matrixStats.benchmarks

x_OP_y() benchmarks

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

Alternative methods

x_OP_y_R()

as below

> x_OP_y_R <- function(x, y, OP, na.rm = FALSE) {
+     if (na.rm) {
+         xnok <- is.na(x)
+         ynok <- is.na(y)
+         anok <- xnok & ynok
+         unit <- switch(OP, `+` = 0, `-` = NA_real_, `*` = 1, `/` = NA_real_, stop("Unknown 'OP' operator: ", 
+             OP))
+         x[xnok] <- unit
+         y[ynok] <- unit
+     }
+     ans <- switch(OP, `+` = x + y, `-` = x - y, `*` = x * y, `/` = x/y, stop("Unknown 'OP' operator: ", 
+         OP))
+     if (na.rm) {
+         ans[anok] <- NA_real_
+     }
+     ans
+ }

Data type “integer”

Data

> rmatrix <- function(nrow, ncol, mode = c("logical", "double", "integer", "index"), range = c(-100, 
+     +100), na_prob = 0) {
+     mode <- match.arg(mode)
+     n <- nrow * ncol
+     if (mode == "logical") {
+         x <- sample(c(FALSE, TRUE), size = n, replace = TRUE)
+     }     else if (mode == "index") {
+         x <- seq_len(n)
+         mode <- "integer"
+     }     else {
+         x <- runif(n, min = range[1], max = range[2])
+     }
+     storage.mode(x) <- mode
+     if (na_prob > 0) 
+         x[sample(n, size = na_prob * n)] <- NA
+     dim(x) <- c(nrow, ncol)
+     x
+ }
> rmatrices <- function(scale = 10, seed = 1, ...) {
+     set.seed(seed)
+     data <- list()
+     data[[1]] <- rmatrix(nrow = scale * 1, ncol = scale * 1, ...)
+     data[[2]] <- rmatrix(nrow = scale * 10, ncol = scale * 10, ...)
+     data[[3]] <- rmatrix(nrow = scale * 100, ncol = scale * 1, ...)
+     data[[4]] <- t(data[[3]])
+     data[[5]] <- rmatrix(nrow = scale * 10, ncol = scale * 100, ...)
+     data[[6]] <- t(data[[5]])
+     names(data) <- sapply(data, FUN = function(x) paste(dim(x), collapse = "x"))
+     data
+ }
> data <- rmatrices(mode = mode)

Results

10x10 vector

All elements

> x <- data[["10x10"]]
> y <- x[, 1L]

> OP
[1] "+"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb)  max used  (Mb)
Ncells  5404607 288.7    8529671 455.6   8529671 455.6
Vcells 10799895  82.4   39910282 304.5 101881463 777.3

Table: Benchmarking of x_OP_y() and x_OP_y_R() on integer+10x10+add data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.001603	0.0018255	0.1140739	0.0019665	0.0021525	11.184827
1	x_OP_y	0.004082	0.0043470	0.0046784	0.0044490	0.0046170	0.018996

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.000000	1.000000	1.0000000	1.000000	1.000000	1.0000000
1	x_OP_y	2.546475	2.381265	0.0410122	2.262395	2.144948	0.0016984

Figure: Benchmarking of x_OP_y() and x_OP_y_R() on integer+10x10+add data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "-"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb)  max used  (Mb)
Ncells  5402218 288.6    8529671 455.6   8529671 455.6
Vcells 10491034  80.1   39910282 304.5 101881463 777.3

Table: Benchmarking of x_OP_y() and x_OP_y_R() on integer+10x10+sub data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.001762	0.0020735	0.0024343	0.0021740	0.0023385	0.015272
1	x_OP_y	0.004476	0.0047290	0.0051702	0.0048735	0.0050755	0.023710

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.000000	1.000000	1.000000	1.00000	1.000000	1.000000
1	x_OP_y	2.540295	2.280685	2.123931	2.24172	2.170408	1.552514

Figure: Benchmarking of x_OP_y() and x_OP_y_R() on integer+10x10+sub data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "*"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb)  max used  (Mb)
Ncells  5402281 288.6    8529671 455.6   8529671 455.6
Vcells 10491588  80.1   39910282 304.5 101881463 777.3

Table: Benchmarking of x_OP_y() and x_OP_y_R() on integer+10x10+mul data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.001959	0.0022125	0.0024465	0.0023060	0.0024870	0.010178
1	x_OP_y	0.004448	0.0047065	0.0052627	0.0048795	0.0051225	0.026669

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.000000	1.000000	1.000000	1.000000	1.00000	1.000000
1	x_OP_y	2.270546	2.127232	2.151139	2.116002	2.05971	2.620259

