising. The total time spent on it is probably
somewhere around 1-2% of total (after all, I do still *use* Python, it's
not like it's killing me). A lot of my projects are speed-sensitive
(e.g. OpenGLContext trying to give interactive frame-rates in 3D
scenegraph rendering, SimpleParse trying to create really fast parsers,
web-sites trying to process 10s of thousands of records into summary
views interactively), but I don't think I'm *that* far from the norm in
the amount of time spent on optimisation.

I'd be surprised if there's not some difference between the experience
of library programmers and application programmers in this regard.
Library developers tend to need to focus more effort on performance to
allow users of the libraries some inefficiencies in their usage. That
effort tends to be more in the planning stages, though, making sure
you've chosen good algorithms, estimating work-loads and required
throughput. You try to make sure the library is robust and fast by
designing it that way, not by trying to optimise it after the fact. You
plan for worst-case scenarios, and as a result you become "worried"
about performance. An application developer is normally going to use
optimised libraries and just run with it, only needing to deal with
unacceptable performance if it happens to show up in their particular
project.

Anyway, back to it,
Mike

_______________________________________
Mike C. Fletcher
Designer, VR Plumber, Coder
http://members.rogers.com/mcfletch/

[Rene Pijlman]

Mike C. Fletcher:

I am often found optimising.

By an unoptimized or by an optimizing coworker? :-)

--
René Pijlman

[Mike C. Fletcher]

Rene Pijlman wrote:

Mike C. Fletcher:

I am often found optimising.

By an unoptimized or by an optimizing coworker? :-)

Well, in a perfect world, it would be by a lovely and svelte lady,
missing her husband and seeking to drag me off to connubial bliss (we'll
assume for propriety's sake that the husband would be me), but in my sad
and lonely condition, it's normally I who finds myself late at night,
feverishly optimising ;) :) .

All a man really needs to be happy is work, and a woman to drag him away
from it, or so I'm told,
Mike(y)

_______________________________________
Mike C. Fletcher
Designer, VR Plumber, Coder
http://members.rogers.com/mcfletch/

[Jp Calderone]

On Fri, Jan 09, 2004 at 05:09:37PM -0500, Peter Hansen wrote:

Iwan van der Kleyn wrote:

In other words: it would be nice if Python on average would run faster
so the need for optimisation would lessen.

I disagree with the above. My opinion has long been that Python runs
adequately fast and that few people should need to spend much time on
optimization. Maybe that sort of view should be put to the test.

This is my "straw poll" question:

Do you spend a "significant" amount of time actually optimizing your
Python applications? (Significant is here defined as "more than five
percent of your time", which is for example two hours a week in a
40-hour work week.)

No.

Jp

[Ken]

> Do you spend a "significant" amount of time actually optimizing your

Python applications? (Significant is here defined as "more than five
percent of your time", which is for example two hours a week in a
40-hour work week.)

Some of them.

I'm using python for data transformations: some feeds are small and
easily handled by python, however the large ones (10 million rows per
file) require a bit of thought to be spent on performance. However,
this isn't exactly python optimization - more like shifting high-level
pieces around in the architecture: merge two files or do a binary
lookup (nested-loop-join) one one? etc...

To make matters worse we just implemented a metadata-driven
transformation engine entirely written in python. It'll work great on
the small files, but the large ones...

Luckily, the nature of this application lends itself towards
distributed processing - so my plan is to:
1. check out psycho for the metadata-driven tool
2. partition the feeds across multiple servers
3. rewrite performance-intensive functions in c

But I think I'll get by with just options #1 and #2: we're using
python and it's working well - exactly because it is so adaptable.
The cost in performance is inconsequential in this case compared to
the maintainability.

[Terry Reedy]

"Krzysztof Stachlewski" <st***@fr.USUN.pl> wrote in message
news:bt**********@absinth.dialog.net.pl...

"Carl" <ph***********@chello.se> wrote in message
news:ryELb.238$tK2.228@amstwist00...
With "heavy use of Numeric module" you were calling functions
written in C. So how can you say that Python is fast,
when C code is doing all the work.
Well gee. *All* of the functions exposed in builtins *and* in built-in
modules are also written in C. So are all the methods of builtin types and
all the hidden functions (some exposed in the C API), including the
compilation and interpretation. So how can anyone even talk about the
speed of Python, when C code is doing all the work, whether quickly or
slowly!

[and in another post]I just think that the Numeric package is not the best example
of the speed of Python itself.

