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matrixStats: Benchmark report

colAlls() and rowAlls() benchmarks

This report benchmark the performance of colAlls() and rowAlls() against alternative methods.

Alternative methods

  • apply() + all()
  • colSums() == n or rowSums() == n

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 = "logical")

Results

10x10 matrix

> X <- data[["10x10"]]
> gc()
          used (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 1536119 82.1    2637877 140.9  2637877 140.9
Vcells 2374583 18.2    5986528  45.7 55745682 425.4
> colStats <- microbenchmark(colAlls = colAlls(X), `apply+all` = apply(X, MARGIN = 2L, FUN = all), 
+     `colSums==n` = (colSums(X) == nrow(X)), unit = "ms")
> X <- t(X)
> gc()
          used (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 1534953 82.0    2637877 140.9  2637877 140.9
Vcells 2372458 18.2    5986528  45.7 55745682 425.4
> rowStats <- microbenchmark(rowAlls = rowAlls(X), `apply+all` = apply(X, MARGIN = 1L, FUN = all), 
+     `rowSums==n` = (rowSums(X) == ncol(X)), unit = "ms")

Table: Benchmarking of colAlls(), apply+all() and colSums==n() on 10x10 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 colAlls 0.001720 0.0020315 0.0044374 0.0024620 0.0026955 0.205407
3 colSums==n 0.004736 0.0052460 0.0063776 0.0058445 0.0064355 0.054051
2 apply+all 0.023673 0.0246595 0.0262043 0.0252490 0.0260795 0.066520
expr min lq mean median uq max
1 colAlls 1.000000 1.000000 1.000000 1.000000 1.000000 1.0000000
3 colSums==n 2.753488 2.582328 1.437231 2.373883 2.387498 0.2631410
2 apply+all 13.763372 12.138568 5.905263 10.255483 9.675199 0.3238449
Table: Benchmarking of rowAlls(), apply+all() and rowSums==n() on 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 rowAlls 0.001773 0.0020335 0.0044965 0.0024075 0.0026205 0.214778
3 rowSums==n 0.004336 0.0048310 0.0057944 0.0052810 0.0058210 0.033994
2 apply+all 0.023206 0.0243505 0.0256536 0.0250425 0.0256620 0.062862
expr min lq mean median uq max
1 rowAlls 1.000000 1.000000 1.000000 1.000000 1.000000 1.0000000
3 rowSums==n 2.445572 2.375707 1.288661 2.193562 2.221332 0.1582751
2 apply+all 13.088551 11.974674 5.705258 10.401869 9.792788 0.2926836
Figure: Benchmarking of colAlls(), apply+all() and colSums==n() on 10x10 data as well as rowAlls(), apply+all() and rowSums==n() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colAlls() and rowAlls() on 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 rowAlls 1.773 2.0335 4.49648 2.4075 2.6205 214.778
1 colAlls 1.720 2.0315 4.43745 2.4620 2.6955 205.407
expr min lq mean median uq max
2 rowAlls 1.0000000 1.0000000 1.000000 1.000000 1.00000 1.0000000
1 colAlls 0.9701072 0.9990165 0.986872 1.022638 1.02862 0.9563689
Figure: Benchmarking of colAlls() and rowAlls() on 10x10 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

100x100 matrix

> X <- data[["100x100"]]
> gc()
          used (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 1534703 82.0    2637877 140.9  2637877 140.9
Vcells 2139126 16.4    5986528  45.7 55745682 425.4
> colStats <- microbenchmark(colAlls = colAlls(X), `apply+all` = apply(X, MARGIN = 2L, FUN = all), 
+     `colSums==n` = (colSums(X) == nrow(X)), unit = "ms")
> X <- t(X)
> gc()
          used (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 1534697 82.0    2637877 140.9  2637877 140.9
Vcells 2144169 16.4    5986528  45.7 55745682 425.4
> rowStats <- microbenchmark(rowAlls = rowAlls(X), `apply+all` = apply(X, MARGIN = 1L, FUN = all), 
+     `rowSums==n` = (rowSums(X) == ncol(X)), unit = "ms")

