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         8.96ns     20ns 50465381.        0B        0
#> 2 fun()                  140.05ns    160ns  4527363.        0B        0
#> 3 .Call(ccli_tick_reset) 110.01ns    130ns  7146685.        0B        0
#> 4 interactive()            8.96ns     20ns 56501873.        0B        0

ben_st2 <- bench::mark(
  if (`__cli_update_due`) foobar()
)
ben_st2
#> # A tibble: 1 × 6
#>   expression                       min median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr>                     <bch> <bch:>     <dbl> <bch:byt>    <dbl>
#> 1 if (`__cli_update_due`) fooba…  40ns 50.1ns 19111554.        0B        0

`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]]      140ns    151ns  6029526.        0B        0
#> 2 ta[[1]]       160ns    180ns  5297671.        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()         11.9ms   11.9ms      81.7    25.1KB     871.
#> 2 fp()         13.5ms   13.6ms      73.4    83.1KB    1064.

(ben_taf$median[2] - ben_taf$median[1]) / 1e5
#> [1] 17.5ns

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)     134ms    142ms      6.53        0B     60.4
#> 2 fp(1e+06)     147ms    148ms      6.75    1.82KB     60.7

(ben_taf2$median[2] - ben_taf2$median[1]) / 1e6
#> [1] 6.66ns

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)     1.34s    1.34s     0.745        0B     39.5
#> 2 fp(1e+07)     1.47s    1.47s     0.680    1.82KB     36.7

(ben_taf3$median[2] - ben_taf3$median[1]) / 1e7
#> [1] 12.8ns

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)     13.8s    13.8s    0.0726        0B     44.2
#> 2 fp(1e+08)     14.8s    14.8s    0.0675    1.82KB     39.2

(ben_taf4$median[2] - ben_taf4$median[1]) / 1e8
#> [1] 10.3ns

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()         73.9ms   80.9ms     12.0      781KB     13.7
#> 2 f01()       103.6ms  106.2ms      8.18     781KB     16.4
#> 3 fp()        123.1ms  132.1ms      7.63     783KB     11.4

(ben_tam$median[3] - ben_tam$median[1]) / 1e5
#> [1] 512ns

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)  814.12ms 814.12ms     1.23     7.63MB     8.60
#> 2 f01(1e+06)    1.23s    1.23s     0.813    7.63MB     6.50
#> 3 fp(1e+06)     1.86s    1.86s     0.539    7.63MB     4.31

(ben_tam2$median[3] - ben_tam2$median[1]) / 1e6
#> [1] 1.04µs

(ben_tam2$median[3] - ben_tam2$median[2]) / 1e6
#> [1] 626ns

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())
#> Warning: Some expressions had a GC in every iteration; so filtering is
#> disabled.

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()         67.4ms   74.6ms     13.1      884KB     9.33
#> 2 f01()       100.6ms  120.8ms      8.06     781KB     8.06
#> 3 fp()        118.1ms  124.9ms      7.99     783KB     6.00

(ben_pur$median[3] - ben_pur$median[1]) / 1e5
#> [1] 503ns

(ben_pur$median[3] - ben_pur$median[2]) / 1e5
#> [1] 41.7ns

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)  756.16ms 756.16ms     1.32     7.63MB     2.64
#> 2 f01(1e+06) 964.76ms 964.76ms     1.04     7.63MB     2.07
#> 3 fp(1e+06)     1.11s    1.11s     0.898    7.63MB     2.69

(ben_pur2$median[3] - ben_pur2$median[1]) / 1e6
#> [1] 357ns

(ben_pur2$median[3] - ben_pur2$median[2]) / 1e6
#> [1] 149ns

`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()        12.74ms  12.86ms    75.6      40.2KB     3.98
#> 2 fp()          3.88s    3.88s     0.258   100.6KB     2.06

(ben_tk$median[2] - ben_tk$median[1]) / 1e5
#> [1] 38.7µs

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()         11.7ms  11.86ms    68.0      19.9KB     5.66
#> 2 ff()        22.56ms  23.05ms    40.0      28.5KB     2.00
#> 3 fp()          2.19s    2.19s     0.458    25.9KB     1.83

(ben_api$median[3] - ben_api$median[1]) / 1e5
#> [1] 21.7µs

(ben_api$median[2] - ben_api$median[1]) / 1e5
#> [1] 112ns

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)   133.4ms  140.8ms    7.08          0B     5.31
#> 2 ff(1e+06)   244.9ms    250ms    4.00      1.82KB     2.00
#> 3 fp(1e+06)     22.5s    22.5s    0.0444    1.82KB     1.82

(ben_api2$median[3] - ben_api2$median[1]) / 1e6
#> [1] 22.4µs

(ben_api2$median[2] - ben_api2$median[1]) / 1e6
#> [1] 109ns

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()   619.54ms 619.54ms     1.61     2.08KB        0
#> 2 test_modulo()        1.24s    1.24s     0.806    2.23KB        0
#> 3 test_cli()           1.24s    1.24s     0.807   23.91KB        0
#> 4 test_cli_unroll() 620.25ms 620.25ms     1.61     3.59KB        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)
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#> # A tibble: 1 × 6
#>   expression                       min median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr>                    <bch:> <bch:>     <dbl> <bch:byt>    <dbl>
#> 1 cli_progress_update(force = … 5.61ms 5.67ms      173.    1.34MB        0

cli_progress_done()

