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Re: speed comparison of IDL, numPy, Matlab
- Subject: Re: speed comparison of IDL, numPy, Matlab
- From: Gavrie <gavrie(at)my-deja.com>
- Date: Mon, 12 Feb 2001 12:19:48 GMT
- Newsgroups: comp.soft-sys.matlab,comp.lang.python,comp.lang.idl-pvwave
- Organization: Deja.com
- References: <3A7EEE5C.6419B707@pacific.jpl.nasa.gov>
- Xref: news.doit.wisc.edu comp.soft-sys.matlab:84482 comp.lang.python:123355 comp.lang.idl-pvwave:23495
In article <3A7EEE5C.6419B707@pacific.jpl.nasa.gov>,
Benyang Tang <btang@pacific.jpl.nasa.gov> wrote:
[...]
> * Machine: a dual Intel Xeon 550 MHz box with 1GB ram, running RedHat
Linux
> 6.2. The machine was not doing any serious service, so the test code
should
> have had close to 100% of the resources.
Well, I don't know how you got these results on a
dual Xeon 550 with 1G of RAM.
I ran the same benchmarks on a dual PIII-550 with
256MB of RAM, and got:
> python:
multiplication of 100X100 matrixes takes 0.02
multiplication of 200X200 matrixes takes 0.20
multiplication of 300X300 matrixes takes 1.07
multiplication of 400X400 matrixes takes 2.81
multiplication of 500X500 matrixes takes 5.49
multiplication of 600X600 matrixes takes 9.58
> matlab:
multiplication of 100X100 matrixes takes 0.01
multiplication of 200X200 matrixes takes 0.06
multiplication of 300X300 matrixes takes 0.15
multiplication of 400X400 matrixes takes 0.35
multiplication of 500X500 matrixes takes 0.68
multiplication of 600X600 matrixes takes 1.19
So, this difference seems a bit strange, doesn't
it?
I'm using MATLAB 6 (whichis supposed to be
*slower* than 5.3), and Python 1.5.2.
-- Gavrie Philipson.
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