add floyd-rivest selection and parallelization to gmedian

[ben-schwen]

Not directly demanded but improving benchmarks

Details

```r library(atime) library(data.table)

pkg.path = '.'
limit = 5

taken from .ci/atime/tests.R

pkg.edit.fun = function(old.Package, new.Package, sha, new.pkg.path) {
pkg_find_replace = function(glob, FIND, REPLACE) {
atime::glob_find_replace(file.path(new.pkg.path, glob), FIND, REPLACE)
}
Package_regex = gsub(".", "?", old.Package, fixed = TRUE)
Package = gsub(".", "", old.Package, fixed = TRUE)
new.Package = paste0(Package_, "", sha)
pkg_find_replace(
"DESCRIPTION",
paste0("Package:\s+", old.Package),
paste("Package:", new.Package))
pkg_find_replace(
file.path("src", "Makevars.*in"),
Package_regex,
new.Package)
pkg_find_replace(
file.path("R", "onLoad.R"),
Package_regex,
new.Package_)
pkg_find_replace(
file.path("R", "onLoad.R"),
sprintf('packageVersion\("%s"\)', old.Package),
sprintf('packageVersion\("%s"\)', new.Package))
pkg_find_replace(
file.path("src", "init.c"),
paste0("R_init_", Package_regex),
paste0("R_init_", gsub("[.]", "", new.Package)))
pkg_find_replace(
"NAMESPACE",
sprintf('useDynLib\("?%s"?', Package_regex),
paste0('useDynLib(', new.Package_))
}

versions = c(
'before' = 'b0c4ac3b',
'after' = 'df05b9d0'
)

set.seed(123)

===== Benchmark 1: Small groups (10 elements each) =====

N = as.integer(10^seq(2, 6, 0.25))
grpsize_small = 10

gmedian_work1 = lapply(setNames(nm = N), function(n) {
data.table(
grp = rep(1:n, each = grpsize_small),
x = rnorm(n * grpsize_small)
)
})

gmedian_bench1 = atime_versions(
pkg.path, N,
setup = {
dt = gmedian_work1[[as.character(N)]]
},
expr = data.table:::[.data.table(dt, , .(med = median(x)), by = grp),
seconds.limit = limit, verbose = TRUE, sha.vec = versions,
pkg.edit.fun = pkg.edit.fun
)
plot(gmedian_bench1)

===== Benchmark 2: Medium groups (100 elements each) =====

N = as.integer(10^seq(2, 5, 0.25))
grpsize_medium = 100

gmedian_work2 = lapply(setNames(nm = N), function(n) {
data.table(
grp = rep(1:n, each = grpsize_medium),
x = rnorm(n * grpsize_medium)
)
})

gmedian_bench2 = atime_versions(
pkg.path, N,
setup = {
dt = gmedian_work2[[as.character(N)]]
},
expr = data.table:::[.data.table(dt, , .(med = median(x)), by = grp),
seconds.limit = limit, verbose = TRUE, sha.vec = versions,
pkg.edit.fun = pkg.edit.fun
)
plot(gmedian_bench2)

===== Benchmark 3: Large groups (1000 elements each) =====

N = as.integer(10^seq(2, 4.5, 0.25))
grpsize_large = 1000

gmedian_work3 = lapply(setNames(nm = N), function(n) {
data.table(
grp = rep(1:n, each = grpsize_large),
x = rnorm(n * grpsize_large)
)
})

gmedian_bench3 = atime_versions(
pkg.path, N,
setup = {
dt = gmedian_work3[[as.character(N)]]
},
expr = data.table:::[.data.table(dt, , .(med = median(x)), by = grp),
seconds.limit = limit, verbose = TRUE, sha.vec = versions,
pkg.edit.fun = pkg.edit.fun
)
plot(gmedian_bench3)

===== Benchmark 4: Very large groups (10000 elements each) =====

N = as.integer(10^seq(1, 3.5, 0.25))
grpsize_vlarge = 10000

gmedian_work4 = lapply(setNames(nm = N), function(n) {
data.table(
grp = rep(1:n, each = grpsize_vlarge),
x = rnorm(n * grpsize_vlarge)
)
})

gmedian_bench4 = atime_versions(
pkg.path, N,
setup = {
dt = gmedian_work4[[as.character(N)]]
},
expr = data.table:::[.data.table(dt, , .(med = median(x)), by = grp),
seconds.limit = limit, verbose = TRUE, sha.vec = versions,
