colRowProds - HenrikBengtsson/matrixStats GitHub Wiki

matrixStats: Benchmark report

colProds() and rowProds() benchmarks

This report benchmark the performance of colProds() and rowProds() against alternative methods.

Alternative methods

  • colProds()/rowProds() using method="expSumLog"
  • apply() + prod()
  • apply() + product()

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 = "double")

Results

10x10 matrix

> X <- data[["10x10"]]
> colStats <- microbenchmark(`colProds w/ direct` = colProds(X, method = "direct", na.rm = FALSE), 
+     `colProds w/ expSumLog` = colProds(X, method = "expSumLog", na.rm = FALSE), `apply+prod` = apply(X, 
+         MARGIN = 2L, FUN = prod, na.rm = FALSE), `apply+product` = apply(X, MARGIN = 2L, FUN = product, 
+         na.rm = FALSE), unit = "ms")
> X <- t(X)
> rowStats <- microbenchmark(`rowProds w/ direct` = rowProds(X, method = "direct", na.rm = FALSE), 
+     `rowProds w/ expSumLog` = rowProds(X, method = "expSumLog", na.rm = FALSE), `apply+prod` = apply(X, 
+         MARGIN = 1L, FUN = prod, na.rm = FALSE), `apply+product` = apply(X, MARGIN = 1L, FUN = product, 
+         na.rm = FALSE), unit = "ms")

Table: Benchmarking of colProds w/ direct(), colProds w/ expSumLog(), apply+prod() and apply+product() 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 colProds w/ direct 0.008800 0.0094145 0.0099799 0.0098050 0.0102595 0.021529
2 colProds w/ expSumLog 0.014226 0.0148440 0.0154650 0.0155405 0.0158780 0.019312
3 apply+prod 0.020025 0.0210960 0.0215453 0.0214540 0.0218900 0.028227
4 apply+product 0.027252 0.0277515 0.0293899 0.0280850 0.0284865 0.123770
expr min lq mean median uq max
1 colProds w/ direct 1.000000 1.000000 1.000000 1.000000 1.000000 1.0000000
2 colProds w/ expSumLog 1.616591 1.576717 1.549612 1.584957 1.547639 0.8970226
3 apply+prod 2.275568 2.240799 2.158871 2.188067 2.133632 1.3111152
4 apply+product 3.096818 2.947740 2.944905 2.864355 2.776597 5.7489897

Table: Benchmarking of rowProds w/ direct(), rowProds w/ expSumLog(), apply+prod() and apply+product() 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 rowProds w/ direct 0.008971 0.0094915 0.0101705 0.0100415 0.0104500 0.020142
2 rowProds w/ expSumLog 0.014368 0.0149075 0.0159106 0.0156585 0.0162505 0.041111
3 apply+prod 0.019603 0.0206265 0.0223654 0.0212315 0.0216405 0.103849
4 apply+product 0.026428 0.0273865 0.0279480 0.0276900 0.0281740 0.037545
expr min lq mean median uq max
1 rowProds w/ direct 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
2 rowProds w/ expSumLog 1.601605 1.570616 1.564385 1.559379 1.555072 2.041059
3 apply+prod 2.185152 2.173155 2.199039 2.114375 2.070861 5.155843
4 apply+product 2.945937 2.885371 2.747940 2.757556 2.696077 1.864016

Figure: Benchmarking of colProds w/ direct(), colProds w/ expSumLog(), apply+prod() and apply+product() on 10x10 data as well as rowProds w/ direct(), rowProds w/ expSumLog(), apply+prod() and apply+product() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colProds w/ direct() and rowProds w/ direct() 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
1 colProds w/ direct 8.800 9.4145 9.97990 9.8050 10.2595 21.529
2 rowProds w/ direct 8.971 9.4915 10.17054 10.0415 10.4500 20.142
expr min lq mean median uq max
1 colProds w/ direct 1.000000 1.000000 1.000000 1.00000 1.000000 1.0000000
2 rowProds w/ direct 1.019432 1.008179 1.019102 1.02412 1.018568 0.9355753

Figure: Benchmarking of colProds w/ direct() and rowProds w/ direct() on 10x10 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

