matrixStats.benchmarks
colTabulates() and rowTabulates() benchmarks on subsetted computation
This report benchmark the performance of colTabulates() and rowTabulates() on subsetted computation.
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 = "integer", range = c(-10, 10))
Results
10x10 matrix
> X <- data[["10x10"]]
> rows <- sample.int(nrow(X), size = nrow(X) * 0.7)
> cols <- sample.int(ncol(X), size = ncol(X) * 0.7)
> X_S <- X[rows, cols]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5309434 283.6 7916910 422.9 7916910 422.9
Vcells 10540301 80.5 33191153 253.3 53339345 407.0
> colStats <- microbenchmark(colTabulates_X_S = colTabulates(X_S, na.rm = FALSE), `colTabulates(X, rows, cols)` = colTabulates(X,
+ rows = rows, cols = cols, na.rm = FALSE), `colTabulates(X[rows, cols])` = colTabulates(X[rows,
+ cols], na.rm = FALSE), unit = "ms")
> X <- t(X)
> X_S <- t(X_S)
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5309717 283.6 7916910 422.9 7916910 422.9
Vcells 10541349 80.5 33191153 253.3 53339345 407.0
> rowStats <- microbenchmark(rowTabulates_X_S = rowTabulates(X_S, na.rm = FALSE), `rowTabulates(X, cols, rows)` = rowTabulates(X,
+ rows = cols, cols = rows, na.rm = FALSE), `rowTabulates(X[cols, rows])` = rowTabulates(X[cols,
+ rows], na.rm = FALSE), unit = "ms")
Table: Benchmarking of colTabulates_X_S(), colTabulates(X, rows, cols)() and colTabulates(X[rows, cols])() on 10x10 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 3 | colTabulates(X[rows, cols]) | 0.150281 | 0.1653295 | 0.1796339 | 0.1741140 | 0.187048 | 0.253773 |
| 1 | colTabulates_X_S | 0.146889 | 0.1653320 | 0.1862859 | 0.1749945 | 0.194673 | 0.647950 |
| 2 | colTabulates(X, rows, cols) | 0.150339 | 0.1660475 | 0.1836304 | 0.1767630 | 0.197752 | 0.241339 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 3 | colTabulates(X[rows, cols]) | 1.0000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.0000000 |
| 1 | colTabulates_X_S | 0.9774289 | 1.000015 | 1.037031 | 1.005057 | 1.040765 | 2.5532661 |
| 2 | colTabulates(X, rows, cols) | 1.0003859 | 1.004343 | 1.022248 | 1.015214 | 1.057226 | 0.9510035 |
Table: Benchmarking of rowTabulates_X_S(), rowTabulates(X, cols, rows)() and rowTabulates(X[cols, rows])() 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 | |
|---|---|---|---|---|---|---|---|
| 3 | rowTabulates(X[cols, rows]) | 0.141842 | 0.1508515 | 0.1665028 | 0.1619565 | 0.171552 | 0.218309 |
| 2 | rowTabulates(X, cols, rows) | 0.141860 | 0.1513865 | 0.1741077 | 0.1639945 | 0.179348 | 0.641714 |
| 1 | rowTabulates_X_S | 0.139307 | 0.1497235 | 0.1715243 | 0.1653250 | 0.190447 | 0.221781 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 3 | rowTabulates(X[cols, rows]) | 1.000000 | 1.0000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 2 | rowTabulates(X, cols, rows) | 1.000127 | 1.0035465 | 1.045674 | 1.012584 | 1.045444 | 2.939476 |
| 1 | rowTabulates_X_S | 0.982128 | 0.9925224 | 1.030159 | 1.020799 | 1.110141 | 1.015904 |
Figure: Benchmarking of colTabulates_X_S(), colTabulates(X, rows, cols)() and colTabulates(X[rows, cols])() on 10x10 data as well as rowTabulates_X_S(), rowTabulates(X, cols, rows)() and rowTabulates(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.