But what is 'Python' itself? I think you are making a false distinction.
Numerical Python and other scientific code driving C and Fortran functions
was a, if not the killer app for Python when I learned it about 7 years
ago. It was so important to the early success of Python, such as it was,
that the slice object was added just for its use.

Terry J. Reedy

[Brian Kelley]

> This is my "straw poll" question:

Do you spend a "significant" amount of time actually optimizing your
Python applications? (Significant is here defined as "more than five
percent of your time", which is for example two hours a week in a
40-hour work week.)

Yes and No :) I find that optimizing algorithms is a lot more
beneficial than optimizing code. Let me give a small example. I have
written a chemoinformatics engine (http://frowns.sourceforge.net/) and
one of it's features is substructure searching. That is finding if a
graph is embedded in another graph. This is an NP-Complete problem. A
company recently compared their technique in terms of speed and
correctness to frowns. According to them, frowns was 99% percent
correct but 1000x slower. (Why they were marketing their system which
was built over 5+ man years against mine which was built over 3 months I
never will understand)

Now, 1000x is a *lot* slower. However, when used in practice in a
database setting, my system has a quick mechanism that can reject false
matches very quickly. This is standard practice for chemistry databases
by the way. All of a sudden the 1000x difference becomes almost
meaningless. For a given search across 300000+ compounds, my system
takes 1.2 seconds and their's takes 25 minutes. Using my prefiltering
scheme their system takes 0.7 seconds. Now my code didn't change at
all, only the way it was used changed.

I could, of course, generate an example that takes me much longer but
the average case is a whole lot better. My system is free though, so my
users tend not to mind (or quite honestly, expect) as much :)

Brian

[Lothar Scholz]

Samuel Walters <sw*************@yahoo.com> wrote in message news:<pa****************************@yahoo.com>...

For numerical processing, C is the right tool,

Definitely not, you don't want a pointer language when using numerical
processing: use Fortran.

[Sean 'Shaleh' Perry]

On Friday 09 January 2004 14:09, Peter Hansen wrote:

Yes or no answers suffice, but feel free to follow up with a paragraph
qualifying your answer (or quantifying it!). :-)

not any longer.

As I learned on this and the tutor list, writing code in Pythonic style tends
to also result in code being fast enough. Most of my early problems resulted
from trying to write C in Python.

[Tim Delaney]

From: "Peter Hansen" <pe***@engcorp.com>

This is my "straw poll" question:

Do you spend a "significant" amount of time actually optimizing your
Python applications? (Significant is here defined as "more than five
percent of your time", which is for example two hours a week in a
40-hour work week.)

I have to say that I do, but I'm also dealing with datasets up to about
500MB in the worst case - but about 10-20MB in the normal case.

In most cases, the optimisations are things like only doing a single pass
over any file, etc. A naive prototype often doesn't scale well when I need
to deal with large datasets. Often using psyco will be sufficient to get me
over the hurdle (see the psyco thread) but sometimes not.

Tim Delaney

[Skip Montanaro]

QOTW perhaps?

Sam> I read the benchmark and I think it doesn't measure python in it's
Sam> target area. That's like taking a world-class marathon runner and
Sam> wondering why he doesn't compete well in a figure-skating event.

Skip

[Samuel Walters]

|Thus Spake Lothar Scholz On the now historical date of Fri, 09 Jan 2004
21:29:56 -0800|

Samuel Walters <sw*************@yahoo.com> wrote in message
news:<pa****************************@yahoo.com>...

For numerical processing, C is the right tool,

Definitely not, you don't want a pointer language when using numerical
processing: use Fortran.

Hmm. I feel misunderstood. I'm going to try to clarify, but if I'm the
one doing the misunderstanding, feel free to give me a good old-fashioned
usenet style bitchslapping back to the stone age.

First off: Truth in advertising.
I know very little about numeric processing, and even less about Fortran.
It's true that my background is in mathematics, but in *pure* mathematics
where pointer-based languages tend to be helpful, not hurtful. I chose
pure mathematics precisely because it eschews the grubby sort of shortcuts
that applied mathematics uses. In other words, I didn't have the proper
sort of mathematical intuition for it, so I chose pure, where my kind of
intuition works well. (In the end, this was to my detriment. All the
interesting problems are in applied math!)

As I see it, when one considers which language is best for one's needs,
one considers a couple of things:

1) Does the language have the semantics I want.
2) Does the language have the primitives I need.
3) Can I *easily* build any missing or suboptimal primitives.