Figure: Benchmarking of x_OP_y() and x_OP_y_R() on integer+10x10+mul data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "/"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb)  max used  (Mb)
Ncells  5402344 288.6    8529671 455.6   8529671 455.6
Vcells 10491630  80.1   39910282 304.5 101881463 777.3

Table: Benchmarking of x_OP_y() and x_OP_y_R() on integer+10x10+div data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.001814	0.0020675	0.0023544	0.0021820	0.0023335	0.014515
1	x_OP_y	0.004503	0.0047955	0.0051758	0.0049525	0.0050735	0.022483

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	x_OP_y	2.482359	2.319468	2.198381	2.269707	2.174202	1.548949

Figure: Benchmarking of x_OP_y() and x_OP_y_R() on integer+10x10+div data. Outliers are displayed as crosses. Times are in milliseconds.

100x100 vector

All elements

> x <- data[["100x100"]]
> y <- x[, 1L]

> OP
[1] "+"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb)  max used  (Mb)
Ncells  5402407 288.6    8529671 455.6   8529671 455.6
Vcells 10491714  80.1   39910282 304.5 101881463 777.3

Table: Benchmarking of x_OP_y() and x_OP_y_R() on integer+100x100+add data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.031469	0.0326930	0.0341347	0.0337845	0.0351970	0.042978
1	x_OP_y	0.058127	0.0608795	0.0644826	0.0632325	0.0659555	0.089735

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	x_OP_y	1.847119	1.862157	1.889064	1.871642	1.873895	2.087929

Figure: Benchmarking of x_OP_y() and x_OP_y_R() on integer+100x100+add data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "-"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb)  max used  (Mb)
Ncells  5402470 288.6    8529671 455.6   8529671 455.6
Vcells 10491967  80.1   39910282 304.5 101881463 777.3

Table: Benchmarking of x_OP_y() and x_OP_y_R() on integer+100x100+sub data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.030145	0.0314005	0.0330939	0.0329155	0.0341405	0.049186
1	x_OP_y	0.054284	0.0564870	0.0596179	0.0589780	0.0615700	0.083293

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.000000	1.00000	1.000000	1.0000	1.00000	1.000000
1	x_OP_y	1.800763	1.79892	1.801477	1.7918	1.80343	1.693429

Figure: Benchmarking of x_OP_y() and x_OP_y_R() on integer+100x100+sub data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "*"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb)  max used  (Mb)
Ncells  5402533 288.6    8529671 455.6   8529671 455.6
Vcells 10492264  80.1   39910282 304.5 101881463 777.3

Table: Benchmarking of x_OP_y() and x_OP_y_R() on integer+100x100+mul data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.045483	0.0487630	0.0509329	0.0493590	0.0528595	0.069041
1	x_OP_y	0.052961	0.0570025	0.0592485	0.0583745	0.0617815	0.080283

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.000000	1.00000	1.000000	1.000000	1.000000	1.000000
1	x_OP_y	1.164413	1.16897	1.163266	1.182652	1.168787	1.162831

Figure: Benchmarking of x_OP_y() and x_OP_y_R() on integer+100x100+mul data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "/"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb)  max used  (Mb)
Ncells  5402596 288.6    8529671 455.6   8529671 455.6
Vcells 10492306  80.1   39910282 304.5 101881463 777.3

Table: Benchmarking of x_OP_y() and x_OP_y_R() on integer+100x100+div data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.021082	0.022092	0.0236331	0.0231195	0.0239245	0.044275
1	x_OP_y	0.054500	0.056597	0.0591946	0.0592540	0.0603160	0.077289

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	x_OP_y	2.585144	2.561878	2.504738	2.562945	2.521098	1.745658

Figure: Benchmarking of x_OP_y() and x_OP_y_R() on integer+100x100+div data. Outliers are displayed as crosses. Times are in milliseconds.

1000x10 vector

All elements

> x <- data[["1000x10"]]
> y <- x[, 1L]

> OP
[1] "+"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb)  max used  (Mb)
Ncells  5402659 288.6    8529671 455.6   8529671 455.6
Vcells 10493102  80.1   39910282 304.5 101881463 777.3

Table: Benchmarking of x_OP_y() and x_OP_y_R() on integer+1000x10+add data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.031729	0.033068	0.0352056	0.0344665	0.0360445	0.064046
1	x_OP_y	0.056674	0.059156	0.0619956	0.0616530	0.0641960	0.081126

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.000000	1.00000	1.000000	1.00000	1.000000	1.000000
1	x_OP_y	1.786189	1.78892	1.760961	1.78878	1.781021	1.266683

Figure: Benchmarking of x_OP_y() and x_OP_y_R() on integer+1000x10+add data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "-"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb)  max used  (Mb)
Ncells  5402722 288.6    8529671 455.6   8529671 455.6
Vcells 10493144  80.1   39910282 304.5 101881463 777.3