Table: Benchmarking of colAlls(), apply+all() and colSums==n() on 100x100 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 colAlls 0.002636 0.0031315 0.0037904 0.0037945 0.0041760 0.013224
3 colSums==n 0.015105 0.0163255 0.0174746 0.0171295 0.0178225 0.043113
2 apply+all 0.186309 0.1945085 0.2001144 0.1973185 0.2011820 0.285261
expr min lq mean median uq max
1 colAlls 1.000000 1.000000 1.000000 1.000000 1.00000 1.000000
3 colSums==n 5.730273 5.213316 4.610238 4.514297 4.26784 3.260209
2 apply+all 70.678680 62.113524 52.795195 52.001186 48.17577 21.571461
Table: Benchmarking of rowAlls(), apply+all() and rowSums==n() on 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 rowAlls 0.008252 0.0089090 0.0095079 0.0093445 0.0096925 0.020623
3 rowSums==n 0.034393 0.0359225 0.0370284 0.0369885 0.0373710 0.048771
2 apply+all 0.185038 0.1923415 0.1998491 0.1964000 0.2026280 0.301966
expr min lq mean median uq max
1 rowAlls 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
3 rowSums==n 4.167838 4.032159 3.894476 3.958318 3.855662 2.364884
2 apply+all 22.423413 21.589572 21.019226 21.017711 20.905649 14.642196
Figure: Benchmarking of colAlls(), apply+all() and colSums==n() on 100x100 data as well as rowAlls(), apply+all() and rowSums==n() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colAlls() and rowAlls() on 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 colAlls 2.636 3.1315 3.79039 3.7945 4.1760 13.224
2 rowAlls 8.252 8.9090 9.50792 9.3445 9.6925 20.623
expr min lq mean median uq max
1 colAlls 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
2 rowAlls 3.130501 2.844962 2.508428 2.462643 2.321001 1.559513
Figure: Benchmarking of colAlls() and rowAlls() on 100x100 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

1000x10 matrix

> X <- data[["1000x10"]]
> gc()
          used (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 1536101 82.1    2637877 140.9  2637877 140.9
Vcells 2143024 16.4    5986528  45.7 55745682 425.4
> colStats <- microbenchmark(colAlls = colAlls(X), `apply+all` = apply(X, MARGIN = 2L, FUN = all), 
+     `colSums==n` = (colSums(X) == nrow(X)), unit = "ms")
> X <- t(X)
> gc()
          used (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 1536095 82.1    2637877 140.9  2637877 140.9
Vcells 2148067 16.4    5986528  45.7 55745682 425.4
> rowStats <- microbenchmark(rowAlls = rowAlls(X), `apply+all` = apply(X, MARGIN = 1L, FUN = all), 
+     `rowSums==n` = (rowSums(X) == ncol(X)), unit = "ms")

Table: Benchmarking of colAlls(), apply+all() and colSums==n() on 1000x10 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 colAlls 0.001651 0.0019695 0.0026153 0.0025975 0.0028445 0.013843
3 colSums==n 0.015934 0.0164495 0.0174766 0.0173985 0.0178750 0.031607
2 apply+all 0.104468 0.1072815 0.1112322 0.1090035 0.1120520 0.175632
expr min lq mean median uq max
1 colAlls 1.00000 1.00000 1.000000 1.000000 1.000000 1.000000
3 colSums==n 9.65112 8.35212 6.682328 6.698171 6.284057 2.283248
2 apply+all 63.27559 54.47144 42.530665 41.964774 39.392512 12.687423
Table: Benchmarking of rowAlls(), apply+all() and rowSums==n() on 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 rowAlls 0.007292 0.0084570 0.0089494 0.0088990 0.0092060 0.017525
3 rowSums==n 0.035133 0.0358775 0.0371603 0.0367395 0.0381025 0.047463
2 apply+all 0.104138 0.1066025 0.1110490 0.1088720 0.1129890 0.175570
expr min lq mean median uq max
1 rowAlls 1.00000 1.000000 1.000000 1.000000 1.000000 1.000000
3 rowSums==n 4.81802 4.242344 4.152261 4.128498 4.138877 2.708302
2 apply+all 14.28113 12.605238 12.408511 12.234184 12.273409 10.018260
Figure: Benchmarking of colAlls(), apply+all() and colSums==n() on 1000x10 data as well as rowAlls(), apply+all() and rowSums==n() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colAlls() and rowAlls() on 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 colAlls 1.651 1.9695 2.61534 2.5975 2.8445 13.843
2 rowAlls 7.292 8.4570 8.94942 8.8990 9.2060 17.525
expr min lq mean median uq max
1 colAlls 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
2 rowAlls 4.416717 4.293983 3.421895 3.425987 3.236421 1.265983
Figure: Benchmarking of colAlls() and rowAlls() on 1000x10 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

10x1000 matrix

> X <- data[["10x1000"]]
> gc()
          used (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 1536201 82.1    2637877 140.9  2637877 140.9
Vcells 2143684 16.4    5986528  45.7 55745682 425.4
> colStats <- microbenchmark(colAlls = colAlls(X), `apply+all` = apply(X, MARGIN = 2L, FUN = all), 
+     `colSums==n` = (colSums(X) == nrow(X)), unit = "ms")
> X <- t(X)
> gc()
          used (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 1536195 82.1    2637877 140.9  2637877 140.9
Vcells 2148727 16.4    5986528  45.7 55745682 425.4
> rowStats <- microbenchmark(rowAlls = rowAlls(X), `apply+all` = apply(X, MARGIN = 1L, FUN = all), 
+     `rowSums==n` = (rowSums(X) == ncol(X)), unit = "ms")