One would assume that Fortran has the proper semantics for numerical
processing because it seems to have been wildly successful for a long
period of time. It would appear that Python has the proper semantics for
numerical processing because a significant number of people are using it
for that, and they'd be using something else if Python caused them too
many headaches.

Fortran naturally comes with the primitives for numerical processing,
because numerical processing is its stated goal. ("FORmula TRANslation")
Python doesn't seem to have the native and optimal primitives for
numerical processing, so that leads to point three.

Whether one uses Fortran, Python, or any other language, all primitives
are eventually implemented in either C or assembly. At some point or
another, we end up scraping bare metal and silicon to get our answers.
The question then becomes, "How easily can I extend this language to fit
my needs." NumPy is evidence that at least a few people said "Easily
enough." I don't know how extensible Fortran is, but I would guess "very"
since I've heard of it being applied in many domains other than numerical
processing. (OpenFirmware, for example.)

So, I guess that my point is that C might not be the right language for
doing numerical processing, but it seems the right language for
implementing the primitives of numerical processing. Those primitives
should, of course, be designed in such a manner that their behaviors are
not muddied by pointer issues.

Moving on:
I think Python's strength flows from the three criterion for choosing a
language. It's semantics seem to naturally fit the way a programmer
thinks about problems. All the algorithmic primitives are there for
naturally expressing one's self easily. Where the primitives don't exist,
it's easy to bind outside primitives into the system seamlessly. One of
the joy's of python is that c extension libraries almost never feel bolted
on. They feel like an intimate part of the language itself. Part of that
is the blood, sweat and tears of the library implementors, but much of it
is also the elegance of Python.

As far as the straw-poll goes, I think it's a good question to ask, and
that the answer is important, but we also need to figure out where else we
can ask this question. The problem with asking such a question solely on
c.l.p is that everyone here has either decided that optimization in python
isn't enough of an issue to bother them, or hasn't made up their
mind yet. Those who have decided that optimization in python is a problem
have already gone elsewhere. Perhaps a better question to ask is "Who has
decided that Python is too slow for their needs, what prompted that
decision and are the issues they had worth addressing?"

Sam Walters.
--
Never forget the halloween documents.
http://www.opensource.org/halloween/
""" Where will Microsoft try to drag you today?
Do you really want to go there?"""

[Rainer Deyke]

Samuel Walters wrote:

So, I guess that my point is that C might not be the right language
for doing numerical processing, but it seems the right language for
implementing the primitives of numerical processing.

The issue with C is that it is too slow for implementing those primitives
(in part due to pointer aliasing issues). Fortran is considerably faster.
--
Rainer Deyke - ra*****@eldwood.com - http://eldwood.com

[Samuel Walters]

|Thus Spake Skip Montanaro On the now historical date of Sat, 10 Jan 2004
07:50:09 -0600|

QOTW perhaps?

Sam> I read the benchmark and I think it doesn't measure python in
it's Sam> target area. That's like taking a world-class marathon
runner and Sam> wondering why he doesn't compete well in a
figure-skating event.

Skip

*garsh*

I feel flattered. *blush*

You know, I sadly spent quite a bit of time debating which simile to use
there. I wandered around the house wondering what to put there.

Some rejected ideas:
"It's like asking Kasparov why he didn't win the weight-lifting
competition."

"That's like asking a world-class marathon runner why he doesn't compete
well in a weight-lifting competition."

"That's like asking a world-class weightlifter why they didn't do well in
the figure skating competition." (I almost used this one because it
conjures images of a burly Russian weight-lifter floundering on ice
skates. Very Monty-Python-esque.)

I chose the one I did in case I needed to later state that "Both figure
skating and marathon running are aerobic sports, but that doesn't mean
that the skills involved are the same."

..

Now, I feel compelled to justify my statement.

Let's look closely at the benchmarks and try to figure out if there's a
good reason why we fell down where we did.

We did poorly at Math, and competitively at I/O.

I'm reminded of Antoine de Saint-Exupe'ry saying "A designer knows he has
achieved perfection not when there is nothing left to add, but when there
is nothing left to take away." While not part of the Zen of Python, this
seems to be an unstated principle of Python's design. It seems to focus
on the bare minimum of what's needed for elegant expression of algorithms,
and leave any extravagances to importable libraries.

Why, then, are integers and longs part of Python itself, and not part of a
library? Well, we need such constructs for counters, loops and indexes.
Both range and xrange are evidence of this. Were it not for this, I
daresay that we'd have at least argued the necessity of keeping these
things in the language itself.

