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Henrik Bengtsson edited this page Sep 11, 2019 · 6 revisions

matrixStats: Benchmark report

colOrderStats() and rowOrderStats() benchmarks

This report benchmark the performance of colOrderStats() and rowOrderStats() against alternative methods.

Alternative methods

  • apply() + quantile(..., type = 3L)
  • Biobase::rowQ()

Data type "integer"

Data

> rmatrix <- function(nrow, ncol, mode = c("logical", "double", "integer", "index"), range = c(-100, 
+     +100), na_prob = 0) {
+     mode <- match.arg(mode)
+     n <- nrow * ncol
+     if (mode == "logical") {
+         x <- sample(c(FALSE, TRUE), size = n, replace = TRUE)
+     }     else if (mode == "index") {
+         x <- seq_len(n)
+         mode <- "integer"
+     }     else {
+         x <- runif(n, min = range[1], max = range[2])
+     }
+     storage.mode(x) <- mode
+     if (na_prob > 0) 
+         x[sample(n, size = na_prob * n)] <- NA
+     dim(x) <- c(nrow, ncol)
+     x
+ }
> rmatrices <- function(scale = 10, seed = 1, ...) {
+     set.seed(seed)
+     data <- list()
+     data[[1]] <- rmatrix(nrow = scale * 1, ncol = scale * 1, ...)
+     data[[2]] <- rmatrix(nrow = scale * 10, ncol = scale * 10, ...)
+     data[[3]] <- rmatrix(nrow = scale * 100, ncol = scale * 1, ...)
+     data[[4]] <- t(data[[3]])
+     data[[5]] <- rmatrix(nrow = scale * 10, ncol = scale * 100, ...)
+     data[[6]] <- t(data[[5]])
+     names(data) <- sapply(data, FUN = function(x) paste(dim(x), collapse = "x"))
+     data
+ }
> data <- rmatrices(mode = mode)

Results

10x10 integer matrix

> X <- data[["10x10"]]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3164817 169.1    5709258 305.0  5709258 305.0
Vcells 6247177  47.7   22267496 169.9 56666022 432.4
> probs <- 0.3
> which <- round(probs * nrow(X))
> colStats <- microbenchmark(colOrderStats = colOrderStats(X, which = which, na.rm = FALSE), `apply+quantile` = apply(X, 
+     MARGIN = 2L, FUN = quantile, probs = probs, na.rm = FALSE, type = 3L), `rowQ(t(X))` = rowQ(t(X), 
+     which = which), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3162930 169.0    5709258 305.0  5709258 305.0
Vcells 6241821  47.7   22267496 169.9 56666022 432.4
> rowStats <- microbenchmark(rowOrderStats = rowOrderStats(X, which = which, na.rm = FALSE), `apply+quantile` = apply(X, 
+     MARGIN = 1L, FUN = quantile, probs = probs, na.rm = FALSE, type = 3L), rowQ = rowQ(X, which = which), 
+     unit = "ms")

Table: Benchmarking of colOrderStats(), apply+quantile() and rowQ(t(X))() on integer+10x10 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 colOrderStats 0.001932 0.0031710 0.0049054 0.005677 0.0060760 0.018188
3 rowQ(t(X)) 0.014249 0.0182860 0.0231091 0.022621 0.0257695 0.108739
2 apply+quantile 0.700427 0.7188305 0.7642608 0.733271 0.7637415 1.015273
expr min lq mean median uq max
1 colOrderStats 1.000000 1.000000 1.00000 1.000000 1.000000 1.000000
3 rowQ(t(X)) 7.375259 5.766635 4.71092 3.984675 4.241195 5.978612
2 apply+quantile 362.539855 226.688899 155.79895 129.165228 125.698074 55.821036

Table: Benchmarking of rowOrderStats(), apply+quantile() and rowQ() on integer+10x10 data (transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 rowOrderStats 0.002043 0.0030860 0.0048254 0.005679 0.0060765 0.017788
3 rowQ 0.009882 0.0117680 0.0165022 0.014045 0.0196095 0.084045
2 apply+quantile 0.704532 0.7224165 0.7367775 0.727187 0.7370685 1.119893
expr min lq mean median uq max
1 rowOrderStats 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
3 rowQ 4.837004 3.813351 3.419832 2.473147 3.227104 4.724814
2 apply+quantile 344.851689 234.094783 152.685762 128.048424 121.298198 62.957780

Figure: Benchmarking of colOrderStats(), apply+quantile() and rowQ(t(X))() on integer+10x10 data as well as rowOrderStats(), apply+quantile() and rowQ() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colOrderStats() and rowOrderStats() on integer+10x10 data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 colOrderStats 1.932 3.171 4.90543 5.677 6.0760 18.188
2 rowOrderStats 2.043 3.086 4.82545 5.679 6.0765 17.788
expr min lq mean median uq max
1 colOrderStats 1.000000 1.0000000 1.0000000 1.000000 1.000000 1.0000000
2 rowOrderStats 1.057453 0.9731946 0.9836956 1.000352 1.000082 0.9780075

Figure: Benchmarking of colOrderStats() and rowOrderStats() on integer+10x10 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

100x100 integer matrix

> X <- data[["100x100"]]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3161481 168.9    5709258 305.0  5709258 305.0
Vcells 5858388  44.7   22267496 169.9 56666022 432.4
> probs <- 0.3
> which <- round(probs * nrow(X))
> colStats <- microbenchmark(colOrderStats = colOrderStats(X, which = which, na.rm = FALSE), `apply+quantile` = apply(X, 
+     MARGIN = 2L, FUN = quantile, probs = probs, na.rm = FALSE, type = 3L), `rowQ(t(X))` = rowQ(t(X), 
+     which = which), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3161471 168.9    5709258 305.0  5709258 305.0
Vcells 5863489  44.8   22267496 169.9 56666022 432.4
> rowStats <- microbenchmark(rowOrderStats = rowOrderStats(X, which = which, na.rm = FALSE), `apply+quantile` = apply(X, 
+     MARGIN = 1L, FUN = quantile, probs = probs, na.rm = FALSE, type = 3L), rowQ = rowQ(X, which = which), 
+     unit = "ms")

