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O-Matrix Statistics Performance

The high performance of the O-Matrix language makes it ideal for large scale statistical data analysis and simulation. The following benchmark illustrates performance benefits for some of O-Matrix' statistics functions.

 Benchmark O-Matrix 6.5 Matlab 7.6 Sort 3,000,000 random values 0.296 0.417 Sort 30,000 random values 0.002 0.003 Column-wise mean of 100,000x25 random matrix 0.002 0.006 Column-wise 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 Column-wise mean absolute deviation of 100,000x25 random matrix 0.165 NA Column-wise median absolute deviation of 100,000x25 random matrix 0.004 NA Column-wise 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 Column-wise Kurtosis of 100,000x25 random matrix 0.005 NA Column-wise 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

O-Matrix is my first choice for simulation and data analysis.
I could use C++, VB, or Fortran for this, but O-Matrix provides the
performance and convenience I need to bypass these languages.
- Ryan Franklin