matrixStats.benchmarks

---

allocMatrix() benchmarks

This report benchmark the performance of allocMatrix() against alternative methods.

Alternative methods

• matrix()
• matrix() special trick for NA

where

> allocMatrix_R <- function(nrow, ncol, value = NA) {
+     if (is.na(value) && !is.nan(value)) {
+         matrix(data = value[c()], nrow = nrow, ncol = ncol)
+     }     else {
+         matrix(data = value, nrow = nrow, ncol = ncol)
+     }
+ }

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)
> values <- list(zero = 0, one = 1, `NA` = NA_real_)
> if (mode != "double") 
+     values <- lapply(values, FUN = function(x) {
+         storage.mode(x) <- mode
+         x
+     })

Results

10x10 matrix

> dim <- dim(data[["10x10"]])
> nrow <- dim[1L]
> ncol <- dim[2L]
> str(value)
 int 0

> stats <- microbenchmark(allocMatrix = allocMatrix(nrow = nrow, ncol = ncol, value = value), matrix = matrix(data = value, 
+     nrow = nrow, ncol = ncol), allocMatrix_R = allocMatrix_R(nrow = nrow, ncol = ncol, value = value), 
+     unit = "ms")

Table: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on integer+10x10+0 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	allocMatrix	0.001403	0.0017410	0.0025630	0.0018325	0.0019330	0.072118
2	matrix	0.002036	0.0022760	0.0024602	0.0023705	0.0025020	0.006763
3	allocMatrix_R	0.003288	0.0034535	0.0037983	0.0035560	0.0036915	0.023385

	expr	min	lq	mean	median	uq	max
1	allocMatrix	1.000000	1.000000	1.0000000	1.000000	1.000000	1.0000000
2	matrix	1.451176	1.307295	0.9599062	1.293588	1.294361	0.0937769
3	allocMatrix_R	2.343549	1.983630	1.4819644	1.940518	1.909726	0.3242602

Figure: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on integer+10x10+0 data. Outliers are displayed as crosses. Times are in milliseconds.

> dim <- dim(data[["10x10"]])
> nrow <- dim[1L]
> ncol <- dim[2L]
> str(value)
 int 1

> stats <- microbenchmark(allocMatrix = allocMatrix(nrow = nrow, ncol = ncol, value = value), matrix = matrix(data = value, 
+     nrow = nrow, ncol = ncol), allocMatrix_R = allocMatrix_R(nrow = nrow, ncol = ncol, value = value), 
+     unit = "ms")

Table: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on integer+10x10+1 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	allocMatrix	0.001446	0.0016315	0.0019682	0.0017600	0.0019810	0.009461
2	matrix	0.001969	0.0021310	0.0023482	0.0022585	0.0023890	0.004058
3	allocMatrix_R	0.003129	0.0033500	0.0037179	0.0034805	0.0036485	0.018515

	expr	min	lq	mean	median	uq	max
1	allocMatrix	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	matrix	1.361687	1.306160	1.193019	1.283239	1.205957	0.4289187
3	allocMatrix_R	2.163900	2.053325	1.888932	1.977557	1.841747	1.9569813

Figure: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on integer+10x10+1 data. Outliers are displayed as crosses. Times are in milliseconds.

> dim <- dim(data[["10x10"]])
> nrow <- dim[1L]
> ncol <- dim[2L]
> str(value)
 int NA

> stats <- microbenchmark(allocMatrix = allocMatrix(nrow = nrow, ncol = ncol, value = value), matrix = matrix(data = value, 
+     nrow = nrow, ncol = ncol), allocMatrix_R = allocMatrix_R(nrow = nrow, ncol = ncol, value = value), 
+     unit = "ms")

Table: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on integer+10x10+NA data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	allocMatrix	0.001471	0.0016980	0.0020451	0.001829	0.0019790	0.015731
2	matrix	0.001967	0.0021820	0.0024582	0.002320	0.0025265	0.004353
3	allocMatrix_R	0.003835	0.0041085	0.0045412	0.004230	0.0044360	0.023360

	expr	min	lq	mean	median	uq	max
1	allocMatrix	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	matrix	1.337186	1.285041	1.201974	1.268453	1.276655	0.2767148
3	allocMatrix_R	2.607070	2.419611	2.220526	2.312739	2.241536	1.4849660

Figure: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on integer+10x10+NA data. Outliers are displayed as crosses. Times are in milliseconds.

100x100 matrix

> dim <- dim(data[["100x100"]])
> nrow <- dim[1L]
> ncol <- dim[2L]
> str(value)
 int 0

> stats <- microbenchmark(allocMatrix = allocMatrix(nrow = nrow, ncol = ncol, value = value), matrix = matrix(data = value, 
+     nrow = nrow, ncol = ncol), allocMatrix_R = allocMatrix_R(nrow = nrow, ncol = ncol, value = value), 
+     unit = "ms")

Table: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on integer+100x100+0 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	allocMatrix	0.003576	0.0039695	0.0045059	0.0042055	0.0045370	0.012580
2	matrix	0.007398	0.0079255	0.0083078	0.0081305	0.0085765	0.010790
3	allocMatrix_R	0.008799	0.0093135	0.0099239	0.0095415	0.0099060	0.025206

	expr	min	lq	mean	median	uq	max
1	allocMatrix	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	matrix	2.068792	1.996599	1.843743	1.933302	1.890346	0.8577107
3	allocMatrix_R	2.460571	2.346265	2.202407	2.268815	2.183381	2.0036566

Figure: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on integer+100x100+0 data. Outliers are displayed as crosses. Times are in milliseconds.