Table: Benchmarking of colOrderStats(), apply+quantile() and rowQ(t(X))() on integer+100x100 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 colOrderStats 0.100325 0.105361 0.1146209 0.1111180 0.116927 0.261677
3 rowQ(t(X)) 0.257913 0.265549 0.2889740 0.2801135 0.298563 0.722735
2 apply+quantile 7.159544 7.281613 7.9360339 7.4042465 7.797231 24.140317
expr min lq mean median uq max
1 colOrderStats 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
3 rowQ(t(X)) 2.570775 2.520373 2.521128 2.520865 2.553414 2.761935
2 apply+quantile 71.363509 69.111090 69.237208 66.634087 66.684602 92.252345

Table: Benchmarking of rowOrderStats(), apply+quantile() and rowQ() on integer+100x100 data (transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 rowOrderStats 0.102724 0.1060750 0.1124859 0.1105265 0.1167675 0.143676
3 rowQ 0.241839 0.2493755 0.2636848 0.2596140 0.2692875 0.325968
2 apply+quantile 7.175032 7.3049795 7.8897440 7.4196035 7.8356985 15.390523
expr min lq mean median uq max
1 rowOrderStats 1.00000 1.000000 1.000000 1.000000 1.000000 1.000000
3 rowQ 2.35426 2.350936 2.344158 2.348885 2.306185 2.268771
2 apply+quantile 69.84767 68.866175 70.139824 67.129634 67.105132 107.119651

Figure: Benchmarking of colOrderStats(), apply+quantile() and rowQ(t(X))() on integer+100x100 data as well as rowOrderStats(), apply+quantile() and rowQ() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colOrderStats() and rowOrderStats() on integer+100x100 data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
2 rowOrderStats 102.724 106.075 112.4859 110.5265 116.7675 143.676
1 colOrderStats 100.325 105.361 114.6209 111.1180 116.9270 261.677
expr min lq mean median uq max
2 rowOrderStats 1.0000000 1.0000000 1.00000 1.000000 1.000000 1.000000
1 colOrderStats 0.9766462 0.9932689 1.01898 1.005352 1.001366 1.821299

Figure: Benchmarking of colOrderStats() and rowOrderStats() on integer+100x100 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

1000x10 integer matrix

> X <- data[["1000x10"]]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3162237 168.9    5709258 305.0  5709258 305.0
Vcells 5862171  44.8   22267496 169.9 56666022 432.4
> probs <- 0.3
> which <- round(probs * nrow(X))
> colStats <- microbenchmark(colOrderStats = colOrderStats(X, which = which, na.rm = FALSE), `apply+quantile` = apply(X, 
+     MARGIN = 2L, FUN = quantile, probs = probs, na.rm = FALSE, type = 3L), `rowQ(t(X))` = rowQ(t(X), 
+     which = which), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3162227 168.9    5709258 305.0  5709258 305.0
Vcells 5867272  44.8   22267496 169.9 56666022 432.4
> rowStats <- microbenchmark(rowOrderStats = rowOrderStats(X, which = which, na.rm = FALSE), `apply+quantile` = apply(X, 
+     MARGIN = 1L, FUN = quantile, probs = probs, na.rm = FALSE, type = 3L), rowQ = rowQ(X, which = which), 
+     unit = "ms")

Table: Benchmarking of colOrderStats(), apply+quantile() and rowQ(t(X))() on integer+1000x10 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 colOrderStats 0.088044 0.0918545 0.0941119 0.0944045 0.0953010 0.126456
3 rowQ(t(X)) 0.242794 0.2527940 0.2567904 0.2566280 0.2595115 0.291105
2 apply+quantile 1.002920 1.0377160 1.0578490 1.0530305 1.0669350 1.365025
expr min lq mean median uq max
1 colOrderStats 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
3 rowQ(t(X)) 2.757644 2.752113 2.728564 2.718387 2.723072 2.302026
2 apply+quantile 11.391123 11.297389 11.240330 11.154452 11.195423 10.794466

Table: Benchmarking of rowOrderStats(), apply+quantile() and rowQ() on integer+1000x10 data (transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 rowOrderStats 0.092744 0.0960315 0.0982252 0.0982475 0.0992300 0.137773
3 rowQ 0.228148 0.2358790 0.2401534 0.2402835 0.2433455 0.294290
2 apply+quantile 1.002051 1.0393100 1.0524716 1.0524280 1.0648230 1.234466
expr min lq mean median uq max
1 rowOrderStats 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
3 rowQ 2.459976 2.456267 2.444928 2.445696 2.452338 2.136050
2 apply+quantile 10.804483 10.822595 10.714887 10.712008 10.730858 8.960145

Figure: Benchmarking of colOrderStats(), apply+quantile() and rowQ(t(X))() on integer+1000x10 data as well as rowOrderStats(), apply+quantile() and rowQ() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colOrderStats() and rowOrderStats() on integer+1000x10 data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 colOrderStats 88.044 91.8545 94.11192 94.4045 95.301 126.456
2 rowOrderStats 92.744 96.0315 98.22517 98.2475 99.230 137.773
expr min lq mean median uq max
1 colOrderStats 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
2 rowOrderStats 1.053382 1.045474 1.043706 1.040708 1.041227 1.089494

