data.table and parallel computing
I've done some tests per @matt dowle's prior mantra of Rprof, Rprof, Rprof.
What I find is that the decision to parallelize is context dependent; but is likely significant. Depending on the test operations (eg foo below, which can be customized) and the number of cores utilized (I try both 8 and 24), I get different results.
Below results:
- using 8 cores, I see a 21% improvement in this example for parallelization
- using 24 cores, I see 14% improvement.
I also look at some real-world (non shareable) data / operations which shows a larger (33% or 25%, two different tests) improvement paralellizing with 24 cores. Edit May 2018 A new set of real-world example cases are showing closer to 85% improvements from parallel operations with 1000 groups.
R> sessionInfo() # 24 core machine:
R version 3.3.2 (2016-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)
attached base packages:
[1] parallel stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] microbenchmark_1.4-2.1 stringi_1.1.2 data.table_1.10.4
R> sessionInfo() # 8 core machine:
R version 3.3.2 (2016-10-31)
Platform: x86_64-apple-darwin13.4.0 (64-bit)
Running under: macOS Sierra 10.12.4
attached base packages:
[1] parallel stats graphics grDevices utils datasets methods base
other attached packages:
[1] microbenchmark_1.4-2.1 stringi_1.1.5 data.table_1.10.4
Example below:
library(data.table)
library(stringi)
library(microbenchmark)
set.seed(7623452L)
my_grps <- stringi::stri_rand_strings(n= 5000, length= 10)
my_mat <- matrix(rnorm(1e5), ncol= 20)
dt <- data.table(grps= rep(my_grps, each= 20), my_mat)
foo <- function(dt) {
dt2 <- dt ## needed for .SD lock
nr <- nrow(dt2)
idx <- sample.int(nr, 1, replace=FALSE)
dt2[idx,][, `:=` (
new_var1= V1 / V2,
new_var2= V4 * V3 / V10,
new_var3= sum(V12),
new_var4= ifelse(V10 > 0, V11 / V13, 1),
new_var5= ifelse(V9 < 0, V8 / V18, 1)
)]
return(dt2[idx,])
}
split_df <- function(d, var) {
base::split(d, get(var, as.environment(d)))
}
foo2 <- function(dt) {
dt2 <- split_df(dt, "grps")
require(parallel)
cl <- parallel::makeCluster(min(nrow(dt), parallel::detectCores()))
clusterExport(cl, varlist= "foo")
clusterExport(cl, varlist= "dt2", envir = environment())
clusterEvalQ(cl, library("data.table"))
dt2 <- parallel::parLapply(cl, X= dt2, fun= foo)
parallel::stopCluster(cl)
return(rbindlist(dt2))
}
print(parallel::detectCores()) # 8
microbenchmark(
serial= dt[,foo(.SD), by= "grps"],
parallel= foo2(dt),
times= 10L
)
Unit: seconds
expr min lq mean median uq max neval cld
serial 6.962188 7.312666 8.433159 8.758493 9.287294 9.605387 10 b
parallel 6.563674 6.648749 6.976669 6.937556 7.102689 7.654257 10 a
print(parallel::detectCores()) # 24
Unit: seconds
expr min lq mean median uq max neval cld
serial 9.014247 9.804112 12.17843 13.17508 13.56914 14.13133 10 a
parallel 10.732106 10.957608 11.17652 11.06654 11.30386 12.28353 10 a
Profiling:
We can use this answer to provide a more direct response to @matt dowle's original comment to profiling.
