# Test Drive of Parallel Computing with R

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Today, I did a test run of parallel computing with snow and multicore packages in R and compared the parallelism with the single-thread lapply() function.

In the test code below, a data.frame with 20M rows is simulated in a Ubuntu VM with 8-core CPU and 10-G memory. As the baseline, lapply() function is employed to calculate the aggregation by groups. For the comparison purpose, parLapply() function in snow package and mclapply() in multicore package are also used to generate the identical aggregated data.

n <- 20000000 set.seed(2013) df <- data.frame(id = sample(20, n, replace = TRUE), x = rnorm(n), y = runif(n), z = rpois(n, 1)) library(rbenchmark) benchmark(replications = 5, order = "user.self", LAPPLY = { cat('LAPPLY...\n') df1 <- data.frame(lapply(split(df[-1], df[1]), colMeans)) }, SNOW = { library(snow) cat('SNOW...\n') cl <- makeCluster(8, type = "SOCK") df2 <- data.frame(parLapply(cl, split(df[-1], df[1]), colMeans)) stopCluster(cl) }, MULTICORE = { cat('MULTICORE...\n') library(multicore) df3 <- data.frame(mclapply(split(df[-1], df[1]), colMeans, mc.cores = 8)) } ) library(compare) all.equal(df1, df2) all.equal(df1, df3)

Below is the benchmark output. As shown, the parallel solution, e.g. SNOW or MULTICORE, is 3 times more efficient than the baseline solution, e.g. LAPPLY, in terms of user time.

test replications elapsed relative user.self sys.self user.child 3 MULTICORE 5 101.075 1.000 48.587 6.620 310.771 2 SNOW 5 127.715 1.264 53.192 13.685 0.012 1 LAPPLY 5 184.738 1.828 179.855 4.880 0.000 sys.child 3 7.764 2 0.740 1 0.000 Attaching package: ‘compare’ The following object is masked from ‘package:base’: isTRUE [1] TRUE [1] TRUE

In order to illustrate the CPU usage, multiple screenshots have also been taken to show the difference between parallelism and single-thread.

In the first screenshot, it is shown that only 1 out of 8 CPUs is used at 100% with lapply() function and the rest 7 are idle.

In the second screenshot, it is shown that all 8 CPUs are used at 100% with parLapply() function in the snow package.

In the third screenshot, it is also shown that all 8 CPUs are used at 100% with mulapply() function in the multicore package.

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