Floats are a pragmatic convenience, because it's nice to be able to throw
around the odd floating point number when you need to. Trig functions are
housed in a separate library and notice that we didn't do too shabby there.

I/O is one of our strengths, because we understand that most programs are
not algorithmically bound, but rather I/O bound. I/O is a big
bottle-neck, so we should be damn good at it. The fastest assembly
program won't do much good if it's always waiting on the disk-drive.

Perhaps our simplicity is the reason we hear so many Lisp'ers vocally
complaining. While more pragmatic than Lisp, Python is definitely edging
into the "Lambda Calculus Zone" that Lisp'ers have historically been the
proud sole-occupants of. After all, until Python, when one wanted a
nearly theoretical programming experience, one either went to C/Assembly
(Turing Machine Metaphor) or Lisp (Lambda Calculus Metaphor.)

Python is being used in so many introductory programming courses for the
very reason that it so purely fits the way a programmer thinks, while
still managing to be pragmatic. It allows for a natural introduction to
some of the hardest concepts: Pointers/Reference, Namespaces, Objects and
Legibility. Each of these concepts is difficult to learn if you are first
indoctrinated into an environment without them. In my attempts to learn
C++, I initially felt like I was beating my head up against a wall trying
to learn what an object was and why one would use them. I have since
observed that people coming from a strongly functional programming
background have the same experience, while those with no functional
programming dogma in them find objects quite a natural concept. The same
thing is true of the other concepts I mentioned. If you have them, it's
easy to work without them. If you don't, you'll flounder trying to pick
them up. Think about how easy it is to pick out a C programmer from their
Python coding style.

The one important concept I didn't mention is message-passing. This is an
important, but much less used concept. It is the domain of Smalltalk and
Ruby. I've looked some at Ruby, and lurk their Usenet group. From what I
can tell, Ruby takes almost the same philosophy as Python, except where we
think namespaces are a honking great idea, they think message-passing is a
honking great idea. The nice thing about message-passing is that if you
have all the other concepts of OO down, message passing seems natural and
is not terribly difficult to "fake" when it's the only missing OO
primitive. This is why C++, while not a message-based OO language, is
used so often in GUI's, an inherently message-based domain. This is also
why we have such a nice, broad choice of GUI toolkits under Python despite
lacking a message primitive.
Well, I've blathered enough on this topic.
I hope, at least, that I've said something worthwhile.
Though, I doubt I've said anything that hasn't been said better before.

Caffeine, Boredom and Usenet are a dangerous mix.

Sam Walters.
--
Never forget the halloween documents.
http://www.opensource.org/halloween/
""" Where will Microsoft try to drag you today?
Do you really want to go there?"""

[Samuel Walters]

|Thus Spake Rainer Deyke On the now historical date of Sun, 11 Jan 2004
06:46:50 +0000|

Samuel Walters wrote:

So, I guess that my point is that C might not be the right language for
doing numerical processing, but it seems the right language for
implementing the primitives of numerical processing.

The issue with C is that it is too slow for implementing those
primitives (in part due to pointer aliasing issues). Fortran is
considerably faster.

I stand corrected.

Please help me to understand the situation better.

I went digging for technical documents, but thus far haven't found many
useful ones. It seems everyone but me already understands pointer
aliasing models, so they might discuss them, but they don't explain them.
I am limited in part by my understanding of compilers and also by my
understanding of Fortran. Here is what I have gathered so far:

Fortran lacks a stack for function calls. This promotes speed, but
prevents recursive functions. (Most recursive functions can efficiently be
written as loops, though, so this shouldn't be considered a hindrance.)

Fortran passes all arguments by reference. (This is the peppiest way to do
it, especially with static allocation)

Fortran 77 lacks explicit pointers and favors static allocation. This
allows for the compiler to apply powerful automatic optimization.

Fortran 90 added explicit pointers, but required them to only be pointed
at specific kinds of objects, and only when those particular objects are
declared as targets for pointers. This allows the compiler to still apply
powerful automatic optimizations to code. I'm a bit hazy as to whether
Fortran 90 uses static or dynamic allocation, or a combination of both,
and whether it permits recursion.

These pointers not only reference location, but also dimension and stride.
Stride is implicit in C pointer declarations (by virtue of compile-time
knowledge of the data type pointed to) but dimension is not.