Table: Benchmarking of colTabulates_X_S() and rowTabulates_X_S() 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 | rowTabulates_X_S | 139.307 | 149.7235 | 171.5243 | 165.3250 | 190.447 | 221.781 |
| 1 | colTabulates_X_S | 146.889 | 165.3320 | 186.2859 | 174.9945 | 194.673 | 647.950 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | rowTabulates_X_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.00000 | 1.000000 |
| 1 | colTabulates_X_S | 1.054427 | 1.104249 | 1.086061 | 1.058488 | 1.02219 | 2.921576 |
Figure: Benchmarking of colTabulates_X_S() and rowTabulates_X_S() on 10x10 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.
100x100 matrix
> X <- data[["100x100"]]
> rows <- sample.int(nrow(X), size = nrow(X) * 0.7)
> cols <- sample.int(ncol(X), size = ncol(X) * 0.7)
> X_S <- X[rows, cols]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5308760 283.6 7916910 422.9 7916910 422.9
Vcells 10210647 78.0 33191153 253.3 53339345 407.0
> colStats <- microbenchmark(colTabulates_X_S = colTabulates(X_S, na.rm = FALSE), `colTabulates(X, rows, cols)` = colTabulates(X,
+ rows = rows, cols = cols, na.rm = FALSE), `colTabulates(X[rows, cols])` = colTabulates(X[rows,
+ cols], na.rm = FALSE), unit = "ms")
> X <- t(X)
> X_S <- t(X_S)
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5308754 283.6 7916910 422.9 7916910 422.9
Vcells 10215730 78.0 33191153 253.3 53339345 407.0
> rowStats <- microbenchmark(rowTabulates_X_S = rowTabulates(X_S, na.rm = FALSE), `rowTabulates(X, cols, rows)` = rowTabulates(X,
+ rows = cols, cols = rows, na.rm = FALSE), `rowTabulates(X[cols, rows])` = rowTabulates(X[cols,
+ rows], na.rm = FALSE), unit = "ms")
Table: Benchmarking of colTabulates_X_S(), colTabulates(X, rows, cols)() and colTabulates(X[rows, cols])() 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 | colTabulates_X_S | 0.307639 | 0.3102545 | 0.3538999 | 0.322167 | 0.3870175 | 0.524295 |
| 3 | colTabulates(X[rows, cols]) | 0.315670 | 0.3194630 | 0.3664192 | 0.326460 | 0.4035855 | 0.677059 |
| 2 | colTabulates(X, rows, cols) | 0.316056 | 0.3197015 | 0.3617558 | 0.328848 | 0.3983450 | 0.565299 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | colTabulates_X_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 3 | colTabulates(X[rows, cols]) | 1.026105 | 1.029680 | 1.035375 | 1.013325 | 1.042809 | 1.291370 |
| 2 | colTabulates(X, rows, cols) | 1.027360 | 1.030449 | 1.022198 | 1.020738 | 1.029269 | 1.078208 |
Table: Benchmarking of rowTabulates_X_S(), rowTabulates(X, cols, rows)() and rowTabulates(X[cols, rows])() 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 | rowTabulates_X_S | 0.356096 | 0.3629345 | 0.4151751 | 0.3671960 | 0.456039 | 0.702722 |
| 2 | rowTabulates(X, cols, rows) | 0.365645 | 0.3715305 | 0.4177823 | 0.3751515 | 0.437584 | 0.794211 |
| 3 | rowTabulates(X[cols, rows]) | 0.363943 | 0.3707835 | 0.4126707 | 0.3759640 | 0.435015 | 0.619920 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | rowTabulates_X_S | 1.000000 | 1.000000 | 1.0000000 | 1.000000 | 1.0000000 | 1.0000000 |
| 2 | rowTabulates(X, cols, rows) | 1.026816 | 1.023685 | 1.0062798 | 1.021665 | 0.9595320 | 1.1301923 |
| 3 | rowTabulates(X[cols, rows]) | 1.022036 | 1.021626 | 0.9939679 | 1.023878 | 0.9538987 | 0.8821696 |
Figure: Benchmarking of colTabulates_X_S(), colTabulates(X, rows, cols)() and colTabulates(X[rows, cols])() on 100x100 data as well as rowTabulates_X_S(), rowTabulates(X, cols, rows)() and rowTabulates(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.
