colRowRanges - HenrikBengtsson/matrixStats GitHub Wiki

matrixStats: Benchmark report

colRanges() and rowRanges() benchmarks

This report benchmark the performance of colRanges() and rowRanges() against alternative methods.

Alternative methods

  • apply() + range()

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 3185955 170.2    5709258 305.0  5709258 305.0
Vcells 6413357  49.0   22343563 170.5 56666022 432.4
> colStats <- microbenchmark(colRanges = colRanges(X, na.rm = FALSE), `apply+range` = apply(X, MARGIN = 2L, 
+     FUN = range, na.rm = FALSE), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3184275 170.1    5709258 305.0  5709258 305.0
Vcells 6408445  48.9   22343563 170.5 56666022 432.4
> rowStats <- microbenchmark(rowRanges = rowRanges(X, na.rm = FALSE), `apply+range` = apply(X, MARGIN = 1L, 
+     FUN = range, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colRanges() and apply+range() 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 colRanges 0.001188 0.0013595 0.0017808 0.0015495 0.002058 0.009749
2 apply+range 0.036074 0.0369740 0.0384891 0.0374310 0.037765 0.112069
expr min lq mean median uq max
1 colRanges 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000
2 apply+range 30.36532 27.19676 21.61363 24.15682 18.35034 11.49544

Table: Benchmarking of rowRanges() and apply+range() 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 rowRanges 0.001189 0.0014305 0.0019098 0.001948 0.0020940 0.011684
2 apply+range 0.036328 0.0372705 0.0391010 0.037737 0.0383145 0.145866
expr min lq mean median uq max
1 rowRanges 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000
2 apply+range 30.55341 26.05418 20.47367 19.37218 18.29728 12.48425

Figure: Benchmarking of colRanges() and apply+range() on integer+10x10 data as well as rowRanges() and apply+range() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colRanges() and rowRanges() 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 colRanges 1.188 1.3595 1.78078 1.5495 2.058 9.749
2 rowRanges 1.189 1.4305 1.90982 1.9480 2.094 11.684
expr min lq mean median uq max
1 colRanges 1.000000 1.000000 1.000000 1.00000 1.000000 1.000000
2 rowRanges 1.000842 1.052225 1.072463 1.25718 1.017493 1.198482

Figure: Benchmarking of colRanges() and rowRanges() 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 3182828  170    5709258 305.0  5709258 305.0
Vcells 6024946   46   22343563 170.5 56666022 432.4
> colStats <- microbenchmark(colRanges = colRanges(X, na.rm = FALSE), `apply+range` = apply(X, MARGIN = 2L, 
+     FUN = range, na.rm = FALSE), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3182822 170.0    5709258 305.0  5709258 305.0
Vcells 6029989  46.1   22343563 170.5 56666022 432.4
> rowStats <- microbenchmark(rowRanges = rowRanges(X, na.rm = FALSE), `apply+range` = apply(X, MARGIN = 1L, 
+     FUN = range, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colRanges() and apply+range() 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 colRanges 0.019493 0.0204015 0.0211931 0.021145 0.0216250 0.034272
2 apply+range 0.286671 0.2895970 0.2955994 0.291265 0.2943575 0.418665
expr min lq mean median uq max
1 colRanges 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000
2 apply+range 14.70636 14.19489 13.94793 13.77465 13.61191 12.21595

Table: Benchmarking of rowRanges() and apply+range() 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 rowRanges 0.019008 0.0200585 0.0208961 0.0207775 0.021445 0.032492
2 apply+range 0.289331 0.2915570 0.2983378 0.2932490 0.296419 0.470623
expr min lq mean median uq max
1 rowRanges 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000
2 apply+range 15.22154 14.53533 14.27718 14.11378 13.82229 14.48427

Figure: Benchmarking of colRanges() and apply+range() on integer+100x100 data as well as rowRanges() and apply+range() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colRanges() and rowRanges() 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 rowRanges 19.008 20.0585 20.89613 20.7775 21.445 32.492
1 colRanges 19.493 20.4015 21.19306 21.1450 21.625 34.272
expr min lq mean median uq max
2 rowRanges 1.000000 1.0000 1.00000 1.000000 1.000000 1.000000
1 colRanges 1.025516 1.0171 1.01421 1.017687 1.008394 1.054783