As a result, we do see that the majority of compute time is handled by base and not data.table. data.table operations themselves are, as expected, exceptionally fast. While some might argue that this is evidence that there is no need for parallelism within data.table, I posit that this workflow/operation-set is not atypical. That is, it is my strong suspicion that the majority of large data.table aggregation involve a substantial amount of non-data.table code; and that this is correlated with interactive use vs development / production use. I therefore conclude that parallelism would be valuable within data.table for large aggregations.
library(profr)
prof_list <- replicate(100, profr::profr(dt[,foo(.SD), by= "grps"], interval = 0.002),
simplify = FALSE)
pkg_timing <- fun_timing <- vector("list", length= 100)
for (i in 1:100) {
fun_timing[[i]] <- tapply(prof_list[[i]]$time, paste(prof_list[[i]]$source, prof_list[[i]]$f, sep= "::"), sum)
pkg_timing[[i]] <- tapply(prof_list[[i]]$time, prof_list[[i]]$source, sum)
}
sort(sapply(fun_timing, sum)) # no large outliers
fun_timing2 <- rbindlist(lapply(fun_timing, function(x) {
ret <- data.table(fun= names(x), time= x)
ret[, pct_time := time / sum(time)]
return(ret)
}))
pkg_timing2 <- rbindlist(lapply(pkg_timing, function(x) {
ret <- data.table(pkg= names(x), time= x)
ret[, pct_time := time / sum(time)]
return(ret)
}))
fun_timing2[, .(total_time= sum(time),
avg_time= mean(time),
avg_pct= round(mean(pct_time), 4)), by= "fun"][
order(avg_time, decreasing = TRUE),][1:10,]
pkg_timing2[, .(total_time= sum(time),
avg_time= mean(time),
avg_pct= round(mean(pct_time), 4)), by= "pkg"][
order(avg_time, decreasing = TRUE),]
Results:
fun total_time avg_time avg_pct
1: base::[ 670.362 6.70362 0.2694
2: NA::[.data.table 667.350 6.67350 0.2682
3: .GlobalEnv::foo 335.784 3.35784 0.1349
4: base::[[ 163.044 1.63044 0.0655
5: base::[[.data.frame 133.790 1.33790 0.0537
6: base::%in% 120.512 1.20512 0.0484
7: base::sys.call 86.846 0.86846 0.0348
8: NA::replace_dot_alias 27.824 0.27824 0.0112
9: base::which 23.536 0.23536 0.0095
10: base::sapply 22.080 0.22080 0.0089
pkg total_time avg_time avg_pct
1: base 1397.770 13.97770 0.7938
2: .GlobalEnv 335.784 3.35784 0.1908
3: data.table 27.262 0.27262 0.0155
crossposted in github/data.table
First thing to check is that data.table FAQ 3.1 point 2 has sunk in :
One memory allocation is made for the largest group only, then that memory is reused for the other groups. There is very little garbage to collect.
That's one reason data.table grouping is quick. But this approach doesn't lend itself to parallelization. Parallelizing means copying the data to the other threads, instead, costing time. But, my understanding is that data.table grouping is usually faster than plyr with .parallel on anyway. It depends on the computation time of the task for each group, and if that compute time can be easily reduced or not. Moving the data around often dominates (when benchmarking 1 or 3 runs of large data tasks).
More often, so far, it's actually some gotcha that's biting in the j expression of [.data.table. For example, recently we saw poor performance from data.table grouping but the culprit turned out to be min(POSIXct) (Aggregating in R over 80K unique ID's). Avoiding that gotcha yielded over 50 times speedup.
So the mantra is: Rprof, Rprof, Rprof.
Further, point 1 from the same FAQ might be significant :
Only that column is grouped, the other 19 are ignored because data.table inspects the j expression and realises it doesn’t use the other columns.
So, data.table really doesn't follow the split-apply-combine paradigm at all. It works differently. split-apply-combine lends itself to parallelization but it really doesn't scale to large data.
Also see footnote 3 in the data.table intro vignette :
We wonder how many people are deploying parallel techniques to code that is vector scanning
That's trying to say "sure, parallel is significantly faster, but how long should it really take with an efficient algorithm?".
BUT if you've profiled (using Rprof), and the task per group really is compute intensive, then the 3 posts on datatable-help including the word "multicore" might help:
multicore posts on datatable-help
Of course there are many tasks where parallelization would be nice in data.table, and there is a way to do it. But it hasn't been done yet, since usually other factors bite, so it's been low priority. If you can post reproducible dummy data with benchmarks and Rprof results, that would help increase the priority.