Table: Benchmarking of colTabulates_X_S() and rowTabulates_X_S() 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 | colTabulates_X_S | 307.639 | 310.2545 | 353.8999 | 322.167 | 387.0175 | 524.295 |
| 2 | rowTabulates_X_S | 356.096 | 362.9345 | 415.1751 | 367.196 | 456.0390 | 702.722 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | colTabulates_X_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 2 | rowTabulates_X_S | 1.157512 | 1.169796 | 1.173143 | 1.139769 | 1.178342 | 1.340318 |
Figure: Benchmarking of colTabulates_X_S() and rowTabulates_X_S() on 100x100 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.
1000x10 matrix
> X <- data[["1000x10"]]
> rows <- sample.int(nrow(X), size = nrow(X) * 0.7)
> cols <- sample.int(ncol(X), size = ncol(X) * 0.7)
> X_S <- X[rows, cols]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5309508 283.6 7916910 422.9 7916910 422.9
Vcells 10214715 78.0 33191153 253.3 53339345 407.0
> colStats <- microbenchmark(colTabulates_X_S = colTabulates(X_S, na.rm = FALSE), `colTabulates(X, rows, cols)` = colTabulates(X,
+ rows = rows, cols = cols, na.rm = FALSE), `colTabulates(X[rows, cols])` = colTabulates(X[rows,
+ cols], na.rm = FALSE), unit = "ms")
> X <- t(X)
> X_S <- t(X_S)
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5309496 283.6 7916910 422.9 7916910 422.9
Vcells 10219788 78.0 33191153 253.3 53339345 407.0
> rowStats <- microbenchmark(rowTabulates_X_S = rowTabulates(X_S, na.rm = FALSE), `rowTabulates(X, cols, rows)` = rowTabulates(X,
+ rows = cols, cols = rows, na.rm = FALSE), `rowTabulates(X[cols, rows])` = rowTabulates(X[cols,
+ rows], na.rm = FALSE), unit = "ms")
Table: Benchmarking of colTabulates_X_S(), colTabulates(X, rows, cols)() and colTabulates(X[rows, cols])() on 1000x10 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | colTabulates(X, rows, cols) | 0.297316 | 0.3009685 | 0.3384937 | 0.3038785 | 0.3711730 | 0.510221 |
| 3 | colTabulates(X[rows, cols]) | 0.297245 | 0.3014635 | 0.3396631 | 0.3055995 | 0.3749480 | 0.632409 |
| 1 | colTabulates_X_S | 0.288750 | 0.2923220 | 0.3324583 | 0.3125580 | 0.3669625 | 0.483274 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | colTabulates(X, rows, cols) | 1.0000000 | 1.0000000 | 1.0000000 | 1.000000 | 1.0000000 | 1.0000000 |
| 3 | colTabulates(X[rows, cols]) | 0.9997612 | 1.0016447 | 1.0034547 | 1.005663 | 1.0101705 | 1.2394805 |
| 1 | colTabulates_X_S | 0.9711889 | 0.9712711 | 0.9821697 | 1.028562 | 0.9886562 | 0.9471856 |
Table: Benchmarking of rowTabulates_X_S(), rowTabulates(X, cols, rows)() and rowTabulates(X[cols, rows])() 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 | rowTabulates_X_S | 0.351603 | 0.355973 | 0.3964701 | 0.3597425 | 0.4121985 | 0.603988 |
| 3 | rowTabulates(X[cols, rows]) | 0.361078 | 0.365708 | 0.4092195 | 0.3737970 | 0.4421335 | 0.740101 |
| 2 | rowTabulates(X, cols, rows) | 0.360883 | 0.366581 | 0.4112318 | 0.3779340 | 0.4498420 | 0.604123 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | rowTabulates_X_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 3 | rowTabulates(X[cols, rows]) | 1.026948 | 1.027348 | 1.032157 | 1.039068 | 1.072623 | 1.225357 |
| 2 | rowTabulates(X, cols, rows) | 1.026393 | 1.029800 | 1.037233 | 1.050568 | 1.091324 | 1.000223 |
Figure: Benchmarking of colTabulates_X_S(), colTabulates(X, rows, cols)() and colTabulates(X[rows, cols])() on 1000x10 data as well as rowTabulates_X_S(), rowTabulates(X, cols, rows)() and rowTabulates(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.