Figure: Benchmarking of colRanges() and rowRanges() 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 3183561 170.1    5709258 305.0  5709258 305.0
Vcells 6028466  46.0   22343563 170.5 56666022 432.4
> colStats <- microbenchmark(colRanges = colRanges(X, na.rm = FALSE), `apply+range` = apply(X, MARGIN = 2L, 
+     FUN = range, na.rm = FALSE), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3183552 170.1    5709258 305.0  5709258 305.0
Vcells 6033504  46.1   22343563 170.5 56666022 432.4
> rowStats <- microbenchmark(rowRanges = rowRanges(X, na.rm = FALSE), `apply+range` = apply(X, MARGIN = 1L, 
+     FUN = range, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colRanges() and apply+range() 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 colRanges 0.014935 0.0152270 0.0157857 0.0157040 0.0158965 0.030208
2 apply+range 0.109673 0.1113615 0.1147352 0.1141055 0.1150540 0.192582
expr min lq mean median uq max
1 colRanges 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
2 apply+range 7.343355 7.313423 7.268316 7.266015 7.237694 6.375199

Table: Benchmarking of rowRanges() and apply+range() 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 rowRanges 0.016718 0.0171375 0.0177167 0.017611 0.017921 0.031567
2 apply+range 0.109371 0.1112560 0.1151728 0.114329 0.115622 0.192081
expr min lq mean median uq max
1 rowRanges 1.00000 1.000000 1.000000 1.000000 1.00000 1.000000
2 apply+range 6.54211 6.491962 6.500791 6.491909 6.45176 6.084867

Figure: Benchmarking of colRanges() and apply+range() on integer+1000x10 data as well as rowRanges() and apply+range() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colRanges() and rowRanges() 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 colRanges 14.935 15.2270 15.78566 15.704 15.8965 30.208
2 rowRanges 16.718 17.1375 17.71674 17.611 17.9210 31.567
expr min lq mean median uq max
1 colRanges 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
2 rowRanges 1.119384 1.125468 1.122331 1.121434 1.127355 1.044988

Figure: Benchmarking of colRanges() and rowRanges() 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 3183746 170.1    5709258 305.0  5709258 305.0
Vcells 6029138  46.0   22343563 170.5 56666022 432.4
> colStats <- microbenchmark(colRanges = colRanges(X, na.rm = FALSE), `apply+range` = apply(X, MARGIN = 2L, 
+     FUN = range, na.rm = FALSE), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3183740 170.1    5709258 305.0  5709258 305.0
Vcells 6034181  46.1   22343563 170.5 56666022 432.4
> rowStats <- microbenchmark(rowRanges = rowRanges(X, na.rm = FALSE), `apply+range` = apply(X, MARGIN = 1L, 
+     FUN = range, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colRanges() and apply+range() 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 colRanges 0.043947 0.045743 0.0474114 0.0472785 0.048195 0.061508
2 apply+range 1.967796 2.010960 2.1402157 2.0420700 2.140857 7.481601
expr min lq mean median uq max
1 colRanges 1.00000 1.00000 1.00000 1.00000 1.00000 1.0000
2 apply+range 44.77657 43.96214 45.14138 43.19236 44.42073 121.6362

Table: Benchmarking of rowRanges() and apply+range() 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 rowRanges 0.041714 0.0435925 0.0460086 0.0449345 0.046621 0.106088
2 apply+range 1.946157 1.9968740 2.1388868 2.0386435 2.138240 7.513914
expr min lq mean median uq max
1 rowRanges 1.00000 1.00000 1.00000 1.00000 1.0000 1.00000
2 apply+range 46.65477 45.80774 46.48884 45.36923 45.8643 70.82718

Figure: Benchmarking of colRanges() and apply+range() on integer+10x1000 data as well as rowRanges() and apply+range() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colRanges() and rowRanges() 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
2 rowRanges 41.714 43.5925 46.00861 44.9345 46.621 106.088
1 colRanges 43.947 45.7430 47.41139 47.2785 48.195 61.508
expr min lq mean median uq max
2 rowRanges 1.000000 1.000000 1.00000 1.000000 1.000000 1.0000000
1 colRanges 1.053531 1.049332 1.03049 1.052165 1.033762 0.5797828