> dim <- dim(data[["100x100"]])
> nrow <- dim[1L]
> ncol <- dim[2L]
> str(value)
 int 1

> stats <- microbenchmark(allocMatrix = allocMatrix(nrow = nrow, ncol = ncol, value = value), matrix = matrix(data = value, 
+     nrow = nrow, ncol = ncol), allocMatrix_R = allocMatrix_R(nrow = nrow, ncol = ncol, value = value), 
+     unit = "ms")

Table: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on integer+100x100+1 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	allocMatrix	0.006719	0.0075425	0.0078959	0.0077560	0.0079415	0.016258
2	matrix	0.007251	0.0079055	0.0084173	0.0081805	0.0084405	0.018963
3	allocMatrix_R	0.008745	0.0092130	0.0099495	0.0095025	0.0098535	0.040503

	expr	min	lq	mean	median	uq	max
1	allocMatrix	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	matrix	1.079178	1.048127	1.066030	1.054732	1.062834	1.166380
3	allocMatrix_R	1.301533	1.221478	1.260074	1.225181	1.240761	2.491266

Figure: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on integer+100x100+1 data. Outliers are displayed as crosses. Times are in milliseconds.

> dim <- dim(data[["100x100"]])
> nrow <- dim[1L]
> ncol <- dim[2L]
> str(value)
 int NA

> stats <- microbenchmark(allocMatrix = allocMatrix(nrow = nrow, ncol = ncol, value = value), matrix = matrix(data = value, 
+     nrow = nrow, ncol = ncol), allocMatrix_R = allocMatrix_R(nrow = nrow, ncol = ncol, value = value), 
+     unit = "ms")

Table: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on integer+100x100+NA data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	allocMatrix	0.006866	0.0074865	0.0078684	0.007757	0.0080165	0.015277
2	matrix	0.007395	0.0080830	0.0085057	0.008315	0.0085690	0.016088
3	allocMatrix_R	0.012749	0.0132485	0.0140350	0.013534	0.0137910	0.045187

	expr	min	lq	mean	median	uq	max
1	allocMatrix	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	matrix	1.077046	1.079677	1.080999	1.071935	1.068920	1.053086
3	allocMatrix_R	1.856831	1.769652	1.783719	1.744747	1.720327	2.957845

Figure: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on integer+100x100+NA data. Outliers are displayed as crosses. Times are in milliseconds.

1000x10 matrix

> dim <- dim(data[["1000x10"]])
> nrow <- dim[1L]
> ncol <- dim[2L]
> str(value)
 int 0

> stats <- microbenchmark(allocMatrix = allocMatrix(nrow = nrow, ncol = ncol, value = value), matrix = matrix(data = value, 
+     nrow = nrow, ncol = ncol), allocMatrix_R = allocMatrix_R(nrow = nrow, ncol = ncol, value = value), 
+     unit = "ms")

Table: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on integer+1000x10+0 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	allocMatrix	0.003611	0.0038825	0.0042124	0.0040705	0.0042785	0.012207
2	matrix	0.007483	0.0078825	0.0083134	0.0080945	0.0082940	0.017569
3	allocMatrix_R	0.008763	0.0091745	0.0095907	0.0094425	0.0097640	0.014220

	expr	min	lq	mean	median	uq	max
1	allocMatrix	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	matrix	2.072279	2.030264	1.973556	1.988576	1.938530	1.439256
3	allocMatrix_R	2.426752	2.363039	2.276787	2.319740	2.282108	1.164905

Figure: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on integer+1000x10+0 data. Outliers are displayed as crosses. Times are in milliseconds.

> dim <- dim(data[["1000x10"]])
> nrow <- dim[1L]
> ncol <- dim[2L]
> str(value)
 int 1

> stats <- microbenchmark(allocMatrix = allocMatrix(nrow = nrow, ncol = ncol, value = value), matrix = matrix(data = value, 
+     nrow = nrow, ncol = ncol), allocMatrix_R = allocMatrix_R(nrow = nrow, ncol = ncol, value = value), 
+     unit = "ms")

Table: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on integer+1000x10+1 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	allocMatrix	0.007095	0.007520	0.0082978	0.0077645	0.0080380	0.038775
2	matrix	0.007393	0.008021	0.0083291	0.0082040	0.0084505	0.012494
3	allocMatrix_R	0.008867	0.009381	0.0097328	0.0096045	0.0098520	0.014879

	expr	min	lq	mean	median	uq	max
1	allocMatrix	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	matrix	1.042001	1.066622	1.003778	1.056604	1.051319	0.3222179
3	allocMatrix_R	1.249753	1.247473	1.172942	1.236976	1.225678	0.3837266

Figure: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on integer+1000x10+1 data. Outliers are displayed as crosses. Times are in milliseconds.

> dim <- dim(data[["1000x10"]])
> nrow <- dim[1L]
> ncol <- dim[2L]
> str(value)
 int NA

> stats <- microbenchmark(allocMatrix = allocMatrix(nrow = nrow, ncol = ncol, value = value), matrix = matrix(data = value, 
+     nrow = nrow, ncol = ncol), allocMatrix_R = allocMatrix_R(nrow = nrow, ncol = ncol, value = value), 
+     unit = "ms")

Table: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on integer+1000x10+NA data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	allocMatrix	0.006895	0.0075045	0.0078747	0.0077585	0.0079630	0.017942
2	matrix	0.007323	0.0080470	0.0084581	0.0083185	0.0085965	0.017017
3	allocMatrix_R	0.012768	0.0132595	0.0140650	0.0135825	0.0138795	0.044637

	expr	min	lq	mean	median	uq	max
1	allocMatrix	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	matrix	1.062074	1.072290	1.074079	1.072179	1.079555	0.948445
3	allocMatrix_R	1.851777	1.766873	1.786094	1.750661	1.742999	2.487850

Figure: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on integer+1000x10+NA data. Outliers are displayed as crosses. Times are in milliseconds.

