cli progress bar benchmark

Introduction

We make sure that the timer is not TRUE, by setting it to ten hours.

library(cli)
# 10 hours
cli:::cli_tick_set(10 * 60 * 60 * 1000)
cli_tick_reset()
#> NULL
`__cli_update_due`
#> [1] FALSE

R benchmarks

The timer

fun <- function() NULL
ben_st <- bench::mark(
  `__cli_update_due`,
  fun(),
  .Call(ccli_tick_reset),
  interactive(),
  check = FALSE
)
ben_st
#> # A tibble: 4 × 6
#>   expression                  min   median   `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr>             <bch:tm> <bch:tm>       <dbl> <bch:byt>    <dbl>
#> 1 __cli_update_due              0        0 1582887653.        0B        0
#> 2 fun()                    99.9ns    200ns    3294497.        0B        0
#> 3 .Call(ccli_tick_reset)   99.9ns    200ns    5362941.        0B        0
#> 4 interactive()                 0        0  803371233.        0B        0

ben_st2 <- bench::mark(
  if (`__cli_update_due`) foobar()
)
ben_st2
#> # A tibble: 1 × 6
#>   expression                            min median itr/s…¹ mem_a…² gc/se…³
#>   <bch:expr>                       <bch:tm> <bch:>   <dbl> <bch:b>   <dbl>
#> 1 if (`__cli_update_due`) foobar()        0  100ns  1.23e7      0B       0
#> # … with abbreviated variable names ¹`itr/sec`, ²mem_alloc, ³`gc/sec`

`cli_progress_along()`

seq <- 1:100000
ta <- cli_progress_along(seq)
bench::mark(seq[[1]], ta[[1]])
#> # A tibble: 2 × 6
#>   expression      min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr> <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 seq[[1]]     99.9ns    200ns  4811252.        0B        0
#> 2 ta[[1]]      99.9ns    200ns  4223237.        0B        0

`for` loop

This is the baseline:

f0 <- function(n = 1e5) {
  x <- 0
  seq <- 1:n
  for (i in seq) {
    x <- x + i %% 2
  }
  x
}

With progress bars:

fp <- function(n = 1e5) {
  x <- 0
  seq <- 1:n
  for (i in cli_progress_along(seq)) {
    x <- x + seq[[i]] %% 2
  }
  x
}

Overhead per iteration:

ben_taf <- bench::mark(f0(), fp())
ben_taf
#> # A tibble: 2 × 6
#>   expression      min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr> <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 f0()         21.7ms   21.7ms      46.1    25.2KB     785.
#> 2 fp()           28ms     28ms      35.7    83.2KB     571.
(ben_taf$median[2] - ben_taf$median[1]) / 1e5
#> [1] 63.5ns

ben_taf2 <- bench::mark(f0(1e6), fp(1e6))
#> Warning: Some expressions had a GC in every iteration; so filtering is
#> disabled.
ben_taf2
#> # A tibble: 2 × 6
#>   expression      min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr> <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 f0(1e+06)     264ms    269ms      3.72        0B     35.3
#> 2 fp(1e+06)     270ms    275ms      3.64    1.85KB     34.6
(ben_taf2$median[2] - ben_taf2$median[1]) / 1e6
#> [1] 5.5ns

ben_taf3 <- bench::mark(f0(1e7), fp(1e7))
#> Warning: Some expressions had a GC in every iteration; so filtering is
#> disabled.
ben_taf3
#> # A tibble: 2 × 6
#>   expression      min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr> <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 f0(1e+07)     2.46s    2.46s     0.406        0B     38.9
#> 2 fp(1e+07)     2.69s    2.69s     0.372    1.85KB     34.6
(ben_taf3$median[2] - ben_taf3$median[1]) / 1e7
#> [1] 22.4ns

ben_taf4 <- bench::mark(f0(1e8), fp(1e8))
#> Warning: Some expressions had a GC in every iteration; so filtering is
#> disabled.
ben_taf4
#> # A tibble: 2 × 6
#>   expression      min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr> <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 f0(1e+08)     23.2s    23.2s    0.0432        0B     25.4
#> 2 fp(1e+08)     25.4s    25.4s    0.0394    1.85KB     22.4
(ben_taf4$median[2] - ben_taf4$median[1]) / 1e8
#> [1] 22.1ns

Mapping with `lapply()`

This is the baseline:

f0 <- function(n = 1e5) {
  seq <- 1:n
  ret <- lapply(seq, function(x) {
    x %% 2
  })
  invisible(ret)
}

