I'm running package parallel in R-3.0.2. Below are the execution times using system.time for when executing serially versus in parallel (with 2 cores) using parRapply.
Serially: user system elapsed 4.67 0.03 4.71 Using package parallel: user system elapsed 3.82 0.12 6.50 There is evident improvement in the user cpu time, but a big jump in the elapsed time. In my code, I am executing a function on a 1000 row matrix 100 times, with the data different each time of course. The initial call to makeCluster cost 1.25 seconds in elapsed time. I'm not concerned about the makeCluster time since that is a fixed cost. I am concerned about the additional 1.43 seconds in elapsed time (6.50=1.43+1.25). I am wondering if there is a way to structure the code to avoid largely avoid the 1.43 second overhead. For instance, perhaps I could upload the function to both cores manually in order to avoid the function being uploaded at each of the 100 iterations? Also, I am wondering if there is a way to avoid any copying that is occurring at each of the 100 iterations? Thank you. Jeff Flint ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.