10x1000 matrix

> dim <- dim(data[["10x1000"]])
> nrow <- dim[1L]
> ncol <- dim[2L]
> str(value)
 int 0

> stats <- microbenchmark(allocMatrix = allocMatrix(nrow = nrow, ncol = ncol, value = value), matrix = matrix(data = value, 
+     nrow = nrow, ncol = ncol), allocMatrix_R = allocMatrix_R(nrow = nrow, ncol = ncol, value = value), 
+     unit = "ms")

Table: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on integer+10x1000+0 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	allocMatrix	0.003541	0.0037765	0.0043114	0.0040250	0.0043450	0.017992
2	matrix	0.007560	0.0078285	0.0081639	0.0080555	0.0082505	0.013302
3	allocMatrix_R	0.008847	0.0092205	0.0096187	0.0094275	0.0097150	0.018680

	expr	min	lq	mean	median	uq	max
1	allocMatrix	1.000000	1.000000	1.00000	1.000000	1.000000	1.0000000
2	matrix	2.134990	2.072951	1.89357	2.001367	1.898849	0.7393286
3	allocMatrix_R	2.498447	2.441546	2.23099	2.342236	2.235903	1.0382392

Figure: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on integer+10x1000+0 data. Outliers are displayed as crosses. Times are in milliseconds.

> dim <- dim(data[["10x1000"]])
> nrow <- dim[1L]
> ncol <- dim[2L]
> str(value)
 int 1

> stats <- microbenchmark(allocMatrix = allocMatrix(nrow = nrow, ncol = ncol, value = value), matrix = matrix(data = value, 
+     nrow = nrow, ncol = ncol), allocMatrix_R = allocMatrix_R(nrow = nrow, ncol = ncol, value = value), 
+     unit = "ms")

Table: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on integer+10x1000+1 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	allocMatrix	0.007187	0.0076485	0.0082299	0.0078915	0.0081625	0.020956
2	matrix	0.007642	0.0080320	0.0087296	0.0082480	0.0084695	0.050538
3	allocMatrix_R	0.008751	0.0094105	0.0099620	0.0096840	0.0099165	0.025677

	expr	min	lq	mean	median	uq	max
1	allocMatrix	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	matrix	1.063309	1.050141	1.060715	1.045175	1.037611	2.411624
3	allocMatrix_R	1.217615	1.230372	1.210463	1.227143	1.214885	1.225281

Figure: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on integer+10x1000+1 data. Outliers are displayed as crosses. Times are in milliseconds.

> dim <- dim(data[["10x1000"]])
> nrow <- dim[1L]
> ncol <- dim[2L]
> str(value)
 int NA

> stats <- microbenchmark(allocMatrix = allocMatrix(nrow = nrow, ncol = ncol, value = value), matrix = matrix(data = value, 
+     nrow = nrow, ncol = ncol), allocMatrix_R = allocMatrix_R(nrow = nrow, ncol = ncol, value = value), 
+     unit = "ms")

Table: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on integer+10x1000+NA data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	allocMatrix	0.006890	0.0075445	0.0079180	0.0077800	0.0080290	0.015981
2	matrix	0.007755	0.0080400	0.0087644	0.0083505	0.0086255	0.039486
3	allocMatrix_R	0.012888	0.0131770	0.0137029	0.0134680	0.0137755	0.023706

	expr	min	lq	mean	median	uq	max
1	allocMatrix	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	matrix	1.125544	1.065677	1.106885	1.073329	1.074293	2.470809
3	allocMatrix_R	1.870537	1.746570	1.730592	1.731105	1.715718	1.483386

Figure: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on integer+10x1000+NA data. Outliers are displayed as crosses. Times are in milliseconds.

100x1000 matrix

> dim <- dim(data[["100x1000"]])
> nrow <- dim[1L]
> ncol <- dim[2L]
> str(value)
 int 0

> stats <- microbenchmark(allocMatrix = allocMatrix(nrow = nrow, ncol = ncol, value = value), matrix = matrix(data = value, 
+     nrow = nrow, ncol = ncol), allocMatrix_R = allocMatrix_R(nrow = nrow, ncol = ncol, value = value), 
+     unit = "ms")

Table: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on integer+100x1000+0 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	allocMatrix	0.018588	0.0212970	0.0887237	0.0244470	0.1742655	0.230347
3	allocMatrix_R	0.050364	0.0539575	0.1799226	0.1763815	0.1932760	5.231037
2	matrix	0.049377	0.0586030	0.1990730	0.1793560	0.1956190	5.924084

	expr	min	lq	mean	median	uq	max
1	allocMatrix	1.000000	1.000000	1.000000	1.000000	1.000000	1.00000
3	allocMatrix_R	2.709490	2.533573	2.027897	7.214853	1.109089	22.70938
2	matrix	2.656391	2.751702	2.243740	7.336524	1.122534	25.71809

Figure: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on integer+100x1000+0 data. Outliers are displayed as crosses. Times are in milliseconds.

> dim <- dim(data[["100x1000"]])
> nrow <- dim[1L]
> ncol <- dim[2L]
> str(value)
 int 1

> stats <- microbenchmark(allocMatrix = allocMatrix(nrow = nrow, ncol = ncol, value = value), matrix = matrix(data = value, 
+     nrow = nrow, ncol = ncol), allocMatrix_R = allocMatrix_R(nrow = nrow, ncol = ncol, value = value), 
+     unit = "ms")

Table: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on integer+100x1000+1 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
2	matrix	0.046162	0.0548485	0.1755037	0.0620535	0.1851520	5.776891
1	allocMatrix	0.047385	0.0543030	0.1279802	0.1727480	0.1899695	0.211541
3	allocMatrix_R	0.047786	0.0563875	0.1830705	0.1788390	0.1946830	5.198910

	expr	min	lq	mean	median	uq	max
2	matrix	1.000000	1.0000000	1.0000000	1.000000	1.000000	1.0000000
1	allocMatrix	1.026494	0.9900544	0.7292163	2.783856	1.026019	0.0366185
3	allocMatrix_R	1.035181	1.0280591	1.0431146	2.882013	1.051477	0.8999495

Figure: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on integer+100x1000+1 data. Outliers are displayed as crosses. Times are in milliseconds.