Table: Benchmarking of colTabulates_X_S() and rowTabulates_X_S() 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 | colTabulates_X_S | 288.750 | 292.322 | 332.4583 | 312.5580 | 366.9625 | 483.274 |
| 2 | rowTabulates_X_S | 351.603 | 355.973 | 396.4701 | 359.7425 | 412.1985 | 603.988 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | colTabulates_X_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 2 | rowTabulates_X_S | 1.217673 | 1.217743 | 1.192541 | 1.150962 | 1.123271 | 1.249784 |
Figure: Benchmarking of colTabulates_X_S() and rowTabulates_X_S() on 1000x10 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.
10x1000 matrix
> X <- data[["10x1000"]]
> rows <- sample.int(nrow(X), size = nrow(X) * 0.7)
> cols <- sample.int(ncol(X), size = ncol(X) * 0.7)
> X_S <- X[rows, cols]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5309713 283.6 7916910 422.9 7916910 422.9
Vcells 10215623 78.0 33191153 253.3 53339345 407.0
> colStats <- microbenchmark(colTabulates_X_S = colTabulates(X_S, na.rm = FALSE), `colTabulates(X, rows, cols)` = colTabulates(X,
+ rows = rows, cols = cols, na.rm = FALSE), `colTabulates(X[rows, cols])` = colTabulates(X[rows,
+ cols], na.rm = FALSE), unit = "ms")
> X <- t(X)
> X_S <- t(X_S)
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5309701 283.6 7916910 422.9 7916910 422.9
Vcells 10220696 78.0 33191153 253.3 53339345 407.0
> rowStats <- microbenchmark(rowTabulates_X_S = rowTabulates(X_S, na.rm = FALSE), `rowTabulates(X, cols, rows)` = rowTabulates(X,
+ rows = cols, cols = rows, na.rm = FALSE), `rowTabulates(X[cols, rows])` = rowTabulates(X[cols,
+ rows], na.rm = FALSE), unit = "ms")
Table: Benchmarking of colTabulates_X_S(), colTabulates(X, rows, cols)() and colTabulates(X[rows, cols])() on 10x1000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | colTabulates(X, rows, cols) | 0.370650 | 0.3817230 | 0.4278961 | 0.3962655 | 0.4416970 | 0.779126 |
| 1 | colTabulates_X_S | 0.356538 | 0.3702745 | 0.4375368 | 0.4087925 | 0.4876355 | 0.755414 |
| 3 | colTabulates(X[rows, cols]) | 0.369907 | 0.3894570 | 0.4410350 | 0.4158055 | 0.4752500 | 0.663778 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | colTabulates(X, rows, cols) | 1.0000000 | 1.0000000 | 1.000000 | 1.000000 | 1.000000 | 1.0000000 |
| 1 | colTabulates_X_S | 0.9619263 | 0.9700084 | 1.022531 | 1.031613 | 1.104005 | 0.9695659 |
| 3 | colTabulates(X[rows, cols]) | 0.9979954 | 1.0202608 | 1.030706 | 1.049310 | 1.075964 | 0.8519521 |
Table: Benchmarking of rowTabulates_X_S(), rowTabulates(X, cols, rows)() and rowTabulates(X[cols, rows])() 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 | rowTabulates_X_S | 0.380852 | 0.3903555 | 0.4295531 | 0.4136260 | 0.4515630 | 0.587730 |
| 2 | rowTabulates(X, cols, rows) | 0.390755 | 0.4066595 | 0.4425441 | 0.4189230 | 0.4496990 | 0.922018 |
| 3 | rowTabulates(X[cols, rows]) | 0.390353 | 0.4055215 | 0.4536265 | 0.4255615 | 0.4983025 | 0.728833 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | rowTabulates_X_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.0000000 | 1.000000 |
| 2 | rowTabulates(X, cols, rows) | 1.026002 | 1.041767 | 1.030243 | 1.012806 | 0.9958721 | 1.568778 |
| 3 | rowTabulates(X[cols, rows]) | 1.024947 | 1.038852 | 1.056043 | 1.028856 | 1.1035060 | 1.240081 |
Figure: Benchmarking of colTabulates_X_S(), colTabulates(X, rows, cols)() and colTabulates(X[rows, cols])() on 10x1000 data as well as rowTabulates_X_S(), rowTabulates(X, cols, rows)() and rowTabulates(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.