Fortran's extensions for parallel programming have been standardized, and
the language itself makes it easy to decide how to parallelize procedures
at compile time. Thus, it is especially favored for numeric computation on
big iron with lots of parallelism.

Now, for C:

Because of dynamic allocation on the stack and the heap, there is no
compile-time knowledge of where a variable will live, which adds an extra
layer of reference for even static variables. This also invalidates many
of optimizations used by Fortran compilers.

C lacks many of the fundamental array handling semantics and primitives
that Fortran programs rely on. Implementing them in C is a real PITA.

C memory allocation is just plain goofy in comparison to Fortran.

To sum up:
Fortran sacrifices generality and dynamism for compile-time knowledge
about data, and deeply optimizes based on that knowledge.

C sacrifices speed for the sake of generality and dynamism.

Please correct me or help me flesh out my ideas. Please don't skimp on
the low-level details, I've done my fair share of assembly programming, so
what I don't understand, I'll probably be able to find out by googling a
bit.

Some other interesting things I found out:

There are two projects that allow interfacing between Python and Fortran:
F2Py
http://cens.ioc.ee/projects/f2py2e/
PyFortran
http://sourceforge.net/projects/pyfortran

Fortran amply supports interfaces to C and C++

Fortran is compiled. (Doh! and I thought it was interpreted.)

There are lots of debates on whether C++ will ever be as fast as Fortran.
The consensus seems to be "Only if you use the right compiler with the
right switches and are insanely careful about how you program. IOW Don't
bother, just use Fortran if you want to do numeric processing.

Well, there's another language to add to my list of languages to learn. It
seems to be "The Right Tool" for a great many applications, it interfaces
well with other languages, and it's extremely portable. Chances are, I'll
end up using it somewhere somehow someday. Now. To find some Fortran
tutorials.

Thanks in advance for any of your knowledge and wisdom you are willing to
confer upon me.

Sam Walters.

--
Never forget the halloween documents.
http://www.opensource.org/halloween/
""" Where will Microsoft try to drag you today?
Do you really want to go there?"""

[Ganesan R]

>>>>> "Samuel" == Samuel Walters <sw*************@yahoo.com> writes:

I/O is one of our strengths, because we understand that most programs are
not algorithmically bound, but rather I/O bound. I/O is a big
bottle-neck, so we should be damn good at it. The fastest assembly
program won't do much good if it's always waiting on the disk-drive.

Actually, Python is much slower than Perl for I/O. See the thread titled
"Python IO Performance?" in groups.google.com for a thread started by me on
this topic. I am a full time C programmer but do write occasional
Python/Perl for professional/personal use.

To answer the original question about how much percentage of time I spend
optimizing my Python programs - probably never. However I did switch back to
using Perl for my most of my text processing needs. For one program that was
intended to lookup patterns in a gzipped word list, performance of the
original python version was horribly slow. Instead of rewriting it in Perl,
I simply opened a pipe to zgrep and did post processing in python. This
turned out to be much faster - I don't remember how much faster, but I
remember waiting for the output from the pure python version while the
python+zgrep hybrid results were almost instantaneous.

Ganesan

--
Ganesan R

[John J. Lee]

Samuel Walters <sw*************@yahoo.com> writes:

|Thus Spake Rainer Deyke On the now historical date of Sun, 11 Jan 2004
06:46:50 +0000|

[...]

The issue with C is that it is too slow for implementing those
primitives (in part due to pointer aliasing issues). Fortran is
considerably faster.

I stand corrected.

Please help me to understand the situation better.

I went digging for technical documents, but thus far haven't found many
useful ones. It seems everyone but me already understands pointer
aliasing models, so they might discuss them, but they don't explain them.

[...]

(haven't read all your post, so sorry if I'm telling you stuff you
already know)

Pointer aliasing is just the state of affairs where two pointers refer
to a single region of memory. Fortran compilers have more information
about aliasing than do C compilers, so can make more assumptions at
compilation time.

Have you tried comp.lang.fortran, comp.lang.c++, comp.lang.c, etc?

http://www.google.com/groups?q=point...anl.gov&rnum=3

http://tinyurl.com/2v3v5
John

[David M. Cooke]

At some point, Samuel Walters <sw*************@yahoo.com> wrote:

Whether one uses Fortran, Python, or any other language, all primitives
are eventually implemented in either C or assembly. At some point or
another, we end up scraping bare metal and silicon to get our answers.
The question then becomes, "How easily can I extend this language to fit
my needs." NumPy is evidence that at least a few people said "Easily
enough." I don't know how extensible Fortran is, but I would guess "very"
since I've heard of it being applied in many domains other than numerical
processing. (OpenFirmware, for example.)