Figure: Benchmarking of colRanges() and rowRanges() 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 3183930 170.1    5709258 305.0  5709258 305.0
Vcells 6029621  46.1   22343563 170.5 56666022 432.4
> colStats <- microbenchmark(colRanges = colRanges(X, na.rm = FALSE), `apply+range` = apply(X, MARGIN = 2L, 
+     FUN = range, na.rm = FALSE), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3183924 170.1    5709258 305.0  5709258 305.0
Vcells 6079664  46.4   22343563 170.5 56666022 432.4
> rowStats <- microbenchmark(rowRanges = rowRanges(X, na.rm = FALSE), `apply+range` = apply(X, MARGIN = 1L, 
+     FUN = range, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colRanges() and apply+range() 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 colRanges 0.195850 0.1981605 0.2072418 0.2039755 0.2098685 0.258956
2 apply+range 2.683579 2.7374205 2.9877983 2.8024365 2.8899875 17.625025
expr min lq mean median uq max
1 colRanges 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000
2 apply+range 13.70222 13.81416 14.41697 13.73908 13.77047 68.06185

Table: Benchmarking of rowRanges() and apply+range() 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 rowRanges 0.190614 0.194564 0.2014488 0.201166 0.203994 0.249796
2 apply+range 2.687045 2.764858 3.0294722 2.848991 2.915337 18.072235
expr min lq mean median uq max
1 rowRanges 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000
2 apply+range 14.09679 14.21053 15.03842 14.16239 14.29129 72.34798

Figure: Benchmarking of colRanges() and apply+range() on integer+100x1000 data as well as rowRanges() and apply+range() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colRanges() and rowRanges() 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
2 rowRanges 190.614 194.5640 201.4488 201.1660 203.9940 249.796
1 colRanges 195.850 198.1605 207.2418 203.9755 209.8685 258.956
expr min lq mean median uq max
2 rowRanges 1.000000 1.000000 1.000000 1.000000 1.000000 1.00000
1 colRanges 1.027469 1.018485 1.028757 1.013966 1.028797 1.03667

Figure: Benchmarking of colRanges() and rowRanges() 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 3184122 170.1    5709258 305.0  5709258 305.0
Vcells 6030183  46.1   22343563 170.5 56666022 432.4
> colStats <- microbenchmark(colRanges = colRanges(X, na.rm = FALSE), `apply+range` = apply(X, MARGIN = 2L, 
+     FUN = range, na.rm = FALSE), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3184116 170.1    5709258 305.0  5709258 305.0
Vcells 6080226  46.4   22343563 170.5 56666022 432.4
> rowStats <- microbenchmark(rowRanges = rowRanges(X, na.rm = FALSE), `apply+range` = apply(X, MARGIN = 1L, 
+     FUN = range, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colRanges() and apply+range() 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 colRanges 0.141058 0.1422235 0.1504843 0.1427965 0.1485420 0.192611
2 apply+range 0.902127 0.9093035 1.0440243 0.9193610 0.9761505 9.368515
expr min lq mean median uq max
1 colRanges 1.000000 1.000000 1.000000 1.00000 1.000000 1.00000
2 apply+range 6.395433 6.393483 6.937761 6.43826 6.571545 48.63956

Table: Benchmarking of rowRanges() and apply+range() 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 rowRanges 0.149136 0.150664 0.1591479 0.153410 0.156948 0.203205
2 apply+range 0.909309 0.942989 1.0766903 0.957465 0.996298 9.315277
expr min lq mean median uq max
1 rowRanges 1.00000 1.000000 1.000000 1.000000 1.00000 1.00000
2 apply+range 6.09718 6.258887 6.765345 6.241216 6.34795 45.84177

Figure: Benchmarking of colRanges() and apply+range() on integer+1000x100 data as well as rowRanges() and apply+range() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colRanges() and rowRanges() 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 colRanges 141.058 142.2235 150.4843 142.7965 148.542 192.611
2 rowRanges 149.136 150.6640 159.1479 153.4100 156.948 203.205
expr min lq mean median uq max
1 colRanges 1.000000 1.000000 1.000000 1.000000 1.00000 1.000000
2 rowRanges 1.057267 1.059347 1.057571 1.074326 1.05659 1.055002