Figure: Benchmarking of colOrderStats() and rowOrderStats() on integer+1000x10 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

10x1000 integer matrix

> X <- data[["10x1000"]]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3162442 168.9    5709258 305.0  5709258 305.0
Vcells 5862968  44.8   22267496 169.9 56666022 432.4
> probs <- 0.3
> which <- round(probs * nrow(X))
> colStats <- microbenchmark(colOrderStats = colOrderStats(X, which = which, na.rm = FALSE), `apply+quantile` = apply(X, 
+     MARGIN = 2L, FUN = quantile, probs = probs, na.rm = FALSE, type = 3L), `rowQ(t(X))` = rowQ(t(X), 
+     which = which), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3162432 168.9    5709258 305.0  5709258 305.0
Vcells 5868069  44.8   22267496 169.9 56666022 432.4
> rowStats <- microbenchmark(rowOrderStats = rowOrderStats(X, which = which, na.rm = FALSE), `apply+quantile` = apply(X, 
+     MARGIN = 1L, FUN = quantile, probs = probs, na.rm = FALSE, type = 3L), rowQ = rowQ(X, which = which), 
+     unit = "ms")

Table: Benchmarking of colOrderStats(), apply+quantile() and rowQ(t(X))() on integer+10x1000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 colOrderStats 0.096422 0.102217 0.1151726 0.1168315 0.1203185 0.188983
3 rowQ(t(X)) 0.258851 0.269841 0.3028486 0.3116975 0.3231130 0.446715
2 apply+quantile 68.249799 69.420339 76.4880245 71.0519800 79.5367195 336.500526
expr min lq mean median uq max
1 colOrderStats 1.000000 1.000000 1.00000 1.000000 1.000000 1.000000
3 rowQ(t(X)) 2.684564 2.639884 2.62952 2.667924 2.685481 2.363784
2 apply+quantile 707.823930 679.146708 664.11662 608.157731 661.051455 1780.586222

Table: Benchmarking of rowOrderStats(), apply+quantile() and rowQ() on integer+10x1000 data (transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 rowOrderStats 0.097423 0.1014835 0.1138816 0.118211 0.122242 0.142445
3 rowQ 0.243022 0.2486300 0.2737372 0.276842 0.286358 0.602589
2 apply+quantile 68.140618 69.2765190 75.0524350 70.932610 81.330608 98.748079
expr min lq mean median uq max
1 rowOrderStats 1.000000 1.000000 1.0000 1.000000 1.00000 1.000000
3 rowQ 2.494503 2.449955 2.4037 2.341931 2.34255 4.230327
2 apply+quantile 699.430504 682.638252 659.0389 600.050841 665.32459 693.236540

Figure: Benchmarking of colOrderStats(), apply+quantile() and rowQ(t(X))() on integer+10x1000 data as well as rowOrderStats(), apply+quantile() and rowQ() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colOrderStats() and rowOrderStats() on integer+10x1000 data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 colOrderStats 96.422 102.2170 115.1726 116.8315 120.3185 188.983
2 rowOrderStats 97.423 101.4835 113.8816 118.2110 122.2420 142.445
expr min lq mean median uq max
1 colOrderStats 1.000000 1.0000000 1.0000000 1.000000 1.000000 1.000000
2 rowOrderStats 1.010381 0.9928241 0.9887913 1.011808 1.015987 0.753745

Figure: Benchmarking of colOrderStats() and rowOrderStats() on integer+10x1000 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

100x1000 integer matrix

> X <- data[["100x1000"]]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3162655 169.0    5709258 305.0  5709258 305.0
Vcells 5863543  44.8   22267496 169.9 56666022 432.4
> probs <- 0.3
> which <- round(probs * nrow(X))
> colStats <- microbenchmark(colOrderStats = colOrderStats(X, which = which, na.rm = FALSE), `apply+quantile` = apply(X, 
+     MARGIN = 2L, FUN = quantile, probs = probs, na.rm = FALSE, type = 3L), `rowQ(t(X))` = rowQ(t(X), 
+     which = which), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3162645 169.0    5709258 305.0  5709258 305.0
Vcells 5913644  45.2   22267496 169.9 56666022 432.4
> rowStats <- microbenchmark(rowOrderStats = rowOrderStats(X, which = which, na.rm = FALSE), `apply+quantile` = apply(X, 
+     MARGIN = 1L, FUN = quantile, probs = probs, na.rm = FALSE, type = 3L), rowQ = rowQ(X, which = which), 
+     unit = "ms")

Table: Benchmarking of colOrderStats(), apply+quantile() and rowQ(t(X))() on integer+100x1000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 colOrderStats 0.982719 0.992317 1.045162 1.006800 1.030469 1.506634
3 rowQ(t(X)) 2.397756 2.492323 2.623891 2.556417 2.612141 4.120215
2 apply+quantile 71.642484 73.291158 79.121191 74.524256 86.640305 105.794064
expr min lq mean median uq max
1 colOrderStats 1.00000 1.00000 1.00000 1.000000 1.000000 1.000000
3 rowQ(t(X)) 2.43992 2.51162 2.51051 2.539151 2.534903 2.734715
2 apply+quantile 72.90231 73.85861 75.70230 74.020914 84.078476 70.218822

Table: Benchmarking of rowOrderStats(), apply+quantile() and rowQ() on integer+100x1000 data (transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 rowOrderStats 1.011975 1.017653 1.064200 1.036333 1.059772 1.490149
3 rowQ 2.284843 2.362310 2.437772 2.405922 2.468891 2.934251
2 apply+quantile 70.957223 72.591067 78.896157 74.202615 86.877360 110.068004
expr min lq mean median uq max
1 rowOrderStats 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
3 rowQ 2.257806 2.321332 2.290708 2.321572 2.329643 1.969099
2 apply+quantile 70.117565 71.331846 74.136581 71.601131 81.977407 73.863757