100x100 matrix

> X <- data[["100x100"]]
> colStats <- microbenchmark(`colProds w/ direct` = colProds(X, method = "direct", na.rm = FALSE), 
+     `colProds w/ expSumLog` = colProds(X, method = "expSumLog", na.rm = FALSE), `apply+prod` = apply(X, 
+         MARGIN = 2L, FUN = prod, na.rm = FALSE), `apply+product` = apply(X, MARGIN = 2L, FUN = product, 
+         na.rm = FALSE), unit = "ms")
> X <- t(X)
> rowStats <- microbenchmark(`rowProds w/ direct` = rowProds(X, method = "direct", na.rm = FALSE), 
+     `rowProds w/ expSumLog` = rowProds(X, method = "expSumLog", na.rm = FALSE), `apply+prod` = apply(X, 
+         MARGIN = 1L, FUN = prod, na.rm = FALSE), `apply+product` = apply(X, MARGIN = 1L, FUN = product, 
+         na.rm = FALSE), unit = "ms")

Table: Benchmarking of colProds w/ direct(), colProds w/ expSumLog(), apply+prod() and apply+product() 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 colProds w/ direct 0.114208 0.1154645 0.1184836 0.1166710 0.1191515 0.168758
3 apply+prod 0.185868 0.1880695 0.1918381 0.1896675 0.1931980 0.212631
2 colProds w/ expSumLog 0.362362 0.3652075 0.3701785 0.3678675 0.3719820 0.397963
4 apply+product 0.444140 0.4475500 0.4544683 0.4501985 0.4545590 0.594481
expr min lq mean median uq max
1 colProds w/ direct 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
3 apply+prod 1.627452 1.628808 1.619111 1.625661 1.621448 1.259976
2 colProds w/ expSumLog 3.172825 3.162942 3.124301 3.153033 3.121925 2.358188
4 apply+product 3.888869 3.876083 3.835705 3.858701 3.814967 3.522683

Table: Benchmarking of rowProds w/ direct(), rowProds w/ expSumLog(), apply+prod() and apply+product() 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 rowProds w/ direct 0.115724 0.1179720 0.1204872 0.1190215 0.1209745 0.167360
3 apply+prod 0.144730 0.1471305 0.1543799 0.1487155 0.1518415 0.540496
2 rowProds w/ expSumLog 0.366354 0.3695335 0.3764591 0.3714560 0.3754380 0.508744
4 apply+product 0.402775 0.4066835 0.4141205 0.4104350 0.4154095 0.519079
expr min lq mean median uq max
1 rowProds w/ direct 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
3 apply+prod 1.250648 1.247165 1.281297 1.249484 1.255153 3.229541
2 rowProds w/ expSumLog 3.165757 3.132383 3.124474 3.120915 3.103447 3.039818
4 apply+product 3.480479 3.447288 3.437050 3.448411 3.433860 3.101571

Figure: Benchmarking of colProds w/ direct(), colProds w/ expSumLog(), apply+prod() and apply+product() on 100x100 data as well as rowProds w/ direct(), rowProds w/ expSumLog(), apply+prod() and apply+product() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colProds w/ direct() and rowProds w/ direct() 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 colProds w/ direct 114.208 115.4645 118.4836 116.6710 119.1515 168.758
2 rowProds w/ direct 115.724 117.9720 120.4872 119.0215 120.9745 167.360
expr min lq mean median uq max
1 colProds w/ direct 1.000000 1.000000 1.00000 1.000000 1.0000 1.0000000
2 rowProds w/ direct 1.013274 1.021717 1.01691 1.020146 1.0153 0.9917159

Figure: Benchmarking of colProds w/ direct() and rowProds w/ direct() on 100x100 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

1000x10 matrix

> X <- data[["1000x10"]]
> colStats <- microbenchmark(`colProds w/ direct` = colProds(X, method = "direct", na.rm = FALSE), 
+     `colProds w/ expSumLog` = colProds(X, method = "expSumLog", na.rm = FALSE), `apply+prod` = apply(X, 
+         MARGIN = 2L, FUN = prod, na.rm = FALSE), `apply+product` = apply(X, MARGIN = 2L, FUN = product, 
+         na.rm = FALSE), unit = "ms")
> X <- t(X)
> rowStats <- microbenchmark(`rowProds w/ direct` = rowProds(X, method = "direct", na.rm = FALSE), 
+     `rowProds w/ expSumLog` = rowProds(X, method = "expSumLog", na.rm = FALSE), `apply+prod` = apply(X, 
+         MARGIN = 1L, FUN = prod, na.rm = FALSE), `apply+product` = apply(X, MARGIN = 1L, FUN = product, 
+         na.rm = FALSE), unit = "ms")

