We generate pairs of properly distributed Gaussian variables at down to 10nsec intervals, essential in the application. Speed can be an issue, particularly in real time situations.
Ian "Glen Barnett" <[EMAIL PROTECTED]> wrote in message a4plof$p3s$[EMAIL PROTECTED]">news:a4plof$p3s$[EMAIL PROTECTED]... > > Art Kendall <[EMAIL PROTECTED]> wrote in message > [EMAIL PROTECTED]">news:[EMAIL PROTECTED]... > > I tend to be more concerned with the "apparent randomness" of the results > than with the speed of the algorithm. > > This will be mainly a function of the randomness of the uniform generator. If > we assume the same uniform generator for both, and assuming it's a pretty good > one (our current one is reasonable, though I want to go back and update it > soon), there shouldn't be a huge difference in the apparent randomness of the > resulting gaussians. > > > As a thought experiment, what is the cumulative time difference in a run > using the fastest vs the slowest algorithm? A > > whole minute? A second? A fractional second? > > When you need millions of them (as we do; a run of 10,000 simulations could > need as many as 500 million gaussians, and we sometimes want to do more than > 10,000), and you also want your program to be interactive (in the sense that > the user doesn't have to wander off and have coffee just to do one simulation > run), knowing that one algorithm is, say, 30% faster is kind of important. > Particularly if the user may want to do hundreds of simulations... > > A whole minute extra on a simulation run is a big difference, if the user is > doing simulations all day. > > Glen > > ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at http://jse.stat.ncsu.edu/ =================================================================