Figure: Benchmarking of colOrderStats(), apply+quantile() and rowQ(t(X))() on integer+100x1000 data as well as rowOrderStats(), apply+quantile() and rowQ() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colOrderStats() and rowOrderStats() on integer+100x1000 data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 colOrderStats 982.719 992.317 1045.162 1006.800 1030.469 1506.634
2 rowOrderStats 1011.975 1017.653 1064.200 1036.333 1059.772 1490.149
expr min lq mean median uq max
1 colOrderStats 1.00000 1.000000 1.000000 1.000000 1.000000 1.0000000
2 rowOrderStats 1.02977 1.025532 1.018215 1.029333 1.028436 0.9890584

Figure: Benchmarking of colOrderStats() and rowOrderStats() on integer+100x1000 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

1000x100 integer matrix

> X <- data[["1000x100"]]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3162867 169.0    5709258 305.0  5709258 305.0
Vcells 5864231  44.8   22267496 169.9 56666022 432.4
> probs <- 0.3
> which <- round(probs * nrow(X))
> colStats <- microbenchmark(colOrderStats = colOrderStats(X, which = which, na.rm = FALSE), `apply+quantile` = apply(X, 
+     MARGIN = 2L, FUN = quantile, probs = probs, na.rm = FALSE, type = 3L), `rowQ(t(X))` = rowQ(t(X), 
+     which = which), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3162857 169.0    5709258 305.0  5709258 305.0
Vcells 5914332  45.2   22343563 170.5 56666022 432.4
> rowStats <- microbenchmark(rowOrderStats = rowOrderStats(X, which = which, na.rm = FALSE), `apply+quantile` = apply(X, 
+     MARGIN = 1L, FUN = quantile, probs = probs, na.rm = FALSE, type = 3L), rowQ = rowQ(X, which = which), 
+     unit = "ms")

Table: Benchmarking of colOrderStats(), apply+quantile() and rowQ(t(X))() on integer+1000x100 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 colOrderStats 0.908698 0.918584 0.9711281 0.9252755 0.940986 1.500015
3 rowQ(t(X)) 2.348175 2.451135 5.0738679 2.5224085 2.658630 246.989893
2 apply+quantile 9.547893 9.749097 10.3127077 9.8414515 10.013106 21.748900
expr min lq mean median uq max
1 colOrderStats 1.000000 1.000000 1.000000 1.000000 1.000000 1.00000
3 rowQ(t(X)) 2.584109 2.668384 5.224715 2.726116 2.825366 164.65828
2 apply+quantile 10.507224 10.613179 10.619307 10.636239 10.641079 14.49912

Table: Benchmarking of rowOrderStats(), apply+quantile() and rowQ() on integer+1000x100 data (transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 rowOrderStats 0.954094 0.9646065 1.019438 0.972211 0.992552 1.417984
3 rowQ 2.224715 2.3062425 2.513451 2.337960 2.369690 10.432955
2 apply+quantile 9.573497 9.7311070 10.058221 9.840734 9.990557 17.754467
expr min lq mean median uq max
1 rowOrderStats 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
3 rowQ 2.331757 2.390864 2.465525 2.404787 2.387472 7.357597
2 apply+quantile 10.034123 10.088162 9.866433 10.122015 10.065525 12.520922

Figure: Benchmarking of colOrderStats(), apply+quantile() and rowQ(t(X))() on integer+1000x100 data as well as rowOrderStats(), apply+quantile() and rowQ() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colOrderStats() and rowOrderStats() on integer+1000x100 data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 colOrderStats 908.698 918.5840 971.1281 925.2755 940.986 1500.015
2 rowOrderStats 954.094 964.6065 1019.4385 972.2110 992.552 1417.984
expr min lq mean median uq max
1 colOrderStats 1.000000 1.000000 1.000000 1.000000 1.0000 1.0000000
2 rowOrderStats 1.049957 1.050102 1.049747 1.050726 1.0548 0.9453132

Figure: Benchmarking of colOrderStats() and rowOrderStats() on integer+1000x100 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

Data type "double"

Data

> rmatrix <- function(nrow, ncol, mode = c("logical", "double", "integer", "index"), range = c(-100, 
+     +100), na_prob = 0) {
+     mode <- match.arg(mode)
+     n <- nrow * ncol
+     if (mode == "logical") {
+         x <- sample(c(FALSE, TRUE), size = n, replace = TRUE)
+     }     else if (mode == "index") {
+         x <- seq_len(n)
+         mode <- "integer"
+     }     else {
+         x <- runif(n, min = range[1], max = range[2])
+     }
+     storage.mode(x) <- mode
+     if (na_prob > 0) 
+         x[sample(n, size = na_prob * n)] <- NA
+     dim(x) <- c(nrow, ncol)
+     x
+ }
> rmatrices <- function(scale = 10, seed = 1, ...) {
+     set.seed(seed)
+     data <- list()
+     data[[1]] <- rmatrix(nrow = scale * 1, ncol = scale * 1, ...)
+     data[[2]] <- rmatrix(nrow = scale * 10, ncol = scale * 10, ...)
+     data[[3]] <- rmatrix(nrow = scale * 100, ncol = scale * 1, ...)
+     data[[4]] <- t(data[[3]])
+     data[[5]] <- rmatrix(nrow = scale * 10, ncol = scale * 100, ...)
+     data[[6]] <- t(data[[5]])
+     names(data) <- sapply(data, FUN = function(x) paste(dim(x), collapse = "x"))
+     data
+ }
> data <- rmatrices(mode = mode)