With an index vector:

f01 <- function(n = 1e5) {
  seq <- 1:n
  ret <- lapply(seq_along(seq), function(i) {
    seq[[i]] %% 2
  })
  invisible(ret)
}

With progress bars:

fp <- function(n = 1e5) {
  seq <- 1:n
  ret <- lapply(cli_progress_along(seq), function(i) {
    seq[[i]] %% 2
  })
  invisible(ret)
}

Overhead per iteration:

ben_tam <- bench::mark(f0(), f01(), fp())
#> Warning: Some expressions had a GC in every iteration; so filtering is
#> disabled.
ben_tam
#> # A tibble: 3 × 6
#>   expression      min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr> <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 f0()          115ms    138ms      7.31     781KB    20.1 
#> 2 f01()         195ms    208ms      4.26     781KB     9.94
#> 3 fp()          126ms    144ms      7.12     783KB    12.5
(ben_tam$median[3] - ben_tam$median[1]) / 1e5
#> [1] 59.8ns

ben_tam2 <- bench::mark(f0(1e6), f01(1e6), fp(1e6))
#> Warning: Some expressions had a GC in every iteration; so filtering is
#> disabled.
ben_tam2
#> # A tibble: 3 × 6
#>   expression      min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr> <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 f0(1e+06)     1.38s    1.38s     0.725    7.63MB     5.80
#> 2 f01(1e+06)    2.38s    2.38s     0.420    7.63MB     3.36
#> 3 fp(1e+06)     3.26s    3.26s     0.307    7.63MB     2.45
(ben_tam2$median[3] - ben_tam2$median[1]) / 1e6
#> [1] 1.88µs
(ben_tam2$median[3] - ben_tam2$median[2]) / 1e6
#> [1] 880ns

Mapping with purrr

This is the baseline:

f0 <- function(n = 1e5) {
  seq <- 1:n
  ret <- purrr::map(seq, function(x) {
    x %% 2
  })
  invisible(ret)
}

With index vector:

f01 <- function(n = 1e5) {
  seq <- 1:n
  ret <- purrr::map(seq_along(seq), function(i) {
    seq[[i]] %% 2
  })
  invisible(ret)
}

With progress bars:

fp <- function(n = 1e5) {
  seq <- 1:n
  ret <- purrr::map(cli_progress_along(seq), function(i) {
    seq[[i]] %% 2
  })
  invisible(ret)
}

Overhead per iteration:

ben_pur <- bench::mark(f0(), f01(), fp())
ben_pur
#> # A tibble: 3 × 6
#>   expression      min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr> <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 f0()           91ms   98.1ms     10.2      870KB     2.55
#> 2 f01()         102ms  105.2ms      9.41     781KB     6.28
#> 3 fp()          104ms  109.2ms      9.10     783KB     2.28
(ben_pur$median[3] - ben_pur$median[1]) / 1e5
#> [1] 111ns
(ben_pur$median[3] - ben_pur$median[2]) / 1e5
#> [1] 39.4ns

ben_pur2 <- bench::mark(f0(1e6), f01(1e6), fp(1e6))
#> Warning: Some expressions had a GC in every iteration; so filtering is
#> disabled.
ben_pur2
#> # A tibble: 3 × 6
#>   expression      min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr> <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 f0(1e+06)     1.12s    1.12s     0.895    7.63MB     1.79
#> 2 f01(1e+06)    1.29s    1.29s     0.775    7.63MB     1.55
#> 3 fp(1e+06)     1.44s    1.44s     0.696    7.63MB     2.09
(ben_pur2$median[3] - ben_pur2$median[1]) / 1e6
#> [1] 319ns
(ben_pur2$median[3] - ben_pur2$median[2]) / 1e6
#> [1] 146ns

`ticking()`

f0 <- function(n = 1e5) {
  i <- 0
  x <- 0 
  while (i < n) {
    x <- x + i %% 2
    i <- i + 1
  }
  x
}

fp <- function(n = 1e5) {
  i <- 0
  x <- 0 
  while (ticking(i < n)) {
    x <- x + i %% 2
    i <- i + 1
  }
  x
}

ben_tk <- bench::mark(f0(), fp())
#> Warning: Some expressions had a GC in every iteration; so filtering is
#> disabled.
ben_tk
#> # A tibble: 2 × 6
#>   expression      min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr> <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 f0()        21.58ms  22.48ms    44.2      40.3KB     1.92
#> 2 fp()          5.67s    5.67s     0.176   100.8KB     1.41
(ben_tk$median[2] - ben_tk$median[1]) / 1e5
#> [1] 56.5µs