Table: Benchmarking of colProds w/ direct(), colProds w/ expSumLog(), apply+prod() and apply+product() 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 colProds w/ direct 0.056584 0.0594655 0.0608798 0.0605925 0.0617985 0.076076
3 apply+prod 0.118388 0.1214215 0.1260853 0.1234645 0.1250075 0.223058
2 colProds w/ expSumLog 0.264604 0.2671600 0.2710972 0.2685945 0.2701615 0.351917
4 apply+product 0.328278 0.3319470 0.3385626 0.3333385 0.3350340 0.719578
expr min lq mean median uq max
1 colProds w/ direct 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
3 apply+prod 2.092252 2.041881 2.071054 2.037620 2.022824 2.932042
2 colProds w/ expSumLog 4.676304 4.492689 4.452994 4.432801 4.371651 4.625861
4 apply+product 5.801605 5.582178 5.561168 5.501316 5.421394 9.458673

Table: Benchmarking of rowProds w/ direct(), rowProds w/ expSumLog(), apply+prod() and apply+product() 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 rowProds w/ direct 0.058456 0.0606645 0.0625605 0.0625085 0.0636825 0.072641
3 apply+prod 0.076469 0.0810880 0.0838753 0.0829305 0.0848060 0.106722
2 rowProds w/ expSumLog 0.267670 0.2705885 0.2730204 0.2723735 0.2736355 0.292592
4 apply+product 0.287905 0.2904395 0.2956810 0.2922185 0.2960595 0.390372
expr min lq mean median uq max
1 rowProds w/ direct 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
3 apply+prod 1.308146 1.336663 1.340706 1.326708 1.331700 1.469170
2 rowProds w/ expSumLog 4.579000 4.460409 4.364101 4.357383 4.296871 4.027918
4 apply+product 4.925157 4.787635 4.726320 4.674860 4.648993 5.373990

Figure: Benchmarking of colProds w/ direct(), colProds w/ expSumLog(), apply+prod() and apply+product() on 1000x10 data as well as rowProds w/ direct(), rowProds w/ expSumLog(), apply+prod() and apply+product() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colProds w/ direct() and rowProds w/ direct() 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 colProds w/ direct 56.584 59.4655 60.87977 60.5925 61.7985 76.076
2 rowProds w/ direct 58.456 60.6645 62.56052 62.5085 63.6825 72.641
expr min lq mean median uq max
1 colProds w/ direct 1.000000 1.000000 1.000000 1.000000 1.000000 1.0000000
2 rowProds w/ direct 1.033084 1.020163 1.027608 1.031621 1.030486 0.9548478

Figure: Benchmarking of colProds w/ direct() and rowProds w/ direct() on 1000x10 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

10x1000 matrix

> X <- data[["10x1000"]]
> colStats <- microbenchmark(`colProds w/ direct` = colProds(X, method = "direct", na.rm = FALSE), 
+     `colProds w/ expSumLog` = colProds(X, method = "expSumLog", na.rm = FALSE), `apply+prod` = apply(X, 
+         MARGIN = 2L, FUN = prod, na.rm = FALSE), `apply+product` = apply(X, MARGIN = 2L, FUN = product, 
+         na.rm = FALSE), unit = "ms")
> X <- t(X)
> rowStats <- microbenchmark(`rowProds w/ direct` = rowProds(X, method = "direct", na.rm = FALSE), 
+     `rowProds w/ expSumLog` = rowProds(X, method = "expSumLog", na.rm = FALSE), `apply+prod` = apply(X, 
+         MARGIN = 1L, FUN = prod, na.rm = FALSE), `apply+product` = apply(X, MARGIN = 1L, FUN = product, 
+         na.rm = FALSE), unit = "ms")

Table: Benchmarking of colProds w/ direct(), colProds w/ expSumLog(), apply+prod() and apply+product() 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 colProds w/ direct 0.644066 0.684814 0.7599742 0.6989315 0.7216265 5.798954
3 apply+prod 0.812111 0.834671 0.8740589 0.8537895 0.8796950 1.472521
2 colProds w/ expSumLog 1.226847 1.278935 1.3722292 1.3015150 1.3495120 6.507626
4 apply+product 1.464395 1.505959 1.5636033 1.5311550 1.5869835 2.282037
expr min lq mean median uq max
1 colProds w/ direct 1.000000 1.000000 1.000000 1.000000 1.000000 1.0000000
3 apply+prod 1.260913 1.218829 1.150116 1.221564 1.219045 0.2539287
2 colProds w/ expSumLog 1.904847 1.867565 1.805626 1.862150 1.870098 1.1222069
4 apply+product 2.273672 2.199078 2.057443 2.190708 2.199176 0.3935256