Results

10x10 double matrix

> X <- data[["10x10"]]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3163081 169.0    5709258 305.0  5709258 305.0
Vcells 5980119  45.7   22343563 170.5 56666022 432.4
> probs <- 0.3
> which <- round(probs * nrow(X))
> colStats <- microbenchmark(colOrderStats = colOrderStats(X, which = which, na.rm = FALSE), `apply+quantile` = apply(X, 
+     MARGIN = 2L, FUN = quantile, probs = probs, na.rm = FALSE, type = 3L), `rowQ(t(X))` = rowQ(t(X), 
+     which = which), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3163059 169.0    5709258 305.0  5709258 305.0
Vcells 5980300  45.7   22343563 170.5 56666022 432.4
> rowStats <- microbenchmark(rowOrderStats = rowOrderStats(X, which = which, na.rm = FALSE), `apply+quantile` = apply(X, 
+     MARGIN = 1L, FUN = quantile, probs = probs, na.rm = FALSE, type = 3L), rowQ = rowQ(X, which = which), 
+     unit = "ms")

Table: Benchmarking of colOrderStats(), apply+quantile() and rowQ(t(X))() on double+10x10 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 colOrderStats 0.002342 0.0034530 0.0050972 0.0057735 0.0065100 0.017683
3 rowQ(t(X)) 0.007403 0.0093375 0.0130076 0.0112980 0.0155150 0.079125
2 apply+quantile 0.700793 0.7078400 0.7190105 0.7146930 0.7235135 0.981725
expr min lq mean median uq max
1 colOrderStats 1.000000 1.00000 1.000000 1.000000 1.000000 1.000000
3 rowQ(t(X)) 3.160973 2.70417 2.551887 1.956872 2.383256 4.474637
2 apply+quantile 299.228437 204.99276 141.058783 123.788517 111.138786 55.518012

Table: Benchmarking of rowOrderStats(), apply+quantile() and rowQ() on double+10x10 data (transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
3 rowQ 0.003801 0.0045715 0.0070744 0.0056585 0.0087095 0.054254
1 rowOrderStats 0.002495 0.0034090 0.0050627 0.0056910 0.0063535 0.019401
2 apply+quantile 0.698146 0.7050025 0.7144014 0.7106970 0.7175685 0.968225
expr min lq mean median uq max
3 rowQ 1.0000000 1.0000000 1.0000000 1.000000 1.0000000 1.0000000
1 rowOrderStats 0.6564062 0.7457071 0.7156417 1.005744 0.7294908 0.3575958
2 apply+quantile 183.6742962 154.2168872 100.9847378 125.598127 82.3891727 17.8461496

Figure: Benchmarking of colOrderStats(), apply+quantile() and rowQ(t(X))() on double+10x10 data as well as rowOrderStats(), apply+quantile() and rowQ() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colOrderStats() and rowOrderStats() on double+10x10 data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
2 rowOrderStats 2.495 3.409 5.06270 5.6910 6.3535 19.401
1 colOrderStats 2.342 3.453 5.09724 5.7735 6.5100 17.683
expr min lq mean median uq max
2 rowOrderStats 1.0000000 1.000000 1.000000 1.000000 1.000000 1.0000000
1 colOrderStats 0.9386774 1.012907 1.006822 1.014497 1.024632 0.9114479

Figure: Benchmarking of colOrderStats() and rowOrderStats() on double+10x10 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

100x100 double matrix

> X <- data[["100x100"]]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3163274 169.0    5709258 305.0  5709258 305.0
Vcells 5981105  45.7   22343563 170.5 56666022 432.4
> probs <- 0.3
> which <- round(probs * nrow(X))
> colStats <- microbenchmark(colOrderStats = colOrderStats(X, which = which, na.rm = FALSE), `apply+quantile` = apply(X, 
+     MARGIN = 2L, FUN = quantile, probs = probs, na.rm = FALSE, type = 3L), `rowQ(t(X))` = rowQ(t(X), 
+     which = which), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3163264 169.0    5709258 305.0  5709258 305.0
Vcells 5991206  45.8   22343563 170.5 56666022 432.4
> rowStats <- microbenchmark(rowOrderStats = rowOrderStats(X, which = which, na.rm = FALSE), `apply+quantile` = apply(X, 
+     MARGIN = 1L, FUN = quantile, probs = probs, na.rm = FALSE, type = 3L), rowQ = rowQ(X, which = which), 
+     unit = "ms")

Table: Benchmarking of colOrderStats(), apply+quantile() and rowQ(t(X))() on double+100x100 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 colOrderStats 0.151852 0.1541930 0.1640386 0.160511 0.1676685 0.211924
3 rowQ(t(X)) 0.233200 0.2365015 0.2516724 0.245102 0.2590480 0.339546
2 apply+quantile 7.209046 7.3151465 7.8090190 7.423569 7.6967770 16.133390
expr min lq mean median uq max
1 colOrderStats 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
3 rowQ(t(X)) 1.535706 1.533802 1.534228 1.527011 1.545001 1.602207
2 apply+quantile 47.474159 47.441495 47.604780 46.249597 45.904729 76.128187

Table: Benchmarking of rowOrderStats(), apply+quantile() and rowQ() on double+100x100 data (transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 rowOrderStats 0.153110 0.1548275 0.1636779 0.158818 0.1681450 0.211434
3 rowQ 0.177858 0.1796600 0.1923398 0.188050 0.1972395 0.270269
2 apply+quantile 7.216398 7.2831930 7.9174679 7.430089 7.8410540 16.829715
expr min lq mean median uq max
1 rowOrderStats 1.000000 1.000000 1.000000 1.00000 1.000000 1.000000
3 rowQ 1.161635 1.160388 1.175112 1.18406 1.173032 1.278266
2 apply+quantile 47.132114 47.040694 48.372253 46.78367 46.632692 79.597960

