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test and confirm new parallel subset performance #3175
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Following script tests subset by integer row ids. It also measures the timing of
!anyNAbranch. For testing openmp overhead it should be enough.vim dt-parallel-subset.R
args = as.integer(commandArgs(TRUE)) th = args[1L] N = args[2L] K = 100L get_i = function(n.out, n.in) { n.out = as.integer(n.out) n.in = as.integer(n.in) set.seed(n.out) sample(n.in, n.out) } library(data.table) cat(sprintf("# datagen %s rows\n", N)) set.seed(108) DT = data.table( id1 = sample(sprintf("id%03d",1:K), N, TRUE), # large groups (char) id2 = sample(sprintf("id%03d",1:K), N, TRUE), # large groups (char) id3 = sample(sprintf("id%010d",1:(N/K)), N, TRUE), # small groups (char) id4 = sample(K, N, TRUE), # large groups (int) id5 = sample(K, N, TRUE), # large groups (int) id6 = sample(N/K, N, TRUE), # small groups (int) v1 = sample(5, N, TRUE), # int in range [1,5] v2 = sample(5, N, TRUE), # int in range [1,5] v3 = sample(round(runif(100,max=100),4), N, TRUE) # numeric e.g. 23.5749 ) cat(sprintf("# setDTthreads(%s)\n", th)) setDTthreads(th) cat("# 0 row (first `[`` call overhead):\n") system.time(ans<-DT[0L]) cat("# 1 row:\n") i = get_i(1L, nrow(DT)) system.time(ans<-DT[i]) cat("# 2 rows:\n") i = get_i(2L, nrow(DT)) system.time(ans<-DT[i]) cat("# 5 rows:\n") i = get_i(5L, nrow(DT)) system.time(ans<-DT[i]) cat("# 10% of rows:\n") i = get_i(nrow(DT)*0.1, nrow(DT)) system.time(ans<-DT[i]) q("no")
Rscript dt-parallel-subset.R 1 1e6
timings coming soon
1th 1e7
> Rscript dt-parallel-subset.R 1 1e7 # datagen 10000000 rows # setDTthreads(1) # 0 row (first `[`` call overhead): user system elapsed 0.005 0.000 0.005 # 1 row: user system elapsed 0 0 0 # 2 rows: user system elapsed 0.000 0.000 0.001 # 5 rows: user system elapsed 0.000 0.000 0.001 # 10% of rows: user system elapsed 0.153 0.012 0.16520th 1e7
> Rscript dt-parallel-subset.R 20 1e7 # datagen 10000000 rows # setDTthreads(20) # 0 row (first `[`` call overhead): user system elapsed 0.033 0.000 0.007 # 1 row: user system elapsed 0 0 0 # 2 rows: user system elapsed 0.001 0.000 0.000 # 5 rows: user system elapsed 0 0 0 # 10% of rows: user system elapsed 0.440 0.039 0.1031th 1e8
> Rscript dt-parallel-subset.R 1 1e8 # datagen 100000000 rows # setDTthreads(1) # 0 row (first `[`` call overhead): user system elapsed 0.006 0.000 0.005 # 1 row: user system elapsed 0.001 0.000 0.000 # 2 rows: user system elapsed 0 0 0 # 5 rows: user system elapsed 0.001 0.000 0.000 # 10% of rows: user system elapsed 2.393 0.132 2.52420th 1e8
> Rscript dt-parallel-subset.R 20 1e8 # datagen 100000000 rows # setDTthreads(20) # 0 row (first `[`` call overhead): user system elapsed 0.054 0.004 0.010 # 1 row: user system elapsed 0.000 0.000 0.001 # 2 rows: user system elapsed 0.001 0.000 0.000 # 5 rows: user system elapsed 0.000 0.000 0.001 # 10% of rows: user system elapsed 4.218 0.284 1.2651th 1e9
> Rscript dt-parallel-subset.R 1 1e9 # datagen 1000000000 rows # setDTthreads(1) # 0 row (first `[`` call overhead): user system elapsed 0.005 0.000 0.006 # 1 row: user system elapsed 0.001 0.000 0.000 # 2 rows: user system elapsed 0.000 0.000 0.001 # 5 rows: user system elapsed 0 0 0 # 10% of rows: user system elapsed 33.478 1.460 34.93820th 1e9
> Rscript dt-parallel-subset.R 20 1e9 # datagen 1000000000 rows # setDTthreads(20) # 0 row (first `[`` call overhead): user system elapsed 0.057 0.000 0.009 # 1 row: user system elapsed 0.001 0.000 0.001 # 2 rows: user system elapsed 0 0 0 # 5 rows: user system elapsed 0 0 0 # 10% of rows: user system elapsed 58.295 2.454 20.285- added a commit that references this issue
on Jan 24, 2019 During the timings above I observed that team of threads was started even for 1, 2, 5 rows. Still it did not result in noticeable overhead. All subsets of 1, 2, 5 rows were 0.000-0.001.
Above checks were using single subset operation. I encounter some noticeable difference when I loop over subset operation.
library(data.table) m = matrix(1L, nrow=1e8, ncol=10) DT = as.data.table(m) setDTthreads(20) system.time(for (i in 1:1000) DT[i,]) # user system elapsed # 4.210 0.000 0.229 setDTthreads(1) system.time(for (i in 1:1000) DT[i,]) # user system elapsed # 0.107 0.007 0.114
@mattdowle does it quality for reopen?
PR #4484 closes this one.
v1.12.8 to confirm Jan's result:
> m = matrix(1L, nrow=1e8, ncol=10) > DT = as.data.table(m) > setDTthreads(0) > system.time(for (i in 1:1000) DT[i,]) user system elapsed 1.512 0.000 0.143 > setDTthreads(1) > system.time(for (i in 1:1000) DT[i,]) user system elapsed 0.083 0.000 0.083
With #4484 :
> setDTthreads(0) > system.time(for (i in 1:1000) DT[i,]) user system elapsed 0.071 0.000 0.071 > setDTthreads(1) > system.time(for (i in 1:1000) DT[i,]) user system elapsed 0.072 0.000 0.072
Matt commented :
data.table/src/subset.c
Lines 27 to 30 in 1847500