Iterator without a bar

cli_progress_bar(total = NA)
bench::mark(cli_progress_update(force = TRUE), max_iterations = 10000)
#> ⠙ 1 done (429/s) | 3ms
#> ⠹ 2 done (74/s) | 28ms
#> ⠸ 3 done (89/s) | 34ms
#> ⠼ 4 done (99/s) | 41ms
#> ⠴ 5 done (107/s) | 47ms
#> ⠦ 6 done (112/s) | 54ms
#> ⠧ 7 done (116/s) | 61ms
#> ⠇ 8 done (120/s) | 67ms
#> ⠏ 9 done (123/s) | 74ms
#> ⠋ 10 done (125/s) | 81ms
#> ⠙ 11 done (127/s) | 87ms
#> ⠹ 12 done (129/s) | 94ms
#> ⠸ 13 done (130/s) | 100ms
#> ⠼ 14 done (132/s) | 107ms
#> ⠴ 15 done (133/s) | 114ms
#> ⠦ 16 done (134/s) | 120ms
#> ⠧ 17 done (135/s) | 127ms
#> ⠇ 18 done (136/s) | 133ms
#> ⠏ 19 done (136/s) | 140ms
#> ⠋ 20 done (137/s) | 147ms
#> ⠙ 21 done (137/s) | 153ms
#> ⠹ 22 done (138/s) | 160ms
#> ⠸ 23 done (138/s) | 167ms
#> ⠼ 24 done (139/s) | 173ms
#> ⠴ 25 done (139/s) | 180ms
#> ⠦ 26 done (140/s) | 187ms
#> ⠧ 27 done (140/s) | 193ms
#> ⠇ 28 done (140/s) | 200ms
#> ⠏ 29 done (141/s) | 207ms
#> ⠋ 30 done (141/s) | 213ms
#> ⠙ 31 done (141/s) | 220ms
#> ⠹ 32 done (142/s) | 227ms
#> ⠸ 33 done (142/s) | 233ms
#> ⠼ 34 done (142/s) | 240ms
#> ⠴ 35 done (142/s) | 246ms
#> ⠦ 36 done (143/s) | 253ms
#> ⠧ 37 done (143/s) | 260ms
#> ⠇ 38 done (143/s) | 267ms
#> ⠏ 39 done (143/s) | 273ms
#> ⠋ 40 done (143/s) | 280ms
#> ⠙ 41 done (143/s) | 287ms
#> ⠹ 42 done (144/s) | 293ms
#> ⠸ 43 done (144/s) | 300ms
#> ⠼ 44 done (144/s) | 307ms
#> ⠴ 45 done (144/s) | 313ms
#> ⠦ 46 done (144/s) | 320ms
#> ⠧ 47 done (144/s) | 326ms
#> ⠇ 48 done (144/s) | 333ms
#> ⠏ 49 done (144/s) | 340ms
#> ⠋ 50 done (145/s) | 346ms
#> ⠙ 51 done (145/s) | 353ms
#> ⠹ 52 done (145/s) | 360ms
#> ⠸ 53 done (145/s) | 366ms
#> ⠼ 54 done (145/s) | 373ms
#> ⠴ 55 done (145/s) | 380ms
#> ⠦ 56 done (145/s) | 386ms
#> ⠧ 57 done (145/s) | 393ms
#> ⠇ 58 done (145/s) | 399ms
#> ⠏ 59 done (146/s) | 406ms
#> ⠋ 60 done (146/s) | 413ms
#> ⠙ 61 done (146/s) | 419ms
#> ⠹ 62 done (146/s) | 426ms
#> ⠸ 63 done (146/s) | 433ms
#> ⠼ 64 done (146/s) | 439ms
#> ⠴ 65 done (146/s) | 446ms
#> ⠦ 66 done (146/s) | 453ms
#> ⠧ 67 done (146/s) | 459ms
#> ⠇ 68 done (146/s) | 466ms
#> ⠏ 69 done (146/s) | 473ms
#> ⠋ 70 done (146/s) | 479ms
#> ⠙ 71 done (146/s) | 486ms
#> ⠹ 72 done (146/s) | 493ms
#> ⠸ 73 done (146/s) | 499ms
#> ⠼ 74 done (146/s) | 506ms
#> ⠴ 75 done (146/s) | 513ms
#> ⠦ 76 done (146/s) | 519ms
#> ⠧ 77 done (147/s) | 526ms
#> # A tibble: 1 × 6
#>   expression                       min median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr>                    <bch:> <bch:>     <dbl> <bch:byt>    <dbl>
#> 1 cli_progress_update(force = … 6.51ms 6.62ms      151.     198KB        0

cli_progress_done()

Gábor Csárdi

2024-06-23

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

2024-06-23

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()`