You're confusing Fortran with Forth, which is a stack-based language,
much like Postscript, or RPL used on HP 48 calculators.

These days, I doubt Fortran is used for anything but numerical processing.

--
|>|\/|<
/--------------------------------------------------------------------------\
|David M. Cooke
|cookedm(at)physics(dot)mcmaster(dot)ca

[Dan Bishop]

Samuel Walters <sw*************@yahoo.com> wrote in message news:<pa****************************@yahoo.com>...

|Thus Spake Rainer Deyke On the now historical date of Sun, 11 Jan 2004
06:46:50 +0000|

Samuel Walters wrote: [Fortran is faster than C.]

.... I went digging for technical documents, but thus far haven't found many
useful ones. It seems everyone but me already understands pointer
aliasing models, so they might discuss them, but they don't explain them.
I am limited in part by my understanding of compilers and also by my
understanding of Fortran. Here is what I have gathered so far:

Fortran passes all arguments by reference. (This is the peppiest way to do
it, especially with static allocation)
Btw, for some early compilers, this was the case even with literals,
which meant that code like

CALL FOO(4)
PRINT *, 4

could print something other than 4.
...I'm a bit hazy as to whether
Fortran 90 uses static or dynamic allocation, or a combination of both,
You can use both, at least for arrays.
and whether it permits recursion.
Fortran 90 does permit recursion, although you have to explicitly
declare functions as "recursive".
Now, for C: .... C lacks many of the fundamental array handling semantics and primitives
that Fortran programs rely on. Implementing them in C is a real PITA.
This is one of my least favorite things about C.
C memory allocation is just plain goofy in comparison to Fortran.

And even worse in comparison to Python ;-)

[Matthias]

Peter Hansen <pe***@engcorp.com> writes:

This is my "straw poll" question:

Do you spend a "significant" amount of time actually optimizing your
Python applications? (Significant is here defined as "more than five
percent of your time", which is for example two hours a week in a
40-hour work week.)

I was working on an image processing application and was looking for a
quick prototyping language. I was ready to accept a 10-fold decrease
in execution speed w.r.t. C/C++. With python+psycho, I experienced a
1000-fold decrease.

So I started re-writing parts of my program in C. Execution speed now
increased, but productivity was as low as before (actually writing the
programs directly in C++ felt somewhat more natural). Often it
happened that I prototyped an algorithm in python, started the
program, implemented the algorithm in C as an extension module and
before the python algorithm had finished I got the result from the
C-algorithm. :-(

I've tried numerics, but my code was mostly not suitable for
vectorization and I did not like the pointer semantics of numerics.

So my answer to the question above is NO, I don't spend significant
times optimizing python code as I do not use python for
computationally intensive calculations any more. My alternatives are
Matlab and (sometimes) Common Lisp or Scheme or Haskell.

[Frithiof Andreas Jensen]

"Samuel Walters" <sw*************@yahoo.com> wrote in message
news:pa****************************@yahoo.com...

As I see it, when one considers which language is best for one's needs,
one considers a couple of things:

1) Does the language have the semantics I want.
2) Does the language have the primitives I need.
3) Can I *easily* build any missing or suboptimal primitives.

True.
One would assume that Fortran has the proper semantics for numerical
processing because it seems to have been wildly successful for a long
period of time.
That, in my opinion, is wrong: Fortran is successful because it was there
first!

There exists a very large set of actively supported and proven libraries,
NAG f.ex., which nobody will ever bother to port to another language just
for the sake of it, and Fortran has been around for so long that it is well
understood how best to optimise and compile Fortran code. It is easy enough
to link with NAG if one needs to use it.
Fortran naturally comes with the primitives for numerical processing,
because numerical processing is its stated goal. ("FORmula TRANslation")
....or maybe the name sounded cool ;-)
Whether one uses Fortran, Python, or any other language, all primitives
are eventually implemented in either C or assembly. At some point or
another, we end up scraping bare metal and silicon to get our answers.

Exactly - Fortran in itself does not do something that another language
cannot do as well. It is just the case that Fortran is better understood
when applied to numering processing than other languages because more
"numerics" people used it than any other language.

On DSP architectures, f.ex., I doubt that one would have better performance
using Fortran in comparison with the C/C++ tools, DSP's usually ship with -
because DSP's were "born" when C/C++ was hot.