> dim <- dim(data[["100x1000"]])
> nrow <- dim[1L]
> ncol <- dim[2L]
> str(value)
 int NA

> stats <- microbenchmark(allocMatrix = allocMatrix(nrow = nrow, ncol = ncol, value = value), matrix = matrix(data = value, 
+     nrow = nrow, ncol = ncol), allocMatrix_R = allocMatrix_R(nrow = nrow, ncol = ncol, value = value), 
+     unit = "ms")

Table: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on integer+100x1000+NA data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	allocMatrix	0.046800	0.0523670	0.1693497	0.1693800	0.1774470	5.119695
2	matrix	0.047647	0.0514110	0.1202445	0.1713940	0.1771640	0.208192
3	allocMatrix_R	0.077437	0.0791445	0.2072741	0.2026275	0.2152215	5.639855

	expr	min	lq	mean	median	uq	max
1	allocMatrix	1.000000	1.0000000	1.0000000	1.000000	1.0000000	1.0000000
2	matrix	1.018098	0.9817442	0.7100363	1.011890	0.9984052	0.0406649
3	allocMatrix_R	1.654637	1.5113430	1.2239409	1.196289	1.2128776	1.1015998

Figure: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on integer+100x1000+NA data. Outliers are displayed as crosses. Times are in milliseconds.

1000x100 matrix

> dim <- dim(data[["1000x100"]])
> nrow <- dim[1L]
> ncol <- dim[2L]
> str(value)
 int 0

> stats <- microbenchmark(allocMatrix = allocMatrix(nrow = nrow, ncol = ncol, value = value), matrix = matrix(data = value, 
+     nrow = nrow, ncol = ncol), allocMatrix_R = allocMatrix_R(nrow = nrow, ncol = ncol, value = value), 
+     unit = "ms")

Table: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on integer+1000x100+0 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
2	matrix	0.048304	0.0548940	0.1791273	0.124591	0.1859915	5.872833
1	allocMatrix	0.017830	0.0217105	0.1665848	0.167592	0.1832160	5.295121
3	allocMatrix_R	0.049379	0.0530535	0.1238245	0.173495	0.1861995	0.227690

	expr	min	lq	mean	median	uq	max
2	matrix	1.0000000	1.0000000	1.0000000	1.000000	1.0000000	1.0000000
1	allocMatrix	0.3691206	0.3954986	0.9299801	1.345137	0.9850773	0.9016298
3	allocMatrix_R	1.0222549	0.9664717	0.6912653	1.392516	1.0011183	0.0387700

Figure: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on integer+1000x100+0 data. Outliers are displayed as crosses. Times are in milliseconds.

> dim <- dim(data[["1000x100"]])
> nrow <- dim[1L]
> ncol <- dim[2L]
> str(value)
 int 1

> stats <- microbenchmark(allocMatrix = allocMatrix(nrow = nrow, ncol = ncol, value = value), matrix = matrix(data = value, 
+     nrow = nrow, ncol = ncol), allocMatrix_R = allocMatrix_R(nrow = nrow, ncol = ncol, value = value), 
+     unit = "ms")

Table: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on integer+1000x100+1 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
3	allocMatrix_R	0.049098	0.052814	0.1665685	0.0629710	0.1869610	5.567611
1	allocMatrix	0.046998	0.055218	0.1815716	0.1750530	0.1883275	5.600804
2	matrix	0.047537	0.055501	0.1361180	0.1752905	0.1868880	0.240629

	expr	min	lq	mean	median	uq	max
3	allocMatrix_R	1.0000000	1.000000	1.0000000	1.000000	1.0000000	1.0000000
1	allocMatrix	0.9572284	1.045518	1.0900720	2.779899	1.0073090	1.0059618
2	matrix	0.9682064	1.050877	0.8171896	2.783670	0.9996095	0.0432194

Figure: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on integer+1000x100+1 data. Outliers are displayed as crosses. Times are in milliseconds.

> dim <- dim(data[["1000x100"]])
> nrow <- dim[1L]
> ncol <- dim[2L]
> str(value)
 int NA

> stats <- microbenchmark(allocMatrix = allocMatrix(nrow = nrow, ncol = ncol, value = value), matrix = matrix(data = value, 
+     nrow = nrow, ncol = ncol), allocMatrix_R = allocMatrix_R(nrow = nrow, ncol = ncol, value = value), 
+     unit = "ms")

Table: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on integer+1000x100+NA data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
3	allocMatrix_R	0.074961	0.0823790	0.1963554	0.102550	0.2160745	4.985538
1	allocMatrix	0.046138	0.0512700	0.1184074	0.158090	0.1785630	0.206023
2	matrix	0.046679	0.0532165	0.1822956	0.171527	0.1791335	5.606220

	expr	min	lq	mean	median	uq	max
3	allocMatrix_R	1.0000000	1.0000000	1.0000000	1.000000	1.0000000	1.0000000
1	allocMatrix	0.6154934	0.6223674	0.6030259	1.541589	0.8263955	0.0413241
2	matrix	0.6227105	0.6459959	0.9283963	1.672618	0.8290358	1.1244965

Figure: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on integer+1000x100+NA data. 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)
> values <- list(zero = 0, one = 1, `NA` = NA_real_)
> if (mode != "double") 
+     values <- lapply(values, FUN = function(x) {
+         storage.mode(x) <- mode
+         x
+     })

Results

10x10 matrix

> dim <- dim(data[["10x10"]])
> nrow <- dim[1L]
> ncol <- dim[2L]
> str(value)
 num 0

