A comparative timing on Mac OSX 2.1 GHZ PowerPC G5:
erf =: (1 H. 1.5)@*: * 2p_0.5&* % ^@:*:
n01cdf =: -: @ >: @ erf @ %&(%:2)
ny =: n01cdf nx=: i:5j2000
randn =: nx {~ ny I. [EMAIL PROTECTED]&0
matrix =: randn(312,256)
vector =: randn(256,1)
1000 * 1000 (6!:2) 'matrix +/ .* vector'
0.529984
Intel machines might be faster... e.g. on Linux 3.0 GHZ
dual core machine:
erf =: (1 H. 1.5)@*: * 2p_0.5&* % ^@:*:
n01cdf =: -: @ >: @ erf @ %&(%:2)
ny =: n01cdf nx=: i:5j2000
randn =: nx {~ ny I. [EMAIL PROTECTED]&0
matrix =: randn(312,256)
vector =: randn(256,1)
1000 * 1000 (6!:2) 'matrix +/ .* vector'
0.229005
Both of these were run in j602 beta.
- joey
At 10:13 -0800 2007/11/08, [EMAIL PROTECTED] wrote:
I told my friend about how nice J was. Am I wrong?
He said, how fast can you multiply a 1000x1000 matrix times
a 1x1000 vector to get a 1x1000 resultant vector.
his MatLab code ran in about .6 seconds.
His code follows..
matrix = randn(312,256);
vector = randn(256,1);
tic
for i = 1:1000
output = matrix*vector;
end
toc
I wrote my code which runs in about 2.5 minutes
here is my code:
#!/home/efittery/bin/jconsole
A =: 256 312 $ 1+ i. 312 * 256x
B =: 312 1 $ 1+ i. 312x
jumbo =: monad define
for. i. y do.
yVector =: A +/ .* B
end.
)
jumbo 1000
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