Table: Benchmarking of colTabulates_X_S() and rowTabulates_X_S() 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 | colTabulates_X_S | 356.538 | 370.2745 | 437.5369 | 408.7925 | 487.6355 | 755.414 |
| 2 | rowTabulates_X_S | 380.852 | 390.3555 | 429.5531 | 413.6260 | 451.5630 | 587.730 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | colTabulates_X_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.0000000 | 1.0000000 |
| 2 | rowTabulates_X_S | 1.068195 | 1.054233 | 0.981753 | 1.011824 | 0.9260257 | 0.7780237 |
Figure: Benchmarking of colTabulates_X_S() and rowTabulates_X_S() on 10x1000 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.
100x1000 matrix
> X <- data[["100x1000"]]
> rows <- sample.int(nrow(X), size = nrow(X) * 0.7)
> cols <- sample.int(ncol(X), size = ncol(X) * 0.7)
> X_S <- X[rows, cols]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5309923 283.6 7916910 422.9 7916910 422.9
Vcells 10238300 78.2 33191153 253.3 53339345 407.0
> colStats <- microbenchmark(colTabulates_X_S = colTabulates(X_S, na.rm = FALSE), `colTabulates(X, rows, cols)` = colTabulates(X,
+ rows = rows, cols = cols, na.rm = FALSE), `colTabulates(X[rows, cols])` = colTabulates(X[rows,
+ cols], na.rm = FALSE), unit = "ms")
> X <- t(X)
> X_S <- t(X_S)
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5309911 283.6 7916910 422.9 7916910 422.9
Vcells 10288373 78.5 33191153 253.3 53339345 407.0
> rowStats <- microbenchmark(rowTabulates_X_S = rowTabulates(X_S, na.rm = FALSE), `rowTabulates(X, cols, rows)` = rowTabulates(X,
+ rows = cols, cols = rows, na.rm = FALSE), `rowTabulates(X[cols, rows])` = rowTabulates(X[cols,
+ rows], na.rm = FALSE), unit = "ms")
Table: Benchmarking of colTabulates_X_S(), colTabulates(X, rows, cols)() and colTabulates(X[rows, cols])() 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 | colTabulates_X_S | 1.614644 | 1.670087 | 1.809777 | 1.739087 | 1.868170 | 2.792142 |
| 2 | colTabulates(X, rows, cols) | 1.709986 | 1.735956 | 2.042918 | 1.793670 | 2.004110 | 9.707371 |
| 3 | colTabulates(X[rows, cols]) | 1.690566 | 1.731372 | 1.880456 | 1.807530 | 1.983262 | 2.527533 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | colTabulates_X_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.0000000 |
| 2 | colTabulates(X, rows, cols) | 1.059048 | 1.039441 | 1.128823 | 1.031386 | 1.072767 | 3.4766753 |
| 3 | colTabulates(X[rows, cols]) | 1.047021 | 1.036696 | 1.039054 | 1.039355 | 1.061607 | 0.9052308 |
Table: Benchmarking of rowTabulates_X_S(), rowTabulates(X, cols, rows)() and rowTabulates(X[cols, rows])() 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 | rowTabulates_X_S | 2.154210 | 2.189722 | 2.387819 | 2.204312 | 2.278773 | 9.721865 |
| 2 | rowTabulates(X, cols, rows) | 2.250677 | 2.261482 | 2.347641 | 2.301795 | 2.335865 | 3.481036 |
| 3 | rowTabulates(X[cols, rows]) | 2.247985 | 2.261612 | 2.406939 | 2.308937 | 2.462567 | 3.282493 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | rowTabulates_X_S | 1.000000 | 1.000000 | 1.0000000 | 1.000000 | 1.000000 | 1.0000000 |
| 2 | rowTabulates(X, cols, rows) | 1.044781 | 1.032771 | 0.9831739 | 1.044224 | 1.025054 | 0.3580626 |
| 3 | rowTabulates(X[cols, rows]) | 1.043531 | 1.032831 | 1.0080076 | 1.047464 | 1.080655 | 0.3376403 |
Figure: Benchmarking of colTabulates_X_S(), colTabulates(X, rows, cols)() and colTabulates(X[rows, cols])() on 100x1000 data as well as rowTabulates_X_S(), rowTabulates(X, cols, rows)() and rowTabulates(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.
