Faster i

[ColeMiller1]

Towards #3735 (maybe closes...?)
Closes this comment in the code:

TODO: Incorporate which_ here on DT[!i] where i is logical. Should avoid i = !i (above) - inefficient.

dt[i, ] is around twice as fast than before.

library(data.table)
## Base
allIterations <- data.frame(v1 = runif(1e5), v2 = runif(1e5))
DoSomething <- function(row) someCalculation <- row[["v1"]] + 1
system.time({for (r in 1:nrow(allIterations)) {DoSomething(allIterations[r, ])}})

##   user  system elapsed 
##   5.67    0.02    5.91 

setDT(allIterations)
system.time({for (r in 1:nrow(allIterations)) {DoSomething(allIterations[r, ])}})

## Before Patch
##   user  system elapsed 
##  17.53    0.58   18.46

## After Patch
##   user  system elapsed 
##   9.53    0.00    9.67 

For dt[!lgl] we see a lot of memory savings with some speed savings:

library(data.table)
set.seed(123L)
n = 1e8L
dt = data.table(rep.int(1L, n))
inds = sample(c(FALSE, TRUE), n, TRUE)
bench::mark(dt[!inds])

## Before Patch
##  expression   min median `itr/sec` mem_alloc
##  <bch:expr> <bch> <bch:>     <dbl> <bch:byt>
##1 dt[!inds]  1150ms  1150ms     0.873    1.12GB

## After Patch
##  expression   min median `itr/sec` mem_alloc
##  <bch:expr> <bch> <bch:>     <dbl> <bch:byt>
##1 dt[!inds]  925ms  925ms      1.08     763MB

CconvertNegAndZeroIdx is also faster and also includes break when threads are now 1. Also, avoiding the OpenMP when threads are set to 1 also improves performance on at least Windows.

Note - there probably could be follow-up PRs related to the default number of threads (for me on only 2T, somewhere between 1E5 and 1E6 is where the break even point is). Secondly, c(0, seq_len(1025L)) is somehow faster than seq_len(1025L) within the function with this early break. It just seems surprising that somehow removing a zero is faster than returning the inds as is.

library(data.table)

## small scenario just over the 1024 row threshold of 2 threads:
inds = seq_len(1025L)
system.time(for (i in 1:100000) .Call(data.table:::CconvertNegAndZeroIdx, inds, 2000L, TRUE))

setDTthreads(1L)
##   user  system elapsed 
##   1.05    0.00    1.22 

setDTthreads(2L)
##   user  system elapsed 
##   2.90    1.52    4.61  

## early break scenario which is best case scenario
inds = c(0L, inds)

system.time(for (i in 1:100000) .Call(data.table:::CconvertNegAndZeroIdx, inds, 2000L, TRUE))

setDTthreads(1L)
##   user  system elapsed 
##   0.62    0.00    0.63 

setDTthreads(2L)
##   user  system elapsed 
##   3.75    1.54    5.73 

## a normal scenario - 1 million row 
inds = seq_len(1e6L)
system.time(for (i in 1:5000) .Call(data.table:::CconvertNegAndZeroIdx, inds, 2000L, TRUE))

setDTthreads(1L)
##   user  system elapsed 
##   4.98    0.00    5.09 

setDTthreads(2L)
##   user  system elapsed 
##   7.27    0.12    4.19 

[codecov]

Codecov Report

Merging #4585 into master will decrease coverage by 0.02%.
The diff coverage is 95.60%.

@@            Coverage Diff             @@
##           master    #4585      +/-   ##
==========================================
- Coverage   99.61%   99.58%   -0.03%     
==========================================
  Files          73       73              
  Lines       14119    14120       +1     
==========================================
- Hits        14064    14061       -3     
- Misses         55       59       +4     

Impacted Files	Coverage Δ
src/subset.c	98.00% <60.00%> (-2.00%)	⬇️
R/data.table.R	100.00% <100.00%> (ø)

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[MichaelChirico]

what errors are being caught here?

[ColeMiller1]

Here's the test. In this case, cols is missing I believe.

# no error when j is supplied but inherits missingness from caller
DT = data.table(a=1:3, b=4:6)
f = function(cols) DT[,cols]
test(1974.1, f(), output="a.*b.*3:.*6")

edit: I did try removing this branch but it produced errors. It's a real head scratcher but I just kept it. It's only been moved.

[MichaelChirico]

can we add .SD=x to envir arg here & get .SD to work in i just like that?

[ColeMiller1]

I just compiled with adding .SD and success!

Note, previously .N was assigned to the parent.frame() and then restoring it if necessary. Because of that, all 4 eval calls related to processing i were largely the same.

While skipping that approach is faster, we now have to deal with associating each of the 4 eval calls with .N or whatever special variable(s) we want to use so there's a little more accounting. In theory we could have also used the previous approach to also assign .SD to the parent.frame.

[MichaelChirico]

ha, came across this comment again 🙃

[ColeMiller1]

The OpenMP loop is what is missing in coverage. I am unsure - I foolishly included Rprintf("OpenMP_Loop") within the loop and during at least one of the tests, my console was full of "OpenMP_Loop" statements. That would suggest that the code coverage bot only has 1 thread, but I would have expected similar issues in #4558 as I incorporated the approach from that PR.