Traditional API

f0 <- function(n = 1e5) {
  x <- 0
  for (i in 1:n) {
    x <- x + i %% 2
  }
  x
}

fp <- function(n = 1e5) {
  cli_progress_bar(total = n)
  x <- 0
  for (i in 1:n) {
    x <- x + i %% 2
    cli_progress_update()
  }
  x
}

ff <- function(n = 1e5) {
  cli_progress_bar(total = n)
  x <- 0
  for (i in 1:n) {
    x <- x + i %% 2
    if (`__cli_update_due`) cli_progress_update()
  }
  x
}

ben_api <- bench::mark(f0(), ff(), fp())
#> Warning: Some expressions had a GC in every iteration; so filtering is
#> disabled.
ben_api
#> # A tibble: 3 × 6
#>   expression      min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr> <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 f0()         19.5ms  19.58ms    46.9      19.9KB     3.91
#> 2 ff()        28.68ms     29ms    32.7      28.6KB     1.92
#> 3 fp()          2.94s    2.94s     0.340      26KB     1.36
(ben_api$median[3] - ben_api$median[1]) / 1e5
#> [1] 29.2µs
(ben_api$median[2] - ben_api$median[1]) / 1e5
#> [1] 94.2ns

ben_api2 <- bench::mark(f0(1e6), ff(1e6), fp(1e6))
#> Warning: Some expressions had a GC in every iteration; so filtering is
#> disabled.
ben_api2
#> # A tibble: 3 × 6
#>   expression      min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr> <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 f0(1e+06)   219.8ms    228ms    4.39          0B     4.39
#> 2 ff(1e+06)   301.5ms  310.9ms    3.22      1.85KB     1.61
#> 3 fp(1e+06)     32.4s    32.4s    0.0308    1.85KB     1.29
(ben_api2$median[3] - ben_api2$median[1]) / 1e6
#> [1] 32.2µs
(ben_api2$median[2] - ben_api2$median[1]) / 1e6
#> [1] 82.9ns

C benchmarks

Baseline function:

SEXP test_baseline() {
  int i;
  int res = 0;
  for (i = 0; i < 2000000000; i++) {
    res += i % 2;
  }
  return ScalarInteger(res);
}

Switch + modulo check:

SEXP test_modulo(SEXP progress) {
  int i;
  int res = 0;
  int progress_ = LOGICAL(progress)[0];
  for (i = 0; i < 2000000000; i++) {
    if (i % 10000 == 0 && progress_) cli_progress_set(R_NilValue, i);
    res += i % 2;
  }
  return ScalarInteger(res);
}

cli progress bar API:

SEXP test_cli() {
  int i;
  int res = 0;
  SEXP bar = PROTECT(cli_progress_bar(2000000000, NULL));
  for (i = 0; i < 2000000000; i++) {
    if (CLI_SHOULD_TICK) cli_progress_set(bar, i);
    res += i % 2;
  }
  cli_progress_done(bar);
  UNPROTECT(1);
  return ScalarInteger(res);
}

SEXP test_cli_unroll() {
  int i = 0;
  int res = 0;
  SEXP bar = PROTECT(cli_progress_bar(2000000000, NULL));
  int s, final, step = 2000000000 / 100000;
  for (s = 0; s < 100000; s++) {
    if (CLI_SHOULD_TICK) cli_progress_set(bar, i);
    final = (s + 1) * step;
    for (i = s * step; i < final; i++) {
      res += i % 2;
    }
  }
  cli_progress_done(bar);
  UNPROTECT(1);
  return ScalarInteger(res);
}

library(progresstest)
ben_c <- bench::mark(
  test_baseline(),
  test_modulo(),
  test_cli(),
  test_cli_unroll()
)
ben_c
#> # A tibble: 4 × 6
#>   expression             min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr>        <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 test_baseline()         1s       1s     0.995    2.08KB        0
#> 2 test_modulo()        2.05s    2.05s     0.488    2.24KB        0
#> 3 test_cli()           1.61s    1.61s     0.622   23.69KB        0
#> 4 test_cli_unroll()    1.02s    1.02s     0.981    3.35KB        0
(ben_c$median[3] - ben_c$median[1]) / 2000000000
#> [1] 1ns

Display update

We only update the display a fixed number of times per second. (Currently maximum five times per second.)

Let’s measure how long a single update takes.