Table: Benchmarking of colTabulates_X_S() and rowTabulates_X_S() 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 | colTabulates_X_S | 1.614644 | 1.670087 | 1.809777 | 1.739087 | 1.868170 | 2.792142 |
| 2 | rowTabulates_X_S | 2.154210 | 2.189722 | 2.387819 | 2.204312 | 2.278773 | 9.721865 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | colTabulates_X_S | 1.00000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 2 | rowTabulates_X_S | 1.33417 | 1.311143 | 1.319399 | 1.267511 | 1.219789 | 3.481866 |
Figure: Benchmarking of colTabulates_X_S() and rowTabulates_X_S() on 100x1000 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.
1000x100 matrix
> X <- data[["1000x100"]]
> rows <- sample.int(nrow(X), size = nrow(X) * 0.7)
> cols <- sample.int(ncol(X), size = ncol(X) * 0.7)
> X_S <- X[rows, cols]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5310142 283.6 7916910 422.9 7916910 422.9
Vcells 10239135 78.2 33191153 253.3 53339345 407.0
> colStats <- microbenchmark(colTabulates_X_S = colTabulates(X_S, na.rm = FALSE), `colTabulates(X, rows, cols)` = colTabulates(X,
+ rows = rows, cols = cols, na.rm = FALSE), `colTabulates(X[rows, cols])` = colTabulates(X[rows,
+ cols], na.rm = FALSE), unit = "ms")
> X <- t(X)
> X_S <- t(X_S)
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5310124 283.6 7916910 422.9 7916910 422.9
Vcells 10289198 78.6 33191153 253.3 53339345 407.0
> rowStats <- microbenchmark(rowTabulates_X_S = rowTabulates(X_S, na.rm = FALSE), `rowTabulates(X, cols, rows)` = rowTabulates(X,
+ rows = cols, cols = rows, na.rm = FALSE), `rowTabulates(X[cols, rows])` = rowTabulates(X[cols,
+ rows], na.rm = FALSE), unit = "ms")
Table: Benchmarking of colTabulates_X_S(), colTabulates(X, rows, cols)() and colTabulates(X[rows, cols])() 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 | colTabulates_X_S | 1.461543 | 1.495653 | 1.590567 | 1.521579 | 1.592432 | 2.555754 |
| 3 | colTabulates(X[rows, cols]) | 1.523949 | 1.557709 | 1.644528 | 1.586893 | 1.639164 | 2.559557 |
| 2 | colTabulates(X, rows, cols) | 1.539389 | 1.560374 | 1.749221 | 1.591442 | 1.769149 | 8.832614 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | colTabulates_X_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 3 | colTabulates(X[rows, cols]) | 1.042699 | 1.041491 | 1.033926 | 1.042925 | 1.029346 | 1.001488 |
| 2 | colTabulates(X, rows, cols) | 1.053263 | 1.043273 | 1.099747 | 1.045915 | 1.110973 | 3.455972 |
Table: Benchmarking of rowTabulates_X_S(), rowTabulates(X, cols, rows)() and rowTabulates(X[cols, rows])() 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 | rowTabulates_X_S | 2.219772 | 2.260161 | 2.401932 | 2.300524 | 2.312773 | 8.983898 |
| 2 | rowTabulates(X, cols, rows) | 2.310645 | 2.325799 | 2.437679 | 2.370543 | 2.398989 | 3.921769 |
| 3 | rowTabulates(X[cols, rows]) | 2.311351 | 2.326789 | 2.413981 | 2.373002 | 2.388675 | 3.713551 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | rowTabulates_X_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.0000000 |
| 2 | rowTabulates(X, cols, rows) | 1.040938 | 1.029041 | 1.014882 | 1.030436 | 1.037278 | 0.4365331 |
| 3 | rowTabulates(X[cols, rows]) | 1.041256 | 1.029479 | 1.005016 | 1.031505 | 1.032819 | 0.4133563 |
Figure: Benchmarking of colTabulates_X_S(), colTabulates(X, rows, cols)() and colTabulates(X[rows, cols])() on 1000x100 data as well as rowTabulates_X_S(), rowTabulates(X, cols, rows)() and rowTabulates(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.