Figure: Benchmarking of colOrderStats(), apply+quantile() and rowQ(t(X))() on double+100x100 data as well as rowOrderStats(), apply+quantile() and rowQ() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colOrderStats() and rowOrderStats() on double+100x100 data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
2 rowOrderStats 153.110 154.8275 163.6779 158.818 168.1450 211.434
1 colOrderStats 151.852 154.1930 164.0385 160.511 167.6685 211.924
expr min lq mean median uq max
2 rowOrderStats 1.0000000 1.0000000 1.000000 1.00000 1.0000000 1.000000
1 colOrderStats 0.9917837 0.9959019 1.002204 1.01066 0.9971661 1.002317

Figure: Benchmarking of colOrderStats() and rowOrderStats() on double+100x100 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

1000x10 double matrix

> X <- data[["1000x10"]]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3163487 169.0    5709258 305.0  5709258 305.0
Vcells 5982188  45.7   22343563 170.5 56666022 432.4
> probs <- 0.3
> which <- round(probs * nrow(X))
> colStats <- microbenchmark(colOrderStats = colOrderStats(X, which = which, na.rm = FALSE), `apply+quantile` = apply(X, 
+     MARGIN = 2L, FUN = quantile, probs = probs, na.rm = FALSE, type = 3L), `rowQ(t(X))` = rowQ(t(X), 
+     which = which), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3163477 169.0    5709258 305.0  5709258 305.0
Vcells 5992289  45.8   22343563 170.5 56666022 432.4
> rowStats <- microbenchmark(rowOrderStats = rowOrderStats(X, which = which, na.rm = FALSE), `apply+quantile` = apply(X, 
+     MARGIN = 1L, FUN = quantile, probs = probs, na.rm = FALSE, type = 3L), rowQ = rowQ(X, which = which), 
+     unit = "ms")

Table: Benchmarking of colOrderStats(), apply+quantile() and rowQ(t(X))() on double+1000x10 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 colOrderStats 0.137570 0.1398110 0.1443352 0.1422425 0.1461885 0.202188
3 rowQ(t(X)) 0.217051 0.2223905 0.2358092 0.2251030 0.2293180 0.517141
2 apply+quantile 1.112462 1.1351870 1.1728520 1.1503720 1.1706425 1.893386
expr min lq mean median uq max
1 colOrderStats 1.000000 1.000000 1.000000 1.00000 1.000000 1.000000
3 rowQ(t(X)) 1.577749 1.590651 1.633761 1.58253 1.568646 2.557723
2 apply+quantile 8.086516 8.119440 8.125889 8.08740 8.007760 9.364483

Table: Benchmarking of rowOrderStats(), apply+quantile() and rowQ() on double+1000x10 data (transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 rowOrderStats 0.142481 0.1474750 0.1708429 0.1713020 0.1895015 0.237021
3 rowQ 0.164252 0.1699545 0.1962792 0.1974145 0.2177905 0.278304
2 apply+quantile 1.079233 1.0967530 1.2899622 1.1835395 1.4491930 1.880415
expr min lq mean median uq max
1 rowOrderStats 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
3 rowQ 1.152799 1.152429 1.148887 1.152435 1.149281 1.174174
2 apply+quantile 7.574575 7.436874 7.550574 6.909082 7.647396 7.933537

Figure: Benchmarking of colOrderStats(), apply+quantile() and rowQ(t(X))() on double+1000x10 data as well as rowOrderStats(), apply+quantile() and rowQ() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colOrderStats() and rowOrderStats() on double+1000x10 data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 colOrderStats 137.570 139.811 144.3352 142.2425 146.1885 202.188
2 rowOrderStats 142.481 147.475 170.8429 171.3020 189.5015 237.021
expr min lq mean median uq max
1 colOrderStats 1.000000 1.000000 1.000000 1.000000 1.000000 1.00000
2 rowOrderStats 1.035698 1.054817 1.183654 1.204295 1.296282 1.17228

Figure: Benchmarking of colOrderStats() and rowOrderStats() on double+1000x10 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

10x1000 double matrix

> X <- data[["10x1000"]]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3163692 169.0    5709258 305.0  5709258 305.0
Vcells 5982324  45.7   22343563 170.5 56666022 432.4
> probs <- 0.3
> which <- round(probs * nrow(X))
> colStats <- microbenchmark(colOrderStats = colOrderStats(X, which = which, na.rm = FALSE), `apply+quantile` = apply(X, 
+     MARGIN = 2L, FUN = quantile, probs = probs, na.rm = FALSE, type = 3L), `rowQ(t(X))` = rowQ(t(X), 
+     which = which), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3163682 169.0    5709258 305.0  5709258 305.0
Vcells 5992425  45.8   22343563 170.5 56666022 432.4
> rowStats <- microbenchmark(rowOrderStats = rowOrderStats(X, which = which, na.rm = FALSE), `apply+quantile` = apply(X, 
+     MARGIN = 1L, FUN = quantile, probs = probs, na.rm = FALSE, type = 3L), rowQ = rowQ(X, which = which), 
+     unit = "ms")

Table: Benchmarking of colOrderStats(), apply+quantile() and rowQ(t(X))() on double+10x1000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 colOrderStats 0.149321 0.1532270 0.1671298 0.163497 0.1740765 0.215217
3 rowQ(t(X)) 0.194589 0.1990095 0.2256560 0.231264 0.2459720 0.280529
2 apply+quantile 68.324530 69.5748805 75.6796908 70.979443 83.1125005 109.802889
expr min lq mean median uq max
1 colOrderStats 1.000000 1.000000 1.000000 1.000000 1.000000 1.00000
3 rowQ(t(X)) 1.303159 1.298789 1.350184 1.414485 1.413011 1.30347
2 apply+quantile 457.568125 454.064104 452.819903 434.132999 477.448136 510.19617