A lot of real, serious DSP work is done in Mathlab - thus skipping the issue
of language choice and getting right onto getting the job done. This is good
IMO.

[Lothar Scholz]

Samuel Walters <sw*************@yahoo.com> wrote in message news:<pa****************************@yahoo.com>...

I know very little about numeric processing, and even less about Fortran.
It's true that my background is in mathematics, but in *pure* mathematics
where pointer-based languages tend to be helpful, not hurtful.

Okay seems that you don't know a lot about compiler writing.

A C compiler only knows a little bit about the context so it must
always assume that a data inside a member can be referenced from
another place via an alias pointer.

Fortran does not have this problem so a lot of optimizations can be
done and values can be hold in registers for a much longer time,
resulting in much greater speed.

Remember that on supercomputers a 25% spped enhancement (which a good
fortran compiler is faster then C) can mean a few million dollars of
saved hardware costs. The coding time is not importing compared to the
running time. So real hard numerics are always done in Fortran.

GNU Fortran is a stupid project because it translates the Fortran code
to C.
Python for hardcore numerics, even with PyNumerics, is simply a very
bad solution.

[Peter Hansen]

Ganesan R wrote:

>> "Samuel" == Samuel Walters <sw*************@yahoo.com> writes:

I/O is one of our strengths, because we understand that most programs are
not algorithmically bound, but rather I/O bound. I/O is a big
bottle-neck, so we should be damn good at it. The fastest assembly
program won't do much good if it's always waiting on the disk-drive.

Actually, Python is much slower than Perl for I/O. See the thread titled
"Python IO Performance?" in groups.google.com for a thread started by me on
this topic. I am a full time C programmer but do write occasional
Python/Perl for professional/personal use.

To answer the original question about how much percentage of time I spend
optimizing my Python programs - probably never. However I did switch back to
using Perl for my most of my text processing needs. For one program that was
intended to lookup patterns in a gzipped word list, performance of the
original python version was horribly slow. Instead of rewriting it in Perl,
I simply opened a pipe to zgrep and did post processing in python. This
turned out to be much faster - I don't remember how much faster, but I
remember waiting for the output from the pure python version while the
python+zgrep hybrid results were almost instantaneous.

I didn't consider this sort of thing in my poll, but I'd have to say you
actually *are* optimizing your Python programs, even if you did it by falling
back on another language...

-Peter

[Peter Hansen]

Paul Rubin wrote:

Peter Hansen <pe***@engcorp.com> writes:

This is my "straw poll" question:

Do you spend a "significant" amount of time actually optimizing your
Python applications? (Significant is here defined as "more than five
percent of your time", which is for example two hours a week in a
40-hour work week.)

Yes, absolutely.

Algorithmic improvements that you would make regardless of implementation
language do not qualify, and wasting time optimizing a script that you
run once a year so it takes ten seconds instead of fifteen also does not
qualify because you certainly didn't need to do it...

Sometimes I'll take the time to implement a fancy algorithm in Python
where in a faster language I could use brute force and still be fast
enough. I'd count that as an optimization.

I would count it as optimization as well, which is why I qualified my
comment with "regardless of implementation language". Clearly in your
case that clause does not apply.

-Peter

[David M. Cooke]

At some point, ll*****@web.de (Lothar Scholz) wrote:

Samuel Walters <sw*************@yahoo.com> wrote in message news:<pa****************************@yahoo.com>...

I know very little about numeric processing, and even less about Fortran.
It's true that my background is in mathematics, but in *pure* mathematics
where pointer-based languages tend to be helpful, not hurtful.
Okay seems that you don't know a lot about compiler writing.

A C compiler only knows a little bit about the context so it must
always assume that a data inside a member can be referenced from
another place via an alias pointer.

Fortran does not have this problem so a lot of optimizations can be
done and values can be hold in registers for a much longer time,
resulting in much greater speed.

Remember that on supercomputers a 25% spped enhancement (which a good
fortran compiler is faster then C) can mean a few million dollars of
saved hardware costs. The coding time is not importing compared to the
running time. So real hard numerics are always done in Fortran.

GNU Fortran is a stupid project because it translates the Fortran code
to C.

Err, no. You're thinking of f2c. GNU Fortran uses the same backend as
the GNU C compiler, in that it translates to the same intermediate
language (sort of assembly language) on which optimizations are done.
But the C front end and the Fortran front end are separate.