Table: Benchmarking of colTabulates_X_S() and rowTabulates_X_S() 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 | colTabulates_X_S | 1.461543 | 1.495653 | 1.590567 | 1.521579 | 1.592432 | 2.555754 |
| 2 | rowTabulates_X_S | 2.219772 | 2.260161 | 2.401932 | 2.300524 | 2.312773 | 8.983898 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | colTabulates_X_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 2 | rowTabulates_X_S | 1.518787 | 1.511152 | 1.510111 | 1.511932 | 1.452352 | 3.515165 |
Figure: Benchmarking of colTabulates_X_S() and rowTabulates_X_S() on 1000x100 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.
Appendix
Session information
R version 4.1.1 Patched (2021-08-10 r80727)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.5 LTS
Matrix products: default
BLAS: /home/hb/software/R-devel/R-4-1-branch/lib/R/lib/libRblas.so
LAPACK: /home/hb/software/R-devel/R-4-1-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_1.4-7 matrixStats_0.60.0 ggplot2_3.3.5
[4] knitr_1.33 R.devices_2.17.0 R.utils_2.10.1
[7] R.oo_1.24.0 R.methodsS3_1.8.1-9001 history_0.0.1-9000
loaded via a namespace (and not attached):
[1] Biobase_2.52.0 httr_1.4.2 splines_4.1.1
[4] bit64_4.0.5 network_1.17.1 assertthat_0.2.1
[7] highr_0.9 stats4_4.1.1 blob_1.2.2
[10] GenomeInfoDbData_1.2.6 robustbase_0.93-8 pillar_1.6.2
[13] RSQLite_2.2.8 lattice_0.20-44 glue_1.4.2
[16] digest_0.6.27 XVector_0.32.0 colorspace_2.0-2
[19] Matrix_1.3-4 XML_3.99-0.7 pkgconfig_2.0.3
[22] zlibbioc_1.38.0 genefilter_1.74.0 purrr_0.3.4
[25] ergm_4.1.2 xtable_1.8-4 scales_1.1.1
[28] tibble_3.1.4 annotate_1.70.0 KEGGREST_1.32.0
[31] farver_2.1.0 generics_0.1.0 IRanges_2.26.0
[34] ellipsis_0.3.2 cachem_1.0.6 withr_2.4.2
[37] BiocGenerics_0.38.0 mime_0.11 survival_3.2-13
[40] magrittr_2.0.1 crayon_1.4.1 statnet.common_4.5.0
[43] memoise_2.0.0 laeken_0.5.1 fansi_0.5.0
[46] R.cache_0.15.0 MASS_7.3-54 R.rsp_0.44.0
[49] progressr_0.8.0 tools_4.1.1 lifecycle_1.0.0
[52] S4Vectors_0.30.0 trust_0.1-8 munsell_0.5.0
[55] tabby_0.0.1-9001 AnnotationDbi_1.54.1 Biostrings_2.60.2
[58] compiler_4.1.1 GenomeInfoDb_1.28.1 rlang_0.4.11
[61] grid_4.1.1 RCurl_1.98-1.4 cwhmisc_6.6
[64] rstudioapi_0.13 rappdirs_0.3.3 startup_0.15.0
[67] labeling_0.4.2 bitops_1.0-7 base64enc_0.1-3
[70] boot_1.3-28 gtable_0.3.0 DBI_1.1.1
[73] markdown_1.1 R6_2.5.1 lpSolveAPI_5.5.2.0-17.7
[76] rle_0.9.2 dplyr_1.0.7 fastmap_1.1.0
[79] bit_4.0.4 utf8_1.2.2 parallel_4.1.1
[82] Rcpp_1.0.7 vctrs_0.3.8 png_0.1-7
[85] DEoptimR_1.0-9 tidyselect_1.1.1 xfun_0.25
[88] coda_0.19-4
Total processing time was 15.24 secs.
Reproducibility
To reproduce this report, do:
html <- matrixStats:::benchmark('colRowTabulates_subset')
Copyright Dongcan Jiang. Last updated on 2021-08-25 22:30:18 (+0200 UTC). Powered by RSP.