Table: Benchmarking of rowOrderStats(), apply+quantile() and rowQ() on double+10x1000 data (transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 rowOrderStats 0.149030 0.1664155 0.1850085 0.1819330 0.1961385 0.322571
3 rowQ 0.176522 0.1978705 0.2197706 0.2178195 0.2362470 0.319586
2 apply+quantile 68.027821 73.9741105 87.2590179 85.0304155 96.0258535 126.942856
expr min lq mean median uq max
1 rowOrderStats 1.000000 1.000000 1.000000 1.000000 1.000000 1.0000000
3 rowQ 1.184473 1.189015 1.187895 1.197251 1.204491 0.9907462
2 apply+quantile 456.470650 444.514546 471.648646 467.372140 489.581869 393.5346203

Figure: Benchmarking of colOrderStats(), apply+quantile() and rowQ(t(X))() on double+10x1000 data as well as rowOrderStats(), apply+quantile() and rowQ() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colOrderStats() and rowOrderStats() on double+10x1000 data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 colOrderStats 149.321 153.2270 167.1298 163.497 174.0765 215.217
2 rowOrderStats 149.030 166.4155 185.0085 181.933 196.1385 322.571
expr min lq mean median uq max
1 colOrderStats 1.0000000 1.000000 1.000000 1.000000 1.000000 1.000000
2 rowOrderStats 0.9980512 1.086072 1.106975 1.112761 1.126737 1.498817

Figure: Benchmarking of colOrderStats() and rowOrderStats() on double+10x1000 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

100x1000 double matrix

> X <- data[["100x1000"]]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3163905 169.0    5709258 305.0  5709258 305.0
Vcells 5983599  45.7   22343563 170.5 56666022 432.4
> probs <- 0.3
> which <- round(probs * nrow(X))
> colStats <- microbenchmark(colOrderStats = colOrderStats(X, which = which, na.rm = FALSE), `apply+quantile` = apply(X, 
+     MARGIN = 2L, FUN = quantile, probs = probs, na.rm = FALSE, type = 3L), `rowQ(t(X))` = rowQ(t(X), 
+     which = which), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3163895 169.0    5709258 305.0  5709258 305.0
Vcells 6083700  46.5   22343563 170.5 56666022 432.4
> rowStats <- microbenchmark(rowOrderStats = rowOrderStats(X, which = which, na.rm = FALSE), `apply+quantile` = apply(X, 
+     MARGIN = 1L, FUN = quantile, probs = probs, na.rm = FALSE, type = 3L), rowQ = rowQ(X, which = which), 
+     unit = "ms")

Table: Benchmarking of colOrderStats(), apply+quantile() and rowQ(t(X))() on double+100x1000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 colOrderStats 1.493198 1.502794 1.575718 1.541372 1.606196 1.890651
3 rowQ(t(X)) 2.311336 2.393073 2.504513 2.453865 2.578567 3.096269
2 apply+quantile 72.706505 74.228516 83.233698 76.064604 87.782736 346.141771
expr min lq mean median uq max
1 colOrderStats 1.00000 1.000000 1.000000 1.00000 1.000000 1.000000
3 rowQ(t(X)) 1.54791 1.592416 1.589442 1.59200 1.605387 1.637674
2 apply+quantile 48.69180 49.393673 52.822705 49.34863 54.652568 183.080733

Table: Benchmarking of rowOrderStats(), apply+quantile() and rowQ() on double+100x1000 data (transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 rowOrderStats 1.513896 1.526642 1.615790 1.551529 1.640509 2.137383
3 rowQ 1.743967 1.751475 1.861983 1.797514 1.950642 2.402143
2 apply+quantile 71.923080 73.557554 81.185077 75.607957 87.977137 111.668584
expr min lq mean median uq max
1 rowOrderStats 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
3 rowQ 1.151973 1.147273 1.152367 1.158544 1.189047 1.123871
2 apply+quantile 47.508600 48.182583 50.244826 48.731256 53.627951 52.245472

Figure: Benchmarking of colOrderStats(), apply+quantile() and rowQ(t(X))() on double+100x1000 data as well as rowOrderStats(), apply+quantile() and rowQ() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colOrderStats() and rowOrderStats() on double+100x1000 data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 colOrderStats 1.493198 1.502794 1.575718 1.541372 1.606196 1.890651
2 rowOrderStats 1.513896 1.526642 1.615790 1.551529 1.640509 2.137383
expr min lq mean median uq max
1 colOrderStats 1.000000 1.000000 1.000000 1.00000 1.000000 1.000000
2 rowOrderStats 1.013861 1.015869 1.025431 1.00659 1.021363 1.130501

Figure: Benchmarking of colOrderStats() and rowOrderStats() on double+100x1000 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

1000x100 double matrix

> X <- data[["1000x100"]]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3164117 169.0    5709258 305.0  5709258 305.0
Vcells 5983742  45.7   22343563 170.5 56666022 432.4
> probs <- 0.3
> which <- round(probs * nrow(X))
> colStats <- microbenchmark(colOrderStats = colOrderStats(X, which = which, na.rm = FALSE), `apply+quantile` = apply(X, 
+     MARGIN = 2L, FUN = quantile, probs = probs, na.rm = FALSE, type = 3L), `rowQ(t(X))` = rowQ(t(X), 
+     which = which), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3164107 169.0    5709258 305.0  5709258 305.0
Vcells 6083843  46.5   22343563 170.5 56666022 432.4
> rowStats <- microbenchmark(rowOrderStats = rowOrderStats(X, which = which, na.rm = FALSE), `apply+quantile` = apply(X, 
+     MARGIN = 1L, FUN = quantile, probs = probs, na.rm = FALSE, type = 3L), rowQ = rowQ(X, which = which), 
+     unit = "ms")

