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

[Krzysztof Stachlewski]

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

"JanC" <usenet_spam@ja nc.invalid> wrote in message
news:Xns946BE66 E88E82JanC@213. 119.4.35...[color=blue]
> "Krzysztof Stachlewski" <stach@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++...[/color]

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]

Re: Straw poll on Python performance (was Re: Python is far froma top performer ...)

Dave Brueck wrote:
[color=blue]
>Peter wrote:
>
>[/color]
....
[color=blue][color=green]
>>This is my "straw poll" question:
>>
>> Do you spend a "significan t" 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.)
>>
>>[/color]
>
>Yay, straw poll! ;-)
>
>[/color]
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]

Re: Straw poll on Python performance (was Re: Python is far from a top performer ...)

Mike C. Fletcher:[color=blue]
>I am often found optimising.[/color]

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

--
René Pijlman

[Mike C. Fletcher]

Re: Straw poll on Python performance (was Re: Python is far froma top performer ...)

Rene Pijlman wrote:
[color=blue]
>Mike C. Fletcher:
>
>[color=green]
>>I am often found optimising.
>>
>>[/color]
>
>By an unoptimized or by an optimizing coworker? :-)
>
>[/color]
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]

Re: Straw poll on Python performance (was Re: Python is far from atop performer ...)

On Fri, Jan 09, 2004 at 05:09:37PM -0500, Peter Hansen wrote:[color=blue]
> Iwan van der Kleyn wrote:[color=green]
> >
> > In other words: it would be nice if Python on average would run faster
> > so the need for optimisation would lessen.[/color]
>
> 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 "significan t" 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.)
>[/color]

No.

Jp

[Ken]

Re: Straw poll on Python performance (was Re: Python is far from a top performer ...)

> Do you spend a "significan t" amount of time actually optimizing your[color=blue]
> 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.)[/color]

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]

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


"Krzysztof Stachlewski" <stach@fr.USUN. pl> wrote in message
news:btn5db$2f8 $1@absinth.dial og.net.pl...[color=blue]
> "Carl" <phleum_nospam@ chello.se> wrote in message
> news:ryELb.238$ tK2.228@amstwis t00...
> 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.[/color]

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][color=blue]
>I just think that the Numeric package is not the best example
> of the speed of Python itself.[/color]

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]

Re: Straw poll on Python performance (was Re: Python is far froma top performer ...)

> This is my "straw poll" question:[color=blue]
>
> Do you spend a "significan t" 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.)[/color]

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 chemoinformatic s 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]

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

Samuel Walters <swalters_usene t@yahoo.com> wrote in message news:<pan.2004. 01.09.21.35.40. 132608@yahoo.co m>...
[color=blue]
> For numerical processing, C is the right tool,[/color]

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

[Sean 'Shaleh' Perry]

Re: Straw poll on Python performance (was Re: Python is far from atop performer ...)

On Friday 09 January 2004 14:09, Peter Hansen wrote:[color=blue]
>
> Yes or no answers suffice, but feel free to follow up with a paragraph
> qualifying your answer (or quantifying it!). :-)
>[/color]

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]

Re: Straw poll on Python performance (was Re: Python is far from a top performer ...)

From: "Peter Hansen" <peter@engcorp. com>
[color=blue]
> This is my "straw poll" question:
>
> Do you spend a "significan t" 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.)[/color]

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?


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]

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

|Thus Spake Lothar Scholz On the now historical date of Fri, 09 Jan 2004
21:29:56 -0800|
[color=blue]
> Samuel Walters <swalters_usene t@yahoo.com> wrote in message
> news:<pan.2004. 01.09.21.35.40. 132608@yahoo.co m>...
>[color=green]
>> For numerical processing, C is the right tool,[/color]
>
> Definitely not, you don't want a pointer language when using numerical
> processing: use Fortran.[/color]

Hmm. I feel misunderstood. I'm going to try to clarify, but if I'm the
one doing the misunderstandin g, 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]

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

Samuel Walters wrote:[color=blue]
> 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.[/color]

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 - rainerd@eldwood .com - http://eldwood.com

[Samuel Walters]

Re: QOTW?

|Thus Spake Skip Montanaro On the now historical date of Sat, 10 Jan 2004
07:50:09 -0600|
[color=blue]
>
> 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[/color]

*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?"""