Table: Benchmarking of rowProds w/ direct(), rowProds w/ expSumLog(), apply+prod() and apply+product() 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 rowProds w/ direct 0.665562 0.702727 0.7232769 0.715927 0.7426305 0.802537
3 apply+prod 0.798166 0.830586 0.8740196 0.858047 0.8960415 1.202002
2 rowProds w/ expSumLog 1.248327 1.308507 1.4198041 1.349049 1.3859660 6.681312
4 apply+product 1.466300 1.523035 1.6262461 1.564934 1.6061500 6.903179
expr min lq mean median uq max
1 rowProds w/ direct 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
3 apply+prod 1.199236 1.181947 1.208416 1.198512 1.206578 1.497753
2 rowProds w/ expSumLog 1.875598 1.862042 1.963016 1.884339 1.866293 8.325239
4 apply+product 2.203101 2.167321 2.248442 2.185886 2.162785 8.601696

Figure: Benchmarking of colProds w/ direct(), colProds w/ expSumLog(), apply+prod() and apply+product() on 10x1000 data as well as rowProds w/ direct(), rowProds w/ expSumLog(), apply+prod() and apply+product() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colProds w/ direct() and rowProds w/ direct() 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 colProds w/ direct 644.066 684.814 759.9742 698.9315 721.6265 5798.954
2 rowProds w/ direct 665.562 702.727 723.2769 715.9270 742.6305 802.537
expr min lq mean median uq max
1 colProds w/ direct 1.000000 1.000000 1.0000000 1.000000 1.000000 1.0000000
2 rowProds w/ direct 1.033376 1.026158 0.9517124 1.024316 1.029106 0.1383934

Figure: Benchmarking of colProds w/ direct() and rowProds w/ direct() on 10x1000 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

100x1000 matrix

> X <- data[["100x1000"]]
> colStats <- microbenchmark(`colProds w/ direct` = colProds(X, method = "direct", na.rm = FALSE), 
+     `colProds w/ expSumLog` = colProds(X, method = "expSumLog", na.rm = FALSE), `apply+prod` = apply(X, 
+         MARGIN = 2L, FUN = prod, na.rm = FALSE), `apply+product` = apply(X, MARGIN = 2L, FUN = product, 
+         na.rm = FALSE), unit = "ms")
> X <- t(X)
> rowStats <- microbenchmark(`rowProds w/ direct` = rowProds(X, method = "direct", na.rm = FALSE), 
+     `rowProds w/ expSumLog` = rowProds(X, method = "expSumLog", na.rm = FALSE), `apply+prod` = apply(X, 
+         MARGIN = 1L, FUN = prod, na.rm = FALSE), `apply+product` = apply(X, MARGIN = 1L, FUN = product, 
+         na.rm = FALSE), unit = "ms")

Table: Benchmarking of colProds w/ direct(), colProds w/ expSumLog(), apply+prod() and apply+product() 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 colProds w/ direct 1.109562 1.120069 1.179608 1.139917 1.175838 1.720146
3 apply+prod 1.734878 1.757721 2.148371 1.782883 1.842180 17.418038
2 colProds w/ expSumLog 3.583810 3.615213 4.044786 3.647726 3.716857 19.286408
4 apply+product 4.305582 4.347441 4.700679 4.420883 4.504060 21.477891
expr min lq mean median uq max
1 colProds w/ direct 1.000000 1.000000 1.000000 1.000000 1.000000 1.00000
3 apply+prod 1.563570 1.569297 1.821258 1.564046 1.566695 10.12591
2 colProds w/ expSumLog 3.229932 3.227670 3.428923 3.199993 3.161028 11.21208
4 apply+product 3.880434 3.881404 3.984949 3.878250 3.830510 12.48609

Table: Benchmarking of rowProds w/ direct(), rowProds w/ expSumLog(), apply+prod() and apply+product() 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 rowProds w/ direct 1.150689 1.169666 1.258584 1.179300 1.222171 1.889199
3 apply+prod 1.319024 1.338129 1.866213 1.355963 1.388661 17.378289
2 rowProds w/ expSumLog 3.640529 3.670341 3.997877 3.680613 3.730546 20.434404
4 apply+product 3.881884 3.928486 4.268454 3.944160 4.031906 20.527597
expr min lq mean median uq max
1 rowProds w/ direct 1.000000 1.000000 1.000000 1.000000 1.000000 1.00000
3 apply+prod 1.146291 1.144027 1.482788 1.149803 1.136224 9.19876
2 rowProds w/ expSumLog 3.163782 3.137939 3.176489 3.121015 3.052392 10.81644
4 apply+product 3.373530 3.358639 3.391473 3.344492 3.298970 10.86577