pkg.edit.fun = pkg.edit.fun
)
plot(gmedian_bench4)

===== Benchmark 5: Integer data =====

N = as.integer(10^seq(2, 4.5, 0.25))
grpsize_int = 1000

gmedian_work5 = lapply(setNames(nm = N), function(n) {
data.table(
grp = rep(1:n, each = grpsize_int),
x = sample(1L:1000L, n * grpsize_int, replace = TRUE)
)
})

gmedian_bench5 = atime_versions(
pkg.path, N,
setup = {
dt = gmedian_work5[[as.character(N)]]
},
expr = data.table:::[.data.table(dt, , .(med = median(x)), by = grp),
seconds.limit = limit, verbose = TRUE, sha.vec = versions,
pkg.edit.fun = pkg.edit.fun
)
plot(gmedian_bench5)

===== Benchmark 6: With NA values =====

N = as.integer(10^seq(2, 4.5, 0.25))
grpsize_na = 1000

gmedian_work6 = lapply(setNames(nm = N), function(n) {
dt = data.table(
grp = rep(1:n, each = grpsize_na),
x = rnorm(n * grpsize_na)
)

Set 10% of values to NA

dt[sample(.N, as.integer(.N * 0.1)), x := NA_real_]
dt
})

gmedian_bench6 = atime_versions(
pkg.path, N,
setup = {
dt = gmedian_work6[[as.character(N)]]
},
expr = data.table:::[.data.table(dt, , .(med = median(x, na.rm = TRUE)), by = grp),
seconds.limit = limit, verbose = TRUE, sha.vec = versions,
pkg.edit.fun = pkg.edit.fun
)
plot(gmedian_bench6)

===== SAVE BENCHMARK RESULTS =====

results_dir = "benchmark_results"
dir.create(results_dir, showWarnings = FALSE)

save(
gmedian_bench1, gmedian_bench2, gmedian_bench3,
gmedian_bench4, gmedian_bench5, gmedian_bench6,
file = file.path(results_dir, "benchmarks_gmedian.rda")
)

library(ggplot2)
library(patchwork)

load(file.path(results_dir, "benchmarks_gmedian.rda"))

g = function(p, title) p + ggtitle(title)

p = list(
g(plot(gmedian_bench1), "Small groups (10 elements)"),
g(plot(gmedian_bench2), "Medium groups (100 elements)"),
g(plot(gmedian_bench3), "Large groups (1000 elements)"),
g(plot(gmedian_bench4), "Very large groups (10000 elements)"),
g(plot(gmedian_bench5), "Integer data (1000 elements)"),
g(plot(gmedian_bench6), "With 10% NA values (1000 elements)")
)

pdf("benchmark_gmedian_results.pdf", width = 12, height = 9)
for (i in seq(1, length(p), by = 4)) {
pg = wrap_plots(p[i:min(i+3, length(p))], ncol = 2, nrow = 2, guides = "collect")
print(pg)
}
dev.off()

</details>

[codecov]

Codecov Report

❌ Patch coverage is 33.33333% with 6 lines in your changes missing coverage. Please review.
✅ Project coverage is 99.03%. Comparing base (b0c4ac3) to head (36742ea).
⚠️ Report is 22 commits behind head on master.

Files with missing lines	Patch %	Lines
src/quickselect.c	0.00%	6 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##           master    #7481      +/-   ##
==========================================
- Coverage   99.06%   99.03%   -0.04%     
==========================================
  Files          86       86              
  Lines       16618    16605      -13     
==========================================
- Hits        16463    16444      -19     
- Misses        155      161       +6     

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[github-actions]

No obvious timing issues in HEAD=gmedian

Generated via commit 36742ea

Download link for the artifact containing the test results: ↓ atime-results.zip

Task	Duration
R setup and installing dependencies	2 minutes and 57 seconds
Installing different package versions	22 seconds
Running and plotting the test cases	3 minutes and 0 seconds