Brendan Halpin on Correlations, smoothed time-series and sewage sludge.Correlations, smoothed time-series and sewage sludge.The key trick is to make multiple copies of the do-file and to pass parameters to it. When you have a simply parallelisable problem (like a simulation) it’s easy to split and run it over multiple cores.
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( cd run$a & time stata -b do ranktest_mp "$RANDOM 1250" ) &Īlternatively use Stata’s timer command. Stata could also be used to carry out this task, or a DOS. To set the 8 instances running, I have written a little unix shell-script. On my desktop, I have 8 cores, so we can run 8 times 1,250 replications at once. For each value of the error parameter (a list between 0.01 and 1), I want, say, 10,000 replications. dta file using Stata’s postfile framework. My summary statistic for each replication is the mean across the 100 cases of the absolute difference between the ranks I store this in a. I present a pretty simple example simulation: given a true ranking that is based on a true continuous score variable, what does measurement error in the ranking look like if there is measurement in the score variable? I simulate 100 cases, calculating their true rank from the true score (a standard normal random variable), and then a second rank based on the score with a certain amount of random-normal error added (a normal random variable, mean zero, standard deviation given by a parameter). The command-line parameters can include things like a random seed, or other settings that need to differ across runs. In this example I create subdirectories and place copies there, but you could simply copy the source do-file to numbered copies. To avoid having all instances writing to the same log file, the source do-file needs to be renamed or placed in multiple locations. To do this, you need to write your program to take parameters so that it can be invoked multiple times but produce different results.
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In this note I demonstrate a simple case, using the example of a simulation I wish to run many times. However, if your problem can be split up in parts that can run in parallel, it is easy to run multiple instances of Stata. If you don’t have Stata-MP, it can be difficult to benefit from all the cores on your computer.