Iterator with a bar

cli_progress_bar(total = 100000)
bench::mark(cli_progress_update(force = TRUE), max_iterations = 10000)
#> ■                                  0% | ETA:  8m
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#> # A tibble: 1 × 6
#>   expression                            min median itr/s…¹ mem_a…² gc/se…³
#>   <bch:expr>                        <bch:t> <bch:>   <dbl> <bch:b>   <dbl>
#> 1 cli_progress_update(force = TRUE)  8.89ms 9.04ms    108.  1.28MB    2.04
#> # … with abbreviated variable names ¹`itr/sec`, ²mem_alloc, ³`gc/sec`
cli_progress_done()

Iterator without a bar

cli_progress_bar(total = NA)
bench::mark(cli_progress_update(force = TRUE), max_iterations = 10000)
#> ⠙ 1 done (270/s) | 4ms
#> ⠹ 2 done (44/s) | 46ms
#> ⠸ 3 done (53/s) | 57ms
#> ⠼ 4 done (60/s) | 68ms
#> ⠴ 5 done (64/s) | 79ms
#> ⠦ 6 done (68/s) | 89ms
#> ⠧ 7 done (70/s) | 100ms
#> ⠇ 8 done (73/s) | 111ms
#> ⠏ 9 done (74/s) | 122ms
#> ⠋ 10 done (76/s) | 132ms
#> ⠙ 11 done (77/s) | 143ms
#> ⠹ 12 done (79/s) | 153ms
#> ⠸ 13 done (80/s) | 164ms
#> ⠼ 14 done (80/s) | 175ms
#> ⠴ 15 done (81/s) | 185ms
#> ⠦ 16 done (82/s) | 196ms
#> ⠧ 17 done (82/s) | 207ms
#> ⠇ 18 done (83/s) | 218ms
#> ⠏ 19 done (83/s) | 229ms
#> ⠋ 20 done (84/s) | 240ms
#> ⠙ 21 done (84/s) | 251ms
#> ⠹ 22 done (84/s) | 261ms
#> ⠸ 23 done (85/s) | 273ms
#> ⠼ 24 done (85/s) | 283ms
#> ⠴ 25 done (85/s) | 295ms
#> ⠦ 26 done (85/s) | 306ms
#> ⠧ 27 done (85/s) | 317ms
#> ⠇ 28 done (86/s) | 328ms
#> ⠏ 29 done (86/s) | 339ms
#> ⠋ 30 done (86/s) | 350ms
#> ⠙ 31 done (86/s) | 361ms
#> ⠹ 32 done (86/s) | 372ms
#> ⠸ 33 done (86/s) | 383ms
#> ⠼ 34 done (86/s) | 394ms
#> ⠴ 35 done (86/s) | 405ms
#> ⠦ 36 done (87/s) | 417ms
#> ⠧ 37 done (87/s) | 428ms
#> ⠇ 38 done (87/s) | 439ms
#> ⠏ 39 done (87/s) | 450ms
#> ⠋ 40 done (87/s) | 461ms
#> ⠙ 41 done (87/s) | 472ms
#> ⠹ 42 done (87/s) | 483ms
#> ⠸ 43 done (87/s) | 494ms
#> ⠼ 44 done (87/s) | 505ms
#> ⠴ 45 done (87/s) | 516ms
#> ⠦ 46 done (87/s) | 527ms
#> ⠧ 47 done (87/s) | 538ms
#> # A tibble: 1 × 6
#>   expression                            min median itr/s…¹ mem_a…² gc/se…³
#>   <bch:expr>                        <bch:t> <bch:>   <dbl> <bch:b>   <dbl>
#> 1 cli_progress_update(force = TRUE)  10.5ms 10.9ms    91.7   202KB       0
#> # … with abbreviated variable names ¹`itr/sec`, ²mem_alloc, ³`gc/sec`
cli_progress_done()

Gábor Csárdi

2023-01-09

Introduction

R benchmarks

The timer

`cli_progress_along()`

`for` loop

Mapping with `lapply()`

Mapping with purrr

`ticking()`

Traditional API

C benchmarks

Display update

Iterator with a bar

Iterator without a bar

cli progress bar benchmark

Gábor Csárdi

2023-01-09

Introduction

R benchmarks

The timer

cli_progress_along()

for loop

Mapping with lapply()

Mapping with purrr

ticking()

Traditional API

C benchmarks

Display update

Iterator with a bar

Iterator without a bar

`cli_progress_along()`

`for` loop

Mapping with `lapply()`

`ticking()`