Figure: Benchmarking of colRanges() and rowRanges() 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 3184330 170.1    5709258 305.0  5709258 305.0
Vcells 6146532  46.9   22343563 170.5 56666022 432.4
> colStats <- microbenchmark(colRanges = colRanges(X, na.rm = FALSE), `apply+range` = apply(X, MARGIN = 2L, 
+     FUN = range, na.rm = FALSE), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3184315 170.1    5709258 305.0  5709258 305.0
Vcells 6146660  46.9   22343563 170.5 56666022 432.4
> rowStats <- microbenchmark(rowRanges = rowRanges(X, na.rm = FALSE), `apply+range` = apply(X, MARGIN = 1L, 
+     FUN = range, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colRanges() and apply+range() 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 colRanges 0.001233 0.0015645 0.002038 0.0019235 0.0022495 0.010014
2 apply+range 0.036716 0.0382100 0.039597 0.0386610 0.0392060 0.117575
expr min lq mean median uq max
1 colRanges 1.00000 1.00000 1.00000 1.0000 1.00000 1.00000
2 apply+range 29.77778 24.42314 19.42906 20.0993 17.42876 11.74106

Table: Benchmarking of rowRanges() and apply+range() 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
1 rowRanges 0.001226 0.001625 0.0021325 0.0021420 0.002341 0.010143
2 apply+range 0.036255 0.037761 0.0391419 0.0381995 0.038659 0.111980
expr min lq mean median uq max
1 rowRanges 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000
2 apply+range 29.57178 23.23754 18.35502 17.83357 16.51388 11.04013

Figure: Benchmarking of colRanges() and apply+range() on double+10x10 data as well as rowRanges() and apply+range() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colRanges() and rowRanges() 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
1 colRanges 1.233 1.5645 2.03803 1.9235 2.2495 10.014
2 rowRanges 1.226 1.6250 2.13249 2.1420 2.3410 10.143
expr min lq mean median uq max
1 colRanges 1.0000000 1.000000 1.000000 1.000000 1.000000 1.000000
2 rowRanges 0.9943228 1.038671 1.046349 1.113595 1.040676 1.012882

Figure: Benchmarking of colRanges() and rowRanges() 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 3184510 170.1    5709258 305.0  5709258 305.0
Vcells 6146644  46.9   22343563 170.5 56666022 432.4
> colStats <- microbenchmark(colRanges = colRanges(X, na.rm = FALSE), `apply+range` = apply(X, MARGIN = 2L, 
+     FUN = range, na.rm = FALSE), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3184504 170.1    5709258 305.0  5709258 305.0
Vcells 6156687  47.0   22343563 170.5 56666022 432.4
> rowStats <- microbenchmark(rowRanges = rowRanges(X, na.rm = FALSE), `apply+range` = apply(X, MARGIN = 1L, 
+     FUN = range, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colRanges() and apply+range() 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 colRanges 0.016974 0.0177975 0.0190318 0.018428 0.0191705 0.062640
2 apply+range 0.324943 0.3326065 0.3440279 0.337524 0.3444030 0.504698
expr min lq mean median uq max
1 colRanges 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000
2 apply+range 19.14357 18.68838 18.07643 18.31582 17.96526 8.05712

Table: Benchmarking of rowRanges() and apply+range() 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 rowRanges 0.021905 0.0226085 0.023331 0.0231800 0.0236245 0.032950
2 apply+range 0.286434 0.2896050 0.296664 0.2925475 0.2960620 0.437018
expr min lq mean median uq max
1 rowRanges 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000
2 apply+range 13.07619 12.80956 12.71547 12.62069 12.53199 13.26307

Figure: Benchmarking of colRanges() and apply+range() on double+100x100 data as well as rowRanges() and apply+range() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colRanges() and rowRanges() 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
1 colRanges 16.974 17.7975 19.03185 18.428 19.1705 62.64
2 rowRanges 21.905 22.6085 23.33095 23.180 23.6245 32.95
expr min lq mean median uq max
1 colRanges 1.000000 1.000000 1.00000 1.000000 1.000000 1.0000000
2 rowRanges 1.290503 1.270319 1.22589 1.257869 1.232336 0.5260217