> stats <- microbenchmark(allocMatrix = allocMatrix(nrow = nrow, ncol = ncol, value = value), matrix = matrix(data = value, 
+     nrow = nrow, ncol = ncol), allocMatrix_R = allocMatrix_R(nrow = nrow, ncol = ncol, value = value), 
+     unit = "ms")

Table: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on double+10x10+0 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	allocMatrix	0.001422	0.0017830	0.0019980	0.001896	0.0019935	0.010360
2	matrix	0.002060	0.0022980	0.0024078	0.002371	0.0024810	0.003756
3	allocMatrix_R	0.003328	0.0034845	0.0037590	0.003573	0.0036995	0.018480

	expr	min	lq	mean	median	uq	max
1	allocMatrix	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	matrix	1.448664	1.288839	1.205148	1.250527	1.244545	0.3625483
3	allocMatrix_R	2.340366	1.954290	1.881435	1.884494	1.855781	1.7837838

Figure: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on double+10x10+0 data. Outliers are displayed as crosses. Times are in milliseconds.

> dim <- dim(data[["10x10"]])
> nrow <- dim[1L]
> ncol <- dim[2L]
> str(value)
 num 1

> stats <- microbenchmark(allocMatrix = allocMatrix(nrow = nrow, ncol = ncol, value = value), matrix = matrix(data = value, 
+     nrow = nrow, ncol = ncol), allocMatrix_R = allocMatrix_R(nrow = nrow, ncol = ncol, value = value), 
+     unit = "ms")

Table: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on double+10x10+1 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	allocMatrix	0.001592	0.0019120	0.0020817	0.0020150	0.0020940	0.009752
2	matrix	0.002261	0.0023825	0.0024993	0.0024835	0.0025745	0.003307
3	allocMatrix_R	0.003372	0.0036555	0.0039053	0.0037490	0.0038535	0.016026

	expr	min	lq	mean	median	uq	max
1	allocMatrix	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	matrix	1.420226	1.246077	1.200592	1.232506	1.229465	0.3391099
3	allocMatrix_R	2.118091	1.911872	1.876014	1.860546	1.840258	1.6433552

Figure: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on double+10x10+1 data. Outliers are displayed as crosses. Times are in milliseconds.

> dim <- dim(data[["10x10"]])
> nrow <- dim[1L]
> ncol <- dim[2L]
> str(value)
 num NA

> stats <- microbenchmark(allocMatrix = allocMatrix(nrow = nrow, ncol = ncol, value = value), matrix = matrix(data = value, 
+     nrow = nrow, ncol = ncol), allocMatrix_R = allocMatrix_R(nrow = nrow, ncol = ncol, value = value), 
+     unit = "ms")

Table: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on double+10x10+NA data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	allocMatrix	0.001488	0.0018585	0.0021182	0.002003	0.0021005	0.015879
2	matrix	0.002147	0.0023665	0.0024914	0.002446	0.0025690	0.004051
3	allocMatrix_R	0.003860	0.0042415	0.0045491	0.004323	0.0044880	0.018544

	expr	min	lq	mean	median	uq	max
1	allocMatrix	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	matrix	1.442876	1.273339	1.176181	1.221168	1.223042	0.2551168
3	allocMatrix_R	2.594086	2.282217	2.147605	2.158263	2.136634	1.1678317

Figure: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on double+10x10+NA data. Outliers are displayed as crosses. Times are in milliseconds.

100x100 matrix

> dim <- dim(data[["100x100"]])
> nrow <- dim[1L]
> ncol <- dim[2L]
> str(value)
 num 0

> stats <- microbenchmark(allocMatrix = allocMatrix(nrow = nrow, ncol = ncol, value = value), matrix = matrix(data = value, 
+     nrow = nrow, ncol = ncol), allocMatrix_R = allocMatrix_R(nrow = nrow, ncol = ncol, value = value), 
+     unit = "ms")

Table: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on double+100x100+0 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	allocMatrix	0.005604	0.0059770	0.0065320	0.0063100	0.0067715	0.015798
2	matrix	0.009098	0.0100055	0.0117181	0.0116895	0.0125010	0.042903
3	allocMatrix_R	0.010554	0.0116210	0.0135207	0.0134895	0.0142200	0.029162

	expr	min	lq	mean	median	uq	max
1	allocMatrix	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	matrix	1.623483	1.674000	1.793951	1.852536	1.846120	2.715724
3	allocMatrix_R	1.883298	1.944286	2.069925	2.137797	2.099978	1.845930

Figure: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on double+100x100+0 data. Outliers are displayed as crosses. Times are in milliseconds.

> dim <- dim(data[["100x100"]])
> nrow <- dim[1L]
> ncol <- dim[2L]
> str(value)
 num 1

> stats <- microbenchmark(allocMatrix = allocMatrix(nrow = nrow, ncol = ncol, value = value), matrix = matrix(data = value, 
+     nrow = nrow, ncol = ncol), allocMatrix_R = allocMatrix_R(nrow = nrow, ncol = ncol, value = value), 
+     unit = "ms")

Table: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on double+100x100+1 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	allocMatrix	0.008723	0.0096925	0.0117330	0.0123575	0.0130595	0.018485
2	matrix	0.009023	0.0098670	0.0119625	0.0128210	0.0133420	0.021682
3	allocMatrix_R	0.010491	0.0114655	0.0138768	0.0144455	0.0150490	0.042964

	expr	min	lq	mean	median	uq	max
1	allocMatrix	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	matrix	1.034392	1.018004	1.019562	1.037508	1.021632	1.172951
3	allocMatrix_R	1.202683	1.182925	1.182717	1.168966	1.152341	2.324263

Figure: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on double+100x100+1 data. Outliers are displayed as crosses. Times are in milliseconds.

