How many cores is optimal in parallel processing?

Question

Say I have an 8 core CPU. Using doParallel in R, when I register makeCluster(x), what is the ideal number of cores, x, to use?

Is it as many cores as possible? Or would using 7 cores be slower than using 6 cores? Are there any rules around this?

Answer 1

As stated in the comments, the optimal amount of cores depends on the task at hand, but you can find out yourself. Initialize 7 different clusters and benchmark the results. I wouldnt go with all 8 cores, so 7 should be maximum in your case.

Here's a small "silly" template where parallelization doesnt make sense as the simple sapply version is a lot faster because the overhead sent to the cores drastically reduces performance.

Anyway, insert the code you want to optimize, play around and find the perfect setting ;)

require(parallel)
cl2 = makeCluster(2)
cl3 = makeCluster(3)
cl4 = makeCluster(4)
cl5 = makeCluster(5)
cl6 = makeCluster(6)
cl7 = makeCluster(7)

library(microbenchmark)
mc <- microbenchmark(times = 100,
                     noPa = {
                       res = sapply(mtcars, mean, na.rm = TRUE)
                     },
                     cor2 = {
                       res = parSapply(cl2, mtcars, mean, na.rm = TRUE)
                     },
                     cor3 = {
                       res = parSapply(cl3, mtcars, mean, na.rm = TRUE)
                     },
                     cor4 = {
                       res = parSapply(cl4, mtcars, mean, na.rm = TRUE)
                     },
                     cor5 = {
                       res = parSapply(cl5, mtcars, mean, na.rm = TRUE)
                     },
                     cor6 = {
                       res = parSapply(cl6, mtcars, mean, na.rm = TRUE)
                     },
                     cor7 = {
                       res = parSapply(cl7, mtcars, mean, na.rm = TRUE)
                     }
); mc

stopCluster(cl2);stopCluster(cl3);stopCluster(cl4);
stopCluster(cl5);stopCluster(cl6);stopCluster(cl7)

Unit: microseconds
 expr      min        lq       mean   median        uq       max neval
 noPa   77.370   94.4365   97.52549   97.281  101.5475   131.983   100
 cor2  713.388  804.1260  947.56529  836.553  887.4680  7178.812   100
 cor3  840.250  941.2275 1071.55460  967.681 1027.4145  5343.576   100
 cor4  877.797 1046.7570 1194.51996 1077.761 1132.3745  7028.057   100
 cor5 1032.535 1139.2015 1303.64424 1190.686 1241.3170  8148.199   100
 cor6 1141.761 1222.5430 1438.18655 1261.797 1339.1655 10589.302   100
 cor7 1269.192 1345.4240 1586.03513 1399.468 1487.3615 10547.204   100

And here an example where it would make sense to parallelize. Based on the results 7 cores would be the fastest solution. If you run it on your own machine and want to do other stuff next to it, I would go with 4 cores as the timings are comparable and the machine is not working at max capacity.

library(lme4)
f <- function(i) {
  lmer(Petal.Width ~ . - Species + (1 | Species), data = iris)
}

library(microbenchmark)
mc <- microbenchmark(times = 3,
                     noPa = {
                       res = sapply(1:100, f)
                     },
                     cor2 = {
                       res = parSapply(cl2, 1:100, f)
                     },
                     cor3 = {
                       res = parSapply(cl3, 1:100, f)
                     },
                     cor4 = {
                       res = parSapply(cl4, 1:100, f)
                     },
                     cor5 = {
                       res = parSapply(cl5, 1:100, f)
                     },
                     cor6 = {
                       res = parSapply(cl6, 1:100, f)
                     },
                     cor7 = {
                       res = parSapply(cl7, 1:100, f)
                     }
); mc

Unit: milliseconds
 expr       min        lq      mean    median       uq      max neval
 noPa 1925.2889 1964.9473 2169.9294 2004.6057 2292.250 2579.894     3
 cor2 1501.8176 1591.5596 1722.1834 1681.3015 1832.366 1983.431     3
 cor3 1097.4251 1188.6271 1345.1643 1279.8291 1469.034 1658.239     3
 cor4  956.9829 1007.6607 1302.2984 1058.3384 1474.956 1891.574     3
 cor5 1027.5877 1872.3501 2379.9384 2717.1125 3056.114 3395.115     3
 cor6 1001.2572 1048.8277 1217.5999 1096.3983 1325.771 1555.144     3
 cor7  815.2055  905.7948  945.7555  996.3841 1011.030 1025.677     3