The advantage of GNU Fortran is it's *portable*. It runs on everything
that GCC works on -- which is a lot. This makes a difference when
you're developing (like on my Apple iBook running Linux). And it looks
like the G95 project is humming along nicely.
Python for hardcore numerics, even with PyNumerics, is simply a very
bad solution.

You're not limited to pure Python.

No one's saying you must use only C, or only Fortran, or only Python.
Use the best tool for the job. Python just has the advantage that
mixing C and Fortran into it is easy. Write your big, number-crunching
code as a Fortran routine, wrap it with f2py, then add a Python script
around it to run it. Presto, no fiddling with Fortran for reading in
input files, or output files (depending on your routine). You can even
write a GUI in Python if you wish. Or add a webserver.

--
|>|\/|<
/--------------------------------------------------------------------------\
|David M. Cooke
|cookedm(at)physics(dot)mcmaster(dot)ca

[Jeff Epler]

On Mon, Jan 12, 2004 at 05:42:22AM -0800, Lothar Scholz wrote:

Python for hardcore numerics, even with PyNumerics, is simply a very
bad solution.

I think that many of us are not in a situation where we have to make our
program run fast enough to save a million on a supercomputer, but where
we have to make our program run soon enough to save a few thousands or
maybe tens of thousands on programmer time.

ObLink: http://cens.ioc.ee/projects/f2py2e/

Jeff

[Lothar Scholz]

co**********@physics.mcmaster.ca (David M. Cooke) wrote in message news:<qn*************@arbutus.physics.mcmaster.ca> ...

At some point, ll*****@web.de (Lothar Scholz) wrote:

Samuel Walters <sw*************@yahoo.com> wrote in message news:<pa****************************@yahoo.com>...

I know very little about numeric processing, and even less about Fortran.
It's true that my background is in mathematics, but in *pure* mathematics
where pointer-based languages tend to be helpful, not hurtful.

Okay seems that you don't know a lot about compiler writing.

A C compiler only knows a little bit about the context so it must
always assume that a data inside a member can be referenced from
another place via an alias pointer.

Fortran does not have this problem so a lot of optimizations can be
done and values can be hold in registers for a much longer time,
resulting in much greater speed.

Remember that on supercomputers a 25% spped enhancement (which a good
fortran compiler is faster then C) can mean a few million dollars of
saved hardware costs. The coding time is not importing compared to the
running time. So real hard numerics are always done in Fortran.

GNU Fortran is a stupid project because it translates the Fortran code
to C.

Err, no. You're thinking of f2c. GNU Fortran uses the same backend as
the GNU C compiler, in that it translates to the same intermediate
language (sort of assembly language) on which optimizations are done.
But the C front end and the Fortran front end are separate.

But the front end is, as far as i know, only responsible for syntax
analysis. All code generation like control flow analysis, register
allocation etc. is done on the intermediate 3 register language. And
the hints you could get from the fortran language are simply unused.

But i must say that i never used Fortran (when i studied physics) in
the last 15 years for any real programming. I only wanted to point out
that a lot of languages are by design faster then C. Fortran was one
of these. You can find articles on the web where the same is explained
for lisp and functional languages.

C and C++ is not a good compilation language when it comes to compiler
optimization.

[Robin Becker]

In article <6e**************************@posting.google.com >, Lothar
Scholz <ll*****@web.de> writes
.....

Fortran does not have this problem so a lot of optimizations can be
done and values can be hold in registers for a much longer time,
resulting in much greater speed.

I'm not sure I agree with the above. Aliases could certainly occur in
fortran 77, I haven't used 90 so can't say for sure.Remember that on supercomputers a 25% spped enhancement (which a good
fortran compiler is faster then C) can mean a few million dollars of
saved hardware costs. The coding time is not importing compared to the
running time. So real hard numerics are always done in Fortran.

--
Robin Becker

[Jacek Generowicz]

mi***************@poste.it (Michele Simionato) writes:

Jacek Generowicz <ja**************@cern.ch> wrote in message news:<ty*************@pcepsft001.cern.ch>...

Go on, raise your hand if you thought that "Lisp" is a slow,
interperted functional langugage.

Never thought so.

Michele,

My post was not aimed at you specifically. It was aimed a lurkers who
might infer that Lisp is only used in AI (or whatever), or who might
have some unfounded assumptions about Lisp which would be re-inforced
by your post.

I was quite sure that my post would not be telling _you_ much you
didn't already know.