David, Thanks for the test results and the correction - now I recall how it's done in Matlab. Haven't been using it for a while. :-)
-Zhong On Monday, July 11, 2016 at 7:09:28 AM UTC-5, David Barton wrote: > > For reference, with Matlab 2016a: 4.97 sec; Julia 0.4.6: 2.76 sec; Python > 3.5.1: 166.76 sec. > > Note that there is a mistake in your Matlab code - zeros(n) returns an n > by n matrix of zeros (hence running out of memory). Instead you want > zeros(1, n) to get a vector. > > David > > On Monday, 11 July 2016 10:07:01 UTC+1, Zhong Pan wrote: >> >> Hi Andreas, >> >> Thanks for the comments. >> >> * If someone has a more recent Matlab it'll be interesting to try. The >> license is so expensive and I don't have access to newer version now. >> >> * Yes you are right, I also realized that I don't know how much the >> random number generator implementation difference would contribute. One >> thing to try is to leave out the random number generations. >> >> I tried it and here's the result: Python 166.44 sec (107.4x, was 64.3x), >> Julia 2.56 sec (1.7x, was 0.8x), VC++ 1.55 sec (1.0x as reference), C#.NET >> 3.49 sec (2.3x, was 1.1x), Java 10.14 sec (6.5x, was 3.0x), and Matlab 7.75 >> sec (5.0x, was 3.3x). Therefore, it seems VC++ improved the most by >> removing random number generations, and other languages just all look >> relatively 1.5 to 2.2 times more slower. Julia is still the fastest aside >> from VC++, and C#.NET is still not far behind. >> >> Cheers, >> -Zhong >> >> >> On Monday, July 11, 2016 at 3:27:30 AM UTC-5, Andreas Lobinger wrote: >>> >>> 2 small things: >>> >>> * a more recent Matlab should already be faster, especially in this loop >>> thing >>> * random generators' runtime -depending on the complexity they spend- >>> really makes a difference. >>> >>>
