Re: [eigen] slides of my talk

[Benoit Jacob <jacob.benoit.1@xxxxxxxxx>]

Just FYI, if you want something that's guaranteed to kill matlab or
more generally any framework focusing on dynamic-size objects, try a
heavy computation on a large set of fixed-size vectors or matrices.

E.g. take a 4x4 matrix m, take a large array of 4-vectors
v[100000000]; and multiply each of these vectors by the matrix m;

Benoit

2009/1/26 Andre Krause <post@xxxxxxxxxxxxxxxx>:
> Gael Guennebaud schrieb:
>
> well, it seems as if there was something done by the matlab team since version
> 6.5 of matlab. see this link:
>
> http://www.caspur.it/risorse/softappl/doc/matlab_help/techdoc/matlab_prog/ch7_pe11.html
>
> Operating System        MATLAB 6.1      MATLAB 6.5      Performance Gain
> Windows 1 min., 25.9 sec.       1.1 sec.        x 78.1
> Linux   3 min., 13.8 sec.       1.7 sec.        x 114.0
> Solaris 8 min., 51.0 sec.       23.5 sec.       x 22.6
>
> looking at those results, it would be even more interesting, how well matlab
> does now compared to native c++.
>
> seems as if they indeed added some jit compiler...
>
> p.s. just googled - and found this:
>
> http://www.mathworks.se/products/matlab/whatsnew.html
>
> so matlab really has JIT acceleration.
>
>
>> indeed, if you use matlab on relatively large dataset and you only use
>> high level operators/routines then matlab is very fast because it
>> relies on other state of the art library. There even exists plugins to
>> perform some computations on the GPU...
>>
>> Said that, matlab is extremely slow as soon as you are doing some more
>> low level stuff involving complex expressions or a lot of small
>> computations... Matlab can be a couple of order of magnitude slower
>> than a C/C++ equivalent code. It seems MatLab is missing a JIT
>> compiler.
>>
>> btw, benoit thanks for the slides !
>>
>> Gael.
>>
>> On Mon, Jan 26, 2009 at 3:35 PM, Benoit Jacob <jacob.benoit.1@xxxxxxxxx> wrote:
>>> I have no clue how matlab does in general, but on the forum we've had
>>> a user benchmarking matrix product against matlab, and matlab was
>>> doing like MKL and Goto, which suggests that it's using one of these
>>> libraries.
>>>
>>> If you're on *nix, you could try ldd or nm to get a clue about what
>>> library they're using.
>>>
>>> Cheers,
>>> Benoit
>>>
>>> 2009/1/26 Andre Krause <post@xxxxxxxxxxxxxxxx>:
>>>> Benoit Jacob schrieb:
>>>>> Hi,
>>>>>
>>>>> Today morning I gave a talk on Eigen at the CS department. Here are my
>>>>> slides, in case they might be helpful.
>>>>> When you see (SHOW BENCHMARKS) that's where I showed the benchmark
>>>>> graphs that are displayed on the Benchmark page of the wiki.
>>>>> I didn't have time to cover the last part on Eigen internals, I just
>>>>> finished with the benchmarks. However people were interested and asked
>>>>> questions about internals so in effect I got to explain as much of the
>>>>> internals as I had planned to.
>>>>>
>>>>> Thanks a LOT to Keir: he translated the C++ snippets into Matlab for
>>>>> me, and gave me a good briefing as to how to organize a talk for an
>>>>> audience of CS people.
>>>>>
>>>>> I am just afraid that using \documentclass{slides} for a public of CS
>>>>> people makes me look like a dinosaur mathematician.
>>>>>
>>>>> Cheers,
>>>>> Benoit
>>>> dear benoit, you show matlab code corresponding to eigen2 code. but in the
>>>> benchmark section on the wiki, there is no matlab benchmark. i would be very
>>>> curious how slow (or fast?) matlab is, compared to eigen. i teached myself
>>>> matlab over christmas, and now i am curious how matlab compares to c++ (with
>>>> eigen2).
>>>>
>>>>
>>>>
>>>
>>>
>>
>>
>>
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