Figure: Benchmarking of colRanges() and rowRanges() 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 3184703 170.1    5709258 305.0  5709258 305.0
Vcells 6147533  47.0   22343563 170.5 56666022 432.4
> colStats <- microbenchmark(colRanges = colRanges(X, na.rm = FALSE), `apply+range` = apply(X, MARGIN = 2L, 
+     FUN = range, na.rm = FALSE), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3184694 170.1    5709258 305.0  5709258 305.0
Vcells 6157571  47.0   22343563 170.5 56666022 432.4
> rowStats <- microbenchmark(rowRanges = rowRanges(X, na.rm = FALSE), `apply+range` = apply(X, MARGIN = 1L, 
+     FUN = range, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colRanges() and apply+range() 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 colRanges 0.012952 0.0133565 0.0143408 0.0138830 0.0143820 0.038035
2 apply+range 0.161635 0.1642280 0.1687233 0.1665045 0.1690595 0.262972
expr min lq mean median uq max
1 colRanges 1.00000 1.00000 1.00000 1.00000 1.00000 1.000000
2 apply+range 12.47954 12.29574 11.76522 11.99341 11.75494 6.913948

Table: Benchmarking of rowRanges() and apply+range() 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 rowRanges 0.019130 0.0195665 0.0201674 0.0201305 0.0204175 0.035807
2 apply+range 0.120754 0.1231620 0.1267325 0.1247645 0.1275775 0.209332
expr min lq mean median uq max
1 rowRanges 1.000000 1.000000 1.000000 1.000000 1.000000 1.00000
2 apply+range 6.312284 6.294534 6.284044 6.197785 6.248439 5.84612

Figure: Benchmarking of colRanges() and apply+range() on double+1000x10 data as well as rowRanges() and apply+range() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colRanges() and rowRanges() 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 colRanges 12.952 13.3565 14.34085 13.8830 14.3820 38.035
2 rowRanges 19.130 19.5665 20.16735 20.1305 20.4175 35.807
expr min lq mean median uq max
1 colRanges 1.000000 1.000000 1.000000 1.000000 1.000000 1.0000000
2 rowRanges 1.476992 1.464942 1.406287 1.450011 1.419657 0.9414224

Figure: Benchmarking of colRanges() and rowRanges() 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 3184888 170.1    5709258 305.0  5709258 305.0
Vcells 6148557  47.0   22343563 170.5 56666022 432.4
> colStats <- microbenchmark(colRanges = colRanges(X, na.rm = FALSE), `apply+range` = apply(X, MARGIN = 2L, 
+     FUN = range, na.rm = FALSE), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3184882 170.1    5709258 305.0  5709258 305.0
Vcells 6158600  47.0   22343563 170.5 56666022 432.4
> rowStats <- microbenchmark(rowRanges = rowRanges(X, na.rm = FALSE), `apply+range` = apply(X, MARGIN = 1L, 
+     FUN = range, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colRanges() and apply+range() 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 colRanges 0.043253 0.0467135 0.0487173 0.048314 0.0497255 0.068454
2 apply+range 1.879370 1.9707360 2.1020929 2.035511 2.1187615 7.015015
expr min lq mean median uq max
1 colRanges 1.00000 1.00000 1.00000 1.00000 1.00000 1.0000
2 apply+range 43.45063 42.18772 43.14884 42.13086 42.60915 102.4778

Table: Benchmarking of rowRanges() and apply+range() 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 rowRanges 0.042235 0.0449375 0.0477133 0.04675 0.04879 0.067657
2 apply+range 1.898994 1.9515945 2.1202086 2.02335 2.11813 7.952888
expr min lq mean median uq max
1 rowRanges 1.00000 1.00000 1.00000 1.0000 1.0000 1.0000
2 apply+range 44.96257 43.42908 44.43641 43.2802 43.4132 117.5472

Figure: Benchmarking of colRanges() and apply+range() on double+10x1000 data as well as rowRanges() and apply+range() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colRanges() and rowRanges() 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
2 rowRanges 42.235 44.9375 47.71332 46.750 48.7900 67.657
1 colRanges 43.253 46.7135 48.71725 48.314 49.7255 68.454
expr min lq mean median uq max
2 rowRanges 1.000000 1.000000 1.000000 1.000000 1.000000 1.00000
1 colRanges 1.024103 1.039522 1.021041 1.033454 1.019174 1.01178