> dim <- dim(data[["100x100"]])
> nrow <- dim[1L]
> ncol <- dim[2L]
> str(value)
 num NA

> stats <- microbenchmark(allocMatrix = allocMatrix(nrow = nrow, ncol = ncol, value = value), matrix = matrix(data = value, 
+     nrow = nrow, ncol = ncol), allocMatrix_R = allocMatrix_R(nrow = nrow, ncol = ncol, value = value), 
+     unit = "ms")

Table: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on double+100x100+NA data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	allocMatrix	0.008616	0.0096750	0.0116776	0.0122705	0.0129730	0.018305
2	matrix	0.009147	0.0099050	0.0119369	0.0128340	0.0134750	0.018859
3	allocMatrix_R	0.013078	0.0138215	0.0157661	0.0148645	0.0159485	0.046917

	expr	min	lq	mean	median	uq	max
1	allocMatrix	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	matrix	1.061630	1.023773	1.022210	1.045923	1.038696	1.030265
3	allocMatrix_R	1.517874	1.428579	1.350117	1.211401	1.229361	2.563070

Figure: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on double+100x100+NA data. Outliers are displayed as crosses. Times are in milliseconds.

1000x10 matrix

> dim <- dim(data[["1000x10"]])
> nrow <- dim[1L]
> ncol <- dim[2L]
> str(value)
 num 0

> stats <- microbenchmark(allocMatrix = allocMatrix(nrow = nrow, ncol = ncol, value = value), matrix = matrix(data = value, 
+     nrow = nrow, ncol = ncol), allocMatrix_R = allocMatrix_R(nrow = nrow, ncol = ncol, value = value), 
+     unit = "ms")

Table: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on double+1000x10+0 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	allocMatrix	0.005495	0.0059335	0.0065574	0.0062295	0.0067820	0.014168
2	matrix	0.009270	0.0109655	0.0118771	0.0121205	0.0126560	0.021973
3	allocMatrix_R	0.010485	0.0117560	0.0135457	0.0136655	0.0143055	0.046155

	expr	min	lq	mean	median	uq	max
1	allocMatrix	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	matrix	1.686988	1.848066	1.811246	1.945662	1.866116	1.550889
3	allocMatrix_R	1.908098	1.981293	2.065700	2.193675	2.109333	3.257693

Figure: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on double+1000x10+0 data. Outliers are displayed as crosses. Times are in milliseconds.

> dim <- dim(data[["1000x10"]])
> nrow <- dim[1L]
> ncol <- dim[2L]
> str(value)
 num 1

> stats <- microbenchmark(allocMatrix = allocMatrix(nrow = nrow, ncol = ncol, value = value), matrix = matrix(data = value, 
+     nrow = nrow, ncol = ncol), allocMatrix_R = allocMatrix_R(nrow = nrow, ncol = ncol, value = value), 
+     unit = "ms")

Table: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on double+1000x10+1 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
2	matrix	0.009148	0.0097495	0.0115173	0.0105080	0.013336	0.018449
1	allocMatrix	0.008478	0.0093105	0.0115904	0.0111815	0.012600	0.043249
3	allocMatrix_R	0.010590	0.0111770	0.0130357	0.0120745	0.014786	0.018884

	expr	min	lq	mean	median	uq	max
2	matrix	1.0000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	allocMatrix	0.9267599	0.954972	1.006352	1.064094	0.944811	2.344246
3	allocMatrix_R	1.1576301	1.146418	1.131838	1.149077	1.108728	1.023578

Figure: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on double+1000x10+1 data. Outliers are displayed as crosses. Times are in milliseconds.

> dim <- dim(data[["1000x10"]])
> nrow <- dim[1L]
> ncol <- dim[2L]
> str(value)
 num NA

> stats <- microbenchmark(allocMatrix = allocMatrix(nrow = nrow, ncol = ncol, value = value), matrix = matrix(data = value, 
+     nrow = nrow, ncol = ncol), allocMatrix_R = allocMatrix_R(nrow = nrow, ncol = ncol, value = value), 
+     unit = "ms")

Table: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on double+1000x10+NA data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	allocMatrix	0.008061	0.008955	0.0097751	0.009332	0.0100405	0.018595
2	matrix	0.008657	0.009389	0.0101506	0.009758	0.0101795	0.027767
3	allocMatrix_R	0.011833	0.012960	0.0141847	0.013234	0.0138375	0.034754

	expr	min	lq	mean	median	uq	max
1	allocMatrix	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	matrix	1.073936	1.048464	1.038418	1.045649	1.013844	1.493251
3	allocMatrix_R	1.467932	1.447236	1.451109	1.418131	1.378168	1.868997

Figure: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on double+1000x10+NA data. Outliers are displayed as crosses. Times are in milliseconds.

10x1000 matrix

> dim <- dim(data[["10x1000"]])
> nrow <- dim[1L]
> ncol <- dim[2L]
> str(value)
 num 0

> stats <- microbenchmark(allocMatrix = allocMatrix(nrow = nrow, ncol = ncol, value = value), matrix = matrix(data = value, 
+     nrow = nrow, ncol = ncol), allocMatrix_R = allocMatrix_R(nrow = nrow, ncol = ncol, value = value), 
+     unit = "ms")

Table: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on double+10x1000+0 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	allocMatrix	0.005404	0.0059795	0.0068228	0.0062590	0.0066255	0.037027
2	matrix	0.009114	0.0099605	0.0116477	0.0118795	0.0126585	0.018786
3	allocMatrix_R	0.010709	0.0117740	0.0135303	0.0135435	0.0141335	0.036263

	expr	min	lq	mean	median	uq	max
1	allocMatrix	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	matrix	1.686529	1.665775	1.707173	1.897987	1.910573	0.5073595
3	allocMatrix_R	1.981680	1.969061	1.983101	2.163844	2.133198	0.9793664

Figure: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on double+10x1000+0 data. Outliers are displayed as crosses. Times are in milliseconds.