Table: Benchmarking of colAlls(), apply+all() and colSums==n() on 10x1000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 colAlls 0.012207 0.0138865 0.0155181 0.015215 0.016485 0.025999
3 colSums==n 0.013881 0.0158550 0.0177842 0.017109 0.019051 0.041874
2 apply+all 0.880228 0.9383155 1.0128847 1.005190 1.036112 2.638512
expr min lq mean median uq max
1 colAlls 1.000000 1.000000 1.000000 1.000000 1.000000 1.0000
3 colSums==n 1.137134 1.141756 1.146028 1.124482 1.155657 1.6106
2 apply+all 72.108462 67.570338 65.271009 66.065725 62.851835 101.4851
Table: Benchmarking of rowAlls(), apply+all() and rowSums==n() on 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 rowAlls 0.019494 0.0229110 0.0251603 0.0247665 0.0264305 0.041038
3 rowSums==n 0.032585 0.0354995 0.0388549 0.0381830 0.0403165 0.062119
2 apply+all 0.879429 0.9777445 1.0366590 1.0117405 1.0394245 2.616813
expr min lq mean median uq max
1 rowAlls 1.00000 1.000000 1.000000 1.00000 1.000000 1.000000
3 rowSums==n 1.67154 1.549452 1.544295 1.54172 1.525378 1.513695
2 apply+all 45.11280 42.675767 41.202205 40.85117 39.326706 63.765608
Figure: Benchmarking of colAlls(), apply+all() and colSums==n() on 10x1000 data as well as rowAlls(), apply+all() and rowSums==n() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colAlls() and rowAlls() on 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 colAlls 12.207 13.8865 15.51814 15.2150 16.4850 25.999
2 rowAlls 19.494 22.9110 25.16028 24.7665 26.4305 41.038
expr min lq mean median uq max
1 colAlls 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
2 rowAlls 1.596953 1.649876 1.621346 1.627769 1.603306 1.578445
Figure: Benchmarking of colAlls() and rowAlls() on 10x1000 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

100x1000 matrix

> X <- data[["100x1000"]]
> gc()
          used (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 1536318 82.1    2637877 140.9  2637877 140.9
Vcells 2144131 16.4    5986528  45.7 55745682 425.4
> colStats <- microbenchmark(colAlls = colAlls(X), `apply+all` = apply(X, MARGIN = 2L, FUN = all), 
+     `colSums==n` = (colSums(X) == nrow(X)), unit = "ms")
> X <- t(X)
> gc()
          used (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 1536312 82.1    2637877 140.9  2637877 140.9
Vcells 2194174 16.8    5986528  45.7 55745682 425.4
> rowStats <- microbenchmark(rowAlls = rowAlls(X), `apply+all` = apply(X, MARGIN = 1L, FUN = all), 
+     `rowSums==n` = (rowSums(X) == ncol(X)), unit = "ms")

Table: Benchmarking of colAlls(), apply+all() and colSums==n() on 100x1000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 colAlls 0.013471 0.0150050 0.0184525 0.0164855 0.0186845 0.051676
3 colSums==n 0.089160 0.0954235 0.1043528 0.1019440 0.1103905 0.148491
2 apply+all 1.426726 1.5364600 1.8411169 1.6440405 1.7629815 6.672309
expr min lq mean median uq max
1 colAlls 1.000000 1.000000 1.000000 1.000000 1.000000 1.0000
3 colSums==n 6.618662 6.359447 5.655213 6.183859 5.908132 2.8735
2 apply+all 105.910920 102.396535 99.776016 99.726457 94.355294 129.1181
Table: Benchmarking of rowAlls(), apply+all() and rowSums==n() on 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 rowAlls 0.053982 0.0587140 0.0642004 0.0636055 0.068877 0.090047
3 rowSums==n 0.244231 0.2559055 0.3254041 0.2750305 0.297211 4.623414
2 apply+all 1.445115 1.5159245 1.7907320 1.6103005 1.761297 6.852790
expr min lq mean median uq max
1 rowAlls 1.000000 1.000000 1.000000 1.000000 1.000000 1.00000
3 rowSums==n 4.524304 4.358509 5.068566 4.324005 4.315098 51.34445
2 apply+all 26.770312 25.818791 27.892839 25.317001 25.571628 76.10237
Figure: Benchmarking of colAlls(), apply+all() and colSums==n() on 100x1000 data as well as rowAlls(), apply+all() and rowSums==n() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colAlls() and rowAlls() on 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 colAlls 13.471 15.005 18.45250 16.4855 18.6845 51.676
2 rowAlls 53.982 58.714 64.20042 63.6055 68.8770 90.047
expr min lq mean median uq max
1 colAlls 1.000000 1.000000 1.000000 1.000000 1.000000 1.00000
2 rowAlls 4.007275 3.912962 3.479226 3.858269 3.686317 1.74253
Figure: Benchmarking of colAlls() and rowAlls() on 100x1000 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

