
OMatrix Statistics Performance
The high performance of the OMatrix language makes it ideal
for large scale statistical data analysis and simulation. The following
benchmark illustrates performance benefits for some of OMatrix'
statistics functions.
Benchmark 
OMatrix 6.5 
Matlab 7.6 
Sort 3,000,000 random values 
0.296 
0.417 
Sort 30,000 random values 
0.002 
0.003 
Columnwise mean of 100,000x25 random matrix 
0.002 
0.006 
Columnwise standard deviation of 100,000x25 random matrix 
0.002 
0.083 
Norm of 200,000 element random vector 
0.0009 
0.0002 
Create 200,000x20 matrix of uniform distributed random numbers 
0.009 
0.101 
Create 200,000x20 matrix of normally distributed random numbers 
0.061 
0.073 
Create 200,000x20 matrix of log normal distributed random numbers 
0.065 
NA* 
Create 200,000x20 matrix of exponentially distributed random numbers 
0.034 
NA 
Exponential cumulative distribution function of 200,000x20 matrix 
0.037 
NA 
Exponential probability density function of 200,000x20 matrix 
0.033 
NA 
Columnwise mean absolute deviation of 100,000x25 random matrix 
0.165 
NA 
Columnwise median absolute deviation of 100,000x25 random matrix 
0.004 
NA 
Columnwise median of 50,000x25 random matrix 
0.079 
0.136 
Convolution of two 2^14 element vectors 
0.009 
0.626 
Covariance matrix for two 100,000 element vectors 
0.002 
0.007 
Columnwise Kurtosis of 100,000x25 random matrix 
0.005 
NA 
Columnwise sum of 100,000x25 random matrix 
0.002 
0.005 
All timings are in seconds.  Run on an Intel Core 2 Quad, Q6600 at 2.4 GHz, with 4GB of memory
NA*, Not available or requires additional toolbox
OMatrix is my first choice for simulation and data analysis.
I could use C++, VB, or Fortran for this, but OMatrix provides the
performance and convenience I need to bypass these languages.
 Ryan Franklin