Figure: Benchmarking of colRanges() and rowRanges() 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 3185072 170.2    5709258 305.0  5709258 305.0
Vcells 6148678  47.0   22343563 170.5 56666022 432.4
> colStats <- microbenchmark(colRanges = colRanges(X, na.rm = FALSE), `apply+range` = apply(X, MARGIN = 2L, 
+     FUN = range, na.rm = FALSE), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3185066 170.2    5709258 305.0  5709258 305.0
Vcells 6248721  47.7   22343563 170.5 56666022 432.4
> rowStats <- microbenchmark(rowRanges = rowRanges(X, na.rm = FALSE), `apply+range` = apply(X, MARGIN = 1L, 
+     FUN = range, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colRanges() and apply+range() 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 colRanges 0.179437 0.189588 0.201289 0.1946985 0.201708 0.279644
2 apply+range 3.073336 3.240317 3.730899 3.3119565 3.515820 19.493263
expr min lq mean median uq max
1 colRanges 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000
2 apply+range 17.12766 17.09136 18.53503 17.01069 17.43025 69.70742

Table: Benchmarking of rowRanges() and apply+range() 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 rowRanges 0.215885 0.225069 0.238594 0.2286225 0.241789 0.322489
2 apply+range 2.653263 2.766923 3.225074 2.8195575 2.957310 18.589922
expr min lq mean median uq max
1 rowRanges 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000
2 apply+range 12.29017 12.29367 13.51699 12.33281 12.23095 57.64514

Figure: Benchmarking of colRanges() and apply+range() on double+100x1000 data as well as rowRanges() and apply+range() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colRanges() and rowRanges() 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 colRanges 179.437 189.588 201.289 194.6985 201.708 279.644
2 rowRanges 215.885 225.069 238.594 228.6225 241.789 322.489
expr min lq mean median uq max
1 colRanges 1.000000 1.000000 1.00000 1.000000 1.000000 1.000000
2 rowRanges 1.203124 1.187148 1.18533 1.174239 1.198708 1.153213

Figure: Benchmarking of colRanges() and rowRanges() 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 3185267 170.2    5709258 305.0  5709258 305.0
Vcells 6149891  47.0   22343563 170.5 56666022 432.4
> colStats <- microbenchmark(colRanges = colRanges(X, na.rm = FALSE), `apply+range` = apply(X, MARGIN = 2L, 
+     FUN = range, na.rm = FALSE), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3185258 170.2    5709258 305.0  5709258 305.0
Vcells 6249929  47.7   22343563 170.5 56666022 432.4
> rowStats <- microbenchmark(rowRanges = rowRanges(X, na.rm = FALSE), `apply+range` = apply(X, MARGIN = 1L, 
+     FUN = range, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colRanges() and apply+range() 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 colRanges 0.120533 0.122163 0.1291061 0.123614 0.1341775 0.164952
2 apply+range 0.981820 0.994169 1.1872157 1.017043 1.1041030 7.618334
expr min lq mean median uq max
1 colRanges 1.000000 1.000000 1.000000 1.000000 1.000000 1.00000
2 apply+range 8.145653 8.138053 9.195659 8.227575 8.228675 46.18516

Table: Benchmarking of rowRanges() and apply+range() 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 rowRanges 0.174045 0.175108 0.1864741 0.176347 0.193471 0.311685
2 apply+range 1.011355 1.024305 1.2291105 1.053208 1.153560 7.734844
expr min lq mean median uq max
1 rowRanges 1.000000 1.000000 1.00000 1.000000 1.000000 1.00000
2 apply+range 5.810882 5.849561 6.59132 5.972359 5.962441 24.81622

Figure: Benchmarking of colRanges() and apply+range() on double+1000x100 data as well as rowRanges() and apply+range() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colRanges() and rowRanges() 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 colRanges 120.533 122.163 129.1061 123.614 134.1775 164.952
2 rowRanges 174.045 175.108 186.4741 176.347 193.4710 311.685
expr min lq mean median uq max
1 colRanges 1.000000 1.000000 1.000000 1.000000 1.000000 1.00000
2 rowRanges 1.443961 1.433396 1.444348 1.426594 1.441903 1.88955

Figure: Benchmarking of colRanges() and rowRanges() 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 25.15 secs.

Reproducibility

To reproduce this report, do:

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

Copyright Henrik Bengtsson. Last updated on 2019-09-10 20:51:30 (-0700 UTC). Powered by RSP.

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