Figure: Benchmarking of colProds w/ direct(), colProds w/ expSumLog(), apply+prod() and apply+product() on 100x1000 data as well as rowProds w/ direct(), rowProds w/ expSumLog(), apply+prod() and apply+product() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colProds w/ direct() and rowProds w/ direct() 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 colProds w/ direct 1.109562 1.120069 1.179608 1.139917 1.175838 1.720146
2 rowProds w/ direct 1.150689 1.169666 1.258584 1.179300 1.222171 1.889199
expr min lq mean median uq max
1 colProds w/ direct 1.000000 1.00000 1.000000 1.000000 1.000000 1.000000
2 rowProds w/ direct 1.037066 1.04428 1.066951 1.034549 1.039405 1.098278

Figure: Benchmarking of colProds w/ direct() and rowProds w/ direct() on 100x1000 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

1000x100 matrix

> X <- data[["1000x100"]]
> colStats <- microbenchmark(`colProds w/ direct` = colProds(X, method = "direct", na.rm = FALSE), 
+     `colProds w/ expSumLog` = colProds(X, method = "expSumLog", na.rm = FALSE), `apply+prod` = apply(X, 
+         MARGIN = 2L, FUN = prod, na.rm = FALSE), `apply+product` = apply(X, MARGIN = 2L, FUN = product, 
+         na.rm = FALSE), unit = "ms")
> X <- t(X)
> rowStats <- microbenchmark(`rowProds w/ direct` = rowProds(X, method = "direct", na.rm = FALSE), 
+     `rowProds w/ expSumLog` = rowProds(X, method = "expSumLog", na.rm = FALSE), `apply+prod` = apply(X, 
+         MARGIN = 1L, FUN = prod, na.rm = FALSE), `apply+product` = apply(X, MARGIN = 1L, FUN = product, 
+         na.rm = FALSE), unit = "ms")

Table: Benchmarking of colProds w/ direct(), colProds w/ expSumLog(), apply+prod() and apply+product() 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 colProds w/ direct 0.363557 0.3716730 0.3887810 0.3839445 0.3921995 0.477998
3 apply+prod 0.640136 0.6542385 0.9097683 0.6858240 0.7200375 7.951854
2 colProds w/ expSumLog 2.418585 2.4573250 2.5319060 2.4730465 2.4955020 3.160826
4 apply+product 2.732607 2.7511875 2.9202926 2.7832505 2.8313660 9.714860
expr min lq mean median uq max
1 colProds w/ direct 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
3 apply+prod 1.760758 1.760253 2.340053 1.786258 1.835896 16.635747
2 colProds w/ expSumLog 6.652561 6.611524 6.512422 6.441156 6.362838 6.612634
4 apply+product 7.516310 7.402172 7.511408 7.249096 7.219198 20.324060

Table: Benchmarking of rowProds w/ direct(), rowProds w/ expSumLog(), apply+prod() and apply+product() 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 rowProds w/ direct 0.594052 0.602855 0.7037156 0.609530 0.6415405 7.690274
3 apply+prod 0.666871 0.684659 0.7341861 0.715175 0.7453200 1.080402
2 rowProds w/ expSumLog 2.701753 2.731766 2.9677295 2.741329 2.7817565 10.744616
4 apply+product 2.767487 2.815783 3.0263775 2.830687 2.8881060 15.105563
expr min lq mean median uq max
1 rowProds w/ direct 1.000000 1.000000 1.000000 1.000000 1.000000 1.0000000
3 apply+prod 1.122580 1.135694 1.043300 1.173322 1.161766 0.1404894
2 rowProds w/ expSumLog 4.548008 4.531382 4.217229 4.497448 4.336057 1.3971695
4 apply+product 4.658661 4.670746 4.300569 4.644050 4.501830 1.9642425

Figure: Benchmarking of colProds w/ direct(), colProds w/ expSumLog(), apply+prod() and apply+product() on 1000x100 data as well as rowProds w/ direct(), rowProds w/ expSumLog(), apply+prod() and apply+product() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colProds w/ direct() and rowProds w/ direct() 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 colProds w/ direct 363.557 371.673 388.7810 383.9445 392.1995 477.998
2 rowProds w/ direct 594.052 602.855 703.7156 609.5300 641.5405 7690.274
expr min lq mean median uq max
1 colProds w/ direct 1.000 1.000000 1.000000 1.000000 1.00000 1.00000
2 rowProds w/ direct 1.634 1.622004 1.810057 1.587547 1.63575 16.08851

Figure: Benchmarking of colProds w/ direct() and rowProds w/ direct() on 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 17.46 secs.

Reproducibility

To reproduce this report, do:

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

Copyright Henrik Bengtsson. Last updated on 2019-09-10 20:49:13 (-0700 UTC). Powered by RSP.

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