> dim <- dim(data[["10x1000"]])
> nrow <- dim[1L]
> ncol <- dim[2L]
> str(value)
 num 1

> stats <- microbenchmark(allocMatrix = allocMatrix(nrow = nrow, ncol = ncol, value = value), matrix = matrix(data = value, 
+     nrow = nrow, ncol = ncol), allocMatrix_R = allocMatrix_R(nrow = nrow, ncol = ncol, value = value), 
+     unit = "ms")

Table: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on double+10x1000+1 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	allocMatrix	0.008593	0.0094090	0.0116040	0.0119075	0.0129940	0.026036
2	matrix	0.009300	0.0100855	0.0122054	0.0130115	0.0133415	0.029948
3	allocMatrix_R	0.010545	0.0113290	0.0138526	0.0142460	0.0149430	0.045729

	expr	min	lq	mean	median	uq	max
1	allocMatrix	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	matrix	1.082276	1.071899	1.051828	1.092715	1.026743	1.150254
3	allocMatrix_R	1.227162	1.204060	1.193786	1.196389	1.149992	1.756376

Figure: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on double+10x1000+1 data. Outliers are displayed as crosses. Times are in milliseconds.

> dim <- dim(data[["10x1000"]])
> nrow <- dim[1L]
> ncol <- dim[2L]
> str(value)
 num NA

> stats <- microbenchmark(allocMatrix = allocMatrix(nrow = nrow, ncol = ncol, value = value), matrix = matrix(data = value, 
+     nrow = nrow, ncol = ncol), allocMatrix_R = allocMatrix_R(nrow = nrow, ncol = ncol, value = value), 
+     unit = "ms")

Table: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on double+10x1000+NA data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	allocMatrix	0.008344	0.0091300	0.0112733	0.0103390	0.0128935	0.020623
2	matrix	0.009146	0.0099175	0.0123330	0.0111305	0.0134160	0.046179
3	allocMatrix_R	0.012582	0.0136540	0.0158130	0.0152820	0.0161665	0.032673

	expr	min	lq	mean	median	uq	max
1	allocMatrix	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	matrix	1.096117	1.086254	1.093999	1.076555	1.040524	2.239199
3	allocMatrix_R	1.507910	1.495509	1.402695	1.478093	1.253849	1.584299

Figure: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on double+10x1000+NA data. Outliers are displayed as crosses. Times are in milliseconds.

100x1000 matrix

> dim <- dim(data[["100x1000"]])
> nrow <- dim[1L]
> ncol <- dim[2L]
> str(value)
 num 0

> stats <- microbenchmark(allocMatrix = allocMatrix(nrow = nrow, ncol = ncol, value = value), matrix = matrix(data = value, 
+     nrow = nrow, ncol = ncol), allocMatrix_R = allocMatrix_R(nrow = nrow, ncol = ncol, value = value), 
+     unit = "ms")

Table: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on double+100x1000+0 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
3	allocMatrix_R	0.066000	0.0712335	0.2539688	0.2060245	0.3273995	5.135573
2	matrix	0.064473	0.0717115	0.3255174	0.3126555	0.3315425	5.525349
1	allocMatrix	0.034165	0.0402525	0.2864201	0.3316545	0.3537590	6.013321

	expr	min	lq	mean	median	uq	max
3	allocMatrix_R	1.0000000	1.0000000	1.000000	1.000000	1.000000	1.000000
2	matrix	0.9768636	1.0067103	1.281722	1.517565	1.012654	1.075897
1	allocMatrix	0.5176515	0.5650782	1.127777	1.609782	1.080512	1.170915

Figure: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on double+100x1000+0 data. Outliers are displayed as crosses. Times are in milliseconds.

> dim <- dim(data[["100x1000"]])
> nrow <- dim[1L]
> ncol <- dim[2L]
> str(value)
 num 1

> stats <- microbenchmark(allocMatrix = allocMatrix(nrow = nrow, ncol = ncol, value = value), matrix = matrix(data = value, 
+     nrow = nrow, ncol = ncol), allocMatrix_R = allocMatrix_R(nrow = nrow, ncol = ncol, value = value), 
+     unit = "ms")

Table: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on double+100x1000+1 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
2	matrix	0.067710	0.0723005	0.2618119	0.3053665	0.3287630	5.194707
1	allocMatrix	0.068133	0.0720770	0.2763085	0.3106775	0.3246315	5.206379
3	allocMatrix_R	0.068894	0.0729995	0.3293978	0.3138990	0.3364820	5.696306

	expr	min	lq	mean	median	uq	max
2	matrix	1.000000	1.0000000	1.000000	1.000000	1.0000000	1.000000
1	allocMatrix	1.006247	0.9969087	1.055370	1.017392	0.9874332	1.002247
3	allocMatrix_R	1.017486	1.0096680	1.258147	1.027942	1.0234789	1.096560

Figure: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on double+100x1000+1 data. Outliers are displayed as crosses. Times are in milliseconds.

> dim <- dim(data[["100x1000"]])
> nrow <- dim[1L]
> ncol <- dim[2L]
> str(value)
 num NA

> stats <- microbenchmark(allocMatrix = allocMatrix(nrow = nrow, ncol = ncol, value = value), matrix = matrix(data = value, 
+     nrow = nrow, ncol = ncol), allocMatrix_R = allocMatrix_R(nrow = nrow, ncol = ncol, value = value), 
+     unit = "ms")

Table: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on double+100x1000+NA data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	allocMatrix	0.067418	0.0714845	0.2603468	0.3062970	0.3270665	5.650084
2	matrix	0.068016	0.0716390	0.2821139	0.3115355	0.3376305	5.808411
3	allocMatrix_R	0.081688	0.0902545	0.3573313	0.3429810	0.3702105	5.902333

	expr	min	lq	mean	median	uq	max
1	allocMatrix	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	matrix	1.008870	1.002161	1.083608	1.017103	1.032299	1.028022
3	allocMatrix_R	1.211664	1.262574	1.372521	1.119766	1.131912	1.044645

Figure: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on double+100x1000+NA data. Outliers are displayed as crosses. Times are in milliseconds.