Table: Benchmarking of colOrderStats(), apply+quantile() and rowQ(t(X))() on double+1000x100 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 colOrderStats 1.493593 1.508093 1.619081 1.541671 1.624444 2.405428
3 rowQ(t(X)) 1.914679 1.997925 2.163334 2.077514 2.181479 3.283499
2 apply+quantile 10.443364 10.559361 11.512780 10.662634 11.467785 19.872912
expr min lq mean median uq max
1 colOrderStats 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
3 rowQ(t(X)) 1.281928 1.324803 1.336149 1.347573 1.342908 1.365037
2 apply+quantile 6.992108 7.001799 7.110686 6.916284 7.059514 8.261695

Table: Benchmarking of rowOrderStats(), apply+quantile() and rowQ() on double+1000x100 data (transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 rowOrderStats 1.548613 1.554814 1.612256 1.589789 1.624245 2.050989
3 rowQ 1.751948 1.762935 1.891934 1.787286 1.846548 9.923347
2 apply+quantile 10.460200 10.547797 10.970156 10.590273 10.709793 20.604903
expr min lq mean median uq max
1 rowOrderStats 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
3 rowQ 1.131301 1.133856 1.173470 1.124229 1.136866 4.838323
2 apply+quantile 6.754560 6.783963 6.804227 6.661435 6.593708 10.046326

Figure: Benchmarking of colOrderStats(), apply+quantile() and rowQ(t(X))() on double+1000x100 data as well as rowOrderStats(), apply+quantile() and rowQ() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colOrderStats() and rowOrderStats() on double+1000x100 data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 colOrderStats 1.493593 1.508093 1.619081 1.541671 1.624444 2.405428
2 rowOrderStats 1.548613 1.554814 1.612256 1.589789 1.624245 2.050989
expr min lq mean median uq max
1 colOrderStats 1.000000 1.00000 1.0000000 1.000000 1.0000000 1.0000000
2 rowOrderStats 1.036837 1.03098 0.9957845 1.031211 0.9998772 0.8526503

Figure: Benchmarking of colOrderStats() and rowOrderStats() on double+1000x100 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

Appendix

Session information

R version 3.6.1 Patched (2019-08-27 r77078)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.3 LTS

Matrix products: default
BLAS:   /home/hb/software/R-devel/R-3-6-branch/lib/R/lib/libRblas.so
LAPACK: /home/hb/software/R-devel/R-3-6-branch/lib/R/lib/libRlapack.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] microbenchmark_1.4-6    matrixStats_0.55.0-9000 ggplot2_3.2.1          
[4] knitr_1.24              R.devices_2.16.0        R.utils_2.9.0          
[7] R.oo_1.22.0             R.methodsS3_1.7.1       history_0.0.0-9002     

loaded via a namespace (and not attached):
 [1] Biobase_2.45.0       bit64_0.9-7          splines_3.6.1       
 [4] network_1.15         assertthat_0.2.1     highr_0.8           
 [7] stats4_3.6.1         blob_1.2.0           robustbase_0.93-5   
[10] pillar_1.4.2         RSQLite_2.1.2        backports_1.1.4     
[13] lattice_0.20-38      glue_1.3.1           digest_0.6.20       
[16] colorspace_1.4-1     sandwich_2.5-1       Matrix_1.2-17       
[19] XML_3.98-1.20        lpSolve_5.6.13.3     pkgconfig_2.0.2     
[22] genefilter_1.66.0    purrr_0.3.2          ergm_3.10.4         
[25] xtable_1.8-4         mvtnorm_1.0-11       scales_1.0.0        
[28] tibble_2.1.3         annotate_1.62.0      IRanges_2.18.2      
[31] TH.data_1.0-10       withr_2.1.2          BiocGenerics_0.30.0 
[34] lazyeval_0.2.2       mime_0.7             survival_2.44-1.1   
[37] magrittr_1.5         crayon_1.3.4         statnet.common_4.3.0
[40] memoise_1.1.0        laeken_0.5.0         R.cache_0.13.0      
[43] MASS_7.3-51.4        R.rsp_0.43.1         tools_3.6.1         
[46] multcomp_1.4-10      S4Vectors_0.22.1     trust_0.1-7         
[49] munsell_0.5.0        AnnotationDbi_1.46.1 compiler_3.6.1      
[52] rlang_0.4.0          grid_3.6.1           RCurl_1.95-4.12     
[55] cwhmisc_6.6          rappdirs_0.3.1       labeling_0.3        
[58] bitops_1.0-6         base64enc_0.1-3      boot_1.3-23         
[61] gtable_0.3.0         codetools_0.2-16     DBI_1.0.0           
[64] markdown_1.1         R6_2.4.0             zoo_1.8-6           
[67] dplyr_0.8.3          bit_1.1-14           zeallot_0.1.0       
[70] parallel_3.6.1       Rcpp_1.0.2           vctrs_0.2.0         
[73] DEoptimR_1.0-8       tidyselect_0.2.5     xfun_0.9            
[76] coda_0.19-3

Total processing time was 1.64 mins.

Reproducibility

To reproduce this report, do:

html <- matrixStats:::benchmark('colOrderStats')

Copyright Henrik Bengtsson. Last updated on 2019-09-10 20:48:22 (-0700 UTC). Powered by RSP.

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