17  Parallel Computing

D Decrease Computing Time Using Multiple CPUs bi R Built-in parallel  Package D->bi hm Simple Front-end to parallel: runParallel bi->hm

The Hmisc runParallel function makes it easy to use available multiprocessor cores to speed up parallel computations especially for simulations. By default it runs the number of available cores, less one. runParallel makes the parallel package easier to use and does recombinations over per-core batches. The user writes a function that does the work on one core, and the same function is run on all cores. This function has set arguments and must return a named list. A base random number seed is given, and the seed is set to this plus i for core number i. The total number of repetitions is given, and this most balanced possible number of repetitions is run on each core to sum to the total desired number of iterations. runifChanged is again used, to avoid running the simulations if no inputs have changed.

# Loads Hmisc which has the runParallel function

# Function to do simulations on one core
run1 <- function(reps, showprogress, core) {
  cof <- matrix(NA, nrow=reps, ncol=3,
                dimnames=list(NULL, .q(a, b1, b2)))
  for(i in 1 : reps) {
    y <- sample(0:1, n, replace=TRUE)
    f <- lrm(y ~ X)
    cof[i, ] <- coef(f)
# Debug one core run, with only 3 iterations
n    <- 300
seed <- 3
X    <- cbind(x1=runif(n), x2=runif(n))  # condition on covariates
              a         b1        b2
[1,] -0.5455330  0.9572896 0.2215974
[2,] -0.2459193  0.3081405 0.1284809
[3,] -0.1391344 -0.2668562 0.1792319
nsim <- 5000
g <- function() runParallel(run1, reps=nsim, seed=seed)
Coefs <- runifChanged(g, X, run1, nsim, seed)
[1] 5000    3
apply(Coefs, 2, mean)
            a            b1            b2 
 0.0020121803 -0.0007277216 -0.0003258610