Hi.

I have been struggling with NSIS, Cygwin and Windows lately, but have

now made a stand alone installation package for octave-2.1.60, included

octave-forge (CVS -version), some cygwin libraries, and support for

gnuplot (windows version).

You don't need Cygwin to run Octave, since the run-time libraries are

included. ;-)

Remember that there are no atlas or blas included here. It's purely

Octave code.

I compiled octave and octave-forge with gcc-3.3.3, and benchmarked my

octave according to the benchmark suites found in

http://www.sciviews.org/other/benchmark.htm. Thanks to D. Bateman, I

could run the complete test. ;-)

Attaches the gcd2.m file, that are required to finish point 3.C of the

benchmark test.

Take a look at the results from the bench-test. m-files are slow, but

some oct-files are pretty acceptable.

How does this compare with the Linux system?

Could it be an advantage, if I compiled octave with gcc-3.2.2 instead?

Could someone compare with gcc-3.2.2?

I am having some hacking left to do, but when this is ready, I'll will

make the stand-alone Octave available at sourceforge or octave.org.

If you are really in a dead or alive situation with Octave on Windows,

then send me an email.

I will then give you access to my ftp, as fast as I can. ;-)

Cheers,

Ole J.

And my results are:

octave-2.1.60:8> benchmark

Octave Benchmark 2

==================

Number of times each test is run__________________________: 3

I. Matrix calculation

---------------------

Creation, transp., deformation of a 1500x1500 matrix (sec): 1.093

800x800 normal distributed random matrix ^1000______ (sec): 0.899

Sorting of 2,000,000 random values__________________ (sec): 1.34

700x700 cross-product matrix (b = a' * a)___________ (sec): 4.224

Linear regression over a 600x600 matrix (c = a \ b') (sec): 0.9883

------------------------------------------------------

Trimmed geom. mean (2 extremes eliminated): 1.131

II. Matrix functions

--------------------

FFT over 800,000 random values______________________ (sec): 0.9717

Eigenvalues of a 320x320 random matrix______________ (sec): 1.215

Determinant of a 650x650 random matrix______________ (sec): 1.166

Cholesky decomposition of a 900x900 matrix__________ (sec): 0.6697

Inverse of a 400x400 random matrix__________________ (sec): 0.9513

------------------------------------------------------

Trimmed geom. mean (2 extremes eliminated): 1.025

III. Programmation

------------------

750,000 Fibonacci numbers calculation (vector calc)_ (sec): 1.726

Creation of a 2250x2250 Hilbert matrix (matrix calc) (sec): 0.8423

Grand common divisors of 70,000 pairs (recursion)___ (sec): 3.229

Creation of a 220x220 Toeplitz matrix (loops)_______ (sec): 38.49

Escoufier's method on a 37x37 matrix (mixed)________ (sec): 26.15

------------------------------------------------------

Trimmed geom. mean (2 extremes eliminated): 5.263

Total time for all 15 tests_________________________ (sec): 83.96

Overall mean (sum of I, II and III trimmed means/3)_ (sec): 1.828

--- End of test ---

function c = gcd2(a, b)

% Greatest common divisor by a recursive algorithm

% This function is used for the Matlab benchmark

% Use gcd(a, b) instead for other uses

%

% by Ph. Grosjean, 2001 (

[hidden email])

if b <= 1.0E-4

c = a;

else

b(b == 0) = a(b == 0);

c = gcd2(b, rem(a, b));

end