1000x100 matrix

> dim <- dim(data[["1000x100"]])
> nrow <- dim[1L]
> ncol <- dim[2L]
> str(value)
 num 0

> stats <- microbenchmark(allocMatrix = allocMatrix(nrow = nrow, ncol = ncol, value = value), matrix = matrix(data = value, 
+     nrow = nrow, ncol = ncol), allocMatrix_R = allocMatrix_R(nrow = nrow, ncol = ncol, value = value), 
+     unit = "ms")

Table: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on double+1000x100+0 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
2	matrix	0.066394	0.0720645	0.2161910	0.3111450	0.3333495	0.388541
3	allocMatrix_R	0.069641	0.0735630	0.2780355	0.3140955	0.3403995	5.219482
1	allocMatrix	0.033830	0.0398455	0.3786113	0.3293990	0.3643695	5.982206

	expr	min	lq	mean	median	uq	max
2	matrix	1.000000	1.0000000	1.000000	1.000000	1.000000	1.00000
3	allocMatrix_R	1.048905	1.0207939	1.286064	1.009483	1.021149	13.43354
1	allocMatrix	0.509534	0.5529144	1.751281	1.058667	1.093055	15.39659

Figure: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on double+1000x100+0 data. Outliers are displayed as crosses. Times are in milliseconds.

> dim <- dim(data[["1000x100"]])
> nrow <- dim[1L]
> ncol <- dim[2L]
> str(value)
 num 1

> stats <- microbenchmark(allocMatrix = allocMatrix(nrow = nrow, ncol = ncol, value = value), matrix = matrix(data = value, 
+     nrow = nrow, ncol = ncol), allocMatrix_R = allocMatrix_R(nrow = nrow, ncol = ncol, value = value), 
+     unit = "ms")

Table: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on double+1000x100+1 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
2	matrix	0.067829	0.071992	0.3240764	0.3114670	0.3277870	6.026421
3	allocMatrix_R	0.069527	0.074972	0.2776584	0.3133930	0.3391130	5.766016
1	allocMatrix	0.067016	0.072185	0.2837553	0.3134775	0.3564695	5.209411

	expr	min	lq	mean	median	uq	max
2	matrix	1.000000	1.000000	1.0000000	1.000000	1.000000	1.0000000
3	allocMatrix_R	1.025033	1.041394	0.8567682	1.006184	1.034553	0.9567894
1	allocMatrix	0.988014	1.002681	0.8755814	1.006455	1.087503	0.8644287

Figure: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on double+1000x100+1 data. Outliers are displayed as crosses. Times are in milliseconds.

> dim <- dim(data[["1000x100"]])
> nrow <- dim[1L]
> ncol <- dim[2L]
> str(value)
 num NA

> stats <- microbenchmark(allocMatrix = allocMatrix(nrow = nrow, ncol = ncol, value = value), matrix = matrix(data = value, 
+     nrow = nrow, ncol = ncol), allocMatrix_R = allocMatrix_R(nrow = nrow, ncol = ncol, value = value), 
+     unit = "ms")

Table: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on double+1000x100+NA data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
2	matrix	0.068278	0.0738680	0.3210694	0.3108995	0.330711	5.325094
1	allocMatrix	0.068552	0.0740710	0.2225282	0.3117630	0.332891	0.395910
3	allocMatrix_R	0.080048	0.0909165	0.3666173	0.3456725	0.387099	6.170301

	expr	min	lq	mean	median	uq	max
2	matrix	1.000000	1.000000	1.0000000	1.000000	1.000000	1.000000
1	allocMatrix	1.004013	1.002748	0.6930845	1.002777	1.006592	0.074348
3	allocMatrix_R	1.172384	1.230797	1.1418632	1.111846	1.170505	1.158721

Figure: Benchmarking of allocMatrix(), matrix() and allocMatrix_R() on double+1000x100+NA data. 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_0.60.1     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     survival_3.2-13         magrittr_2.0.1         
[40] crayon_1.4.1            statnet.common_4.5.0    memoise_2.0.0          
[43] laeken_0.5.1            fansi_0.5.0             R.cache_0.15.0         
[46] MASS_7.3-54             R.rsp_0.44.0            progressr_0.8.0        
[49] tools_4.1.1             lifecycle_1.0.0         S4Vectors_0.30.0       
[52] trust_0.1-8             munsell_0.5.0           tabby_0.0.1-9001       
[55] AnnotationDbi_1.54.1    Biostrings_2.60.2       compiler_4.1.1         
[58] GenomeInfoDb_1.28.1     rlang_0.4.11            grid_4.1.1             
[61] RCurl_1.98-1.4          cwhmisc_6.6             rappdirs_0.3.3         
[64] startup_0.15.0          labeling_0.4.2          bitops_1.0-7           
[67] base64enc_0.1-3         boot_1.3-28             gtable_0.3.0           
[70] DBI_1.1.1               R6_2.5.1                lpSolveAPI_5.5.2.0-17.7
[73] rle_0.9.2               dplyr_1.0.7             fastmap_1.1.0          
[76] bit_4.0.4               utf8_1.2.2              parallel_4.1.1         
[79] Rcpp_1.0.7              vctrs_0.3.8             png_0.1-7              
[82] DEoptimR_1.0-9          tidyselect_1.1.1        xfun_0.25              
[85] coda_0.19-4            

Total processing time was 36.06 secs.

Reproducibility

To reproduce this report, do:

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

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Copyright Henrik Bengtsson. Last updated on 2021-08-25 18:44:41 (+0200 UTC). Powered by RSP.