1000x100 matrix

> X <- data[["1000x100"]]
> gc()
          used (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 1536422 82.1    2637877 140.9  2637877 140.9
Vcells 2144722 16.4    5986528  45.7 55745682 425.4
> colStats <- microbenchmark(colAlls = colAlls(X), `apply+all` = apply(X, MARGIN = 2L, FUN = all), 
+     `colSums==n` = (colSums(X) == nrow(X)), unit = "ms")
> X <- t(X)
> gc()
          used (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 1536416 82.1    2637877 140.9  2637877 140.9
Vcells 2194765 16.8    5986528  45.7 55745682 425.4
> rowStats <- microbenchmark(rowAlls = rowAlls(X), `apply+all` = apply(X, MARGIN = 1L, FUN = all), 
+     `rowSums==n` = (rowSums(X) == ncol(X)), unit = "ms")

Table: Benchmarking of colAlls(), apply+all() and colSums==n() on 1000x100 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 colAlls 0.002760 0.0037995 0.0052718 0.0047010 0.0058355 0.016144
3 colSums==n 0.096585 0.1059565 0.1122891 0.1132125 0.1173195 0.144481
2 apply+all 0.744418 0.8274860 0.9655296 0.8815355 0.9199280 3.360772
expr min lq mean median uq max
1 colAlls 1.00000 1.00000 1.00000 1.00000 1.00000 1.000000
3 colSums==n 34.99457 27.88696 21.29983 24.08264 20.10445 8.949517
2 apply+all 269.71667 217.78813 183.14885 187.52085 157.64339 208.174678
Table: Benchmarking of rowAlls(), apply+all() and rowSums==n() on 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 rowAlls 0.051885 0.056650 0.0633111 0.0604540 0.0656025 0.231503
3 rowSums==n 0.248694 0.266145 0.2847173 0.2828595 0.3030625 0.325078
2 apply+all 0.734908 0.814167 0.9510324 0.8519450 0.9069325 3.404670
expr min lq mean median uq max
1 rowAlls 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
3 rowSums==n 4.793177 4.698058 4.497113 4.678921 4.619679 1.404206
2 apply+all 14.164171 14.371880 15.021568 14.092450 13.824664 14.706807
Figure: Benchmarking of colAlls(), apply+all() and colSums==n() on 1000x100 data as well as rowAlls(), apply+all() and rowSums==n() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colAlls() and rowAlls() on 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 colAlls 2.760 3.7995 5.27183 4.701 5.8355 16.144
2 rowAlls 51.885 56.6500 63.31113 60.454 65.6025 231.503
expr min lq mean median uq max
1 colAlls 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000
2 rowAlls 18.79891 14.90986 12.00933 12.85982 11.24197 14.33988
Figure: Benchmarking of colAlls() and rowAlls() on 1000x100 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

Appendix

Session information

R version 3.4.2 (2017-09-28)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 16.04.3 LTS

Matrix products: default
BLAS: /usr/lib/atlas-base/atlas/libblas.so.3.0
LAPACK: /usr/lib/atlas-base/atlas/liblapack.so.3.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               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    LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C             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_1.4-2.1  matrixStats_0.52.2-9000 ggplot2_2.2.1           knitr_1.17             
[5] R.devices_2.15.1-9000   R.utils_2.6.0           R.oo_1.21.0             R.methodsS3_1.7.1      

loaded via a namespace (and not attached):
 [1] Rcpp_0.12.13      compiler_3.4.2    plyr_1.8.4        highr_0.6.1       base64enc_0.1-4   tools_3.4.2      
 [7] digest_0.6.12     tibble_1.3.4      gtable_0.2.0      R.cache_0.12.0    lattice_0.20-35   rlang_0.1.2      
[13] Matrix_1.2-11     mvtnorm_1.0-6     R.rsp_0.41.0-9000 grid_3.4.2        prompt_1.0.0      survival_2.41-3  
[19] multcomp_1.4-7    TH.data_1.0-8     scales_0.5.0      clisymbols_1.2.0  codetools_0.2-15  splines_3.4.2    
[25] MASS_7.3-47       mime_0.5.1        memuse_3.0-1      colorspace_1.3-2  labeling_0.3      sandwich_2.4-0   
[31] lazyeval_0.2.1    munsell_0.4.3     markdown_0.8.1    crayon_1.3.4      Cairo_1.5-9       zoo_1.8-0

Total processing time was 7.19 secs.

Reproducibility

To reproduce this report, do:

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

Copyright Henrik Bengtsson. Last updated on 2017-11-04 15:04:52 (-0700 UTC). Powered by RSP.

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