Table: Benchmarking of x_OP_y() and x_OP_y_R() on integer+1000x10+sub data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.035391	0.0367795	0.0391165	0.0384255	0.040157	0.066624
1	x_OP_y	0.053326	0.0559190	0.0589082	0.0579965	0.060646	0.078191

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	x_OP_y	1.506767	1.520385	1.505967	1.509323	1.510222	1.173616

Figure: Benchmarking of x_OP_y() and x_OP_y_R() on integer+1000x10+sub data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "*"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb)  max used  (Mb)
Ncells  5402785 288.6    8529671 455.6   8529671 455.6
Vcells 10493571  80.1   39910282 304.5 101881463 777.3

Table: Benchmarking of x_OP_y() and x_OP_y_R() on integer+1000x10+mul data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.045229	0.0470680	0.0509546	0.049234	0.0528285	0.079048
1	x_OP_y	0.051532	0.0534885	0.0568272	0.055202	0.0584470	0.092235

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.000000	1.000000	1.00000	1.000000	1.000000	1.000000
1	x_OP_y	1.139358	1.136409	1.11525	1.121217	1.106354	1.166823

Figure: Benchmarking of x_OP_y() and x_OP_y_R() on integer+1000x10+mul data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "/"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb)  max used  (Mb)
Ncells  5402848 288.6    8529671 455.6   8529671 455.6
Vcells 10493613  80.1   39910282 304.5 101881463 777.3

Table: Benchmarking of x_OP_y() and x_OP_y_R() on integer+1000x10+div data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.021889	0.0224825	0.0242224	0.023268	0.024735	0.046390
1	x_OP_y	0.055214	0.0561815	0.0586379	0.058037	0.060719	0.079152

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.000000	1.000000	1.000000	1.000000	1.000000	1.00000
1	x_OP_y	2.522454	2.498899	2.420812	2.494284	2.454781	1.70623

Figure: Benchmarking of x_OP_y() and x_OP_y_R() on integer+1000x10+div data. Outliers are displayed as crosses. Times are in milliseconds.

10x1000 vector

All elements

> x <- data[["10x1000"]]
> y <- x[, 1L]

> OP
[1] "+"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb)  max used  (Mb)
Ncells  5402911 288.6    8529671 455.6   8529671 455.6
Vcells 10493610  80.1   39910282 304.5 101881463 777.3

Table: Benchmarking of x_OP_y() and x_OP_y_R() on integer+10x1000+add data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.030982	0.032348	0.0336535	0.0328665	0.034370	0.047486
1	x_OP_y	0.057955	0.060729	0.0634756	0.0620700	0.064336	0.091238

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	x_OP_y	1.870602	1.877365	1.886155	1.888549	1.871865	1.921366

Figure: Benchmarking of x_OP_y() and x_OP_y_R() on integer+10x1000+add data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "-"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb)  max used  (Mb)
Ncells  5402974 288.6    8529671 455.6   8529671 455.6
Vcells 10493652  80.1   39910282 304.5 101881463 777.3

Table: Benchmarking of x_OP_y() and x_OP_y_R() on integer+10x1000+sub data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.030304	0.0308255	0.0327464	0.0320415	0.033471	0.049065
1	x_OP_y	0.055043	0.0560135	0.0598170	0.0585225	0.062212	0.079605

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.000000	1.000000	1.000000	1.000000	1.000000	1.00000
1	x_OP_y	1.816361	1.817116	1.826676	1.826459	1.858684	1.62244

Figure: Benchmarking of x_OP_y() and x_OP_y_R() on integer+10x1000+sub data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "*"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb)  max used  (Mb)
Ncells  5403037 288.6    8529671 455.6   8529671 455.6
Vcells 10493694  80.1   39910282 304.5 101881463 777.3

Table: Benchmarking of x_OP_y() and x_OP_y_R() on integer+10x1000+mul data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.04677	0.0483545	0.0507220	0.0496180	0.0529620	0.063674
1	x_OP_y	0.05630	0.0573090	0.0612647	0.0604095	0.0638595	0.088579

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	x_OP_y	1.203763	1.185184	1.207853	1.217492	1.205761	1.391133

Figure: Benchmarking of x_OP_y() and x_OP_y_R() on integer+10x1000+mul data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "/"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb)  max used  (Mb)
Ncells  5403100 288.6    8529671 455.6   8529671 455.6
Vcells 10494295  80.1   39910282 304.5 101881463 777.3

Table: Benchmarking of x_OP_y() and x_OP_y_R() on integer+10x1000+div data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.021209	0.0223925	0.0242928	0.0233565	0.024489	0.039011
1	x_OP_y	0.055127	0.0574600	0.0603755	0.0598045	0.060769	0.093059

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	x_OP_y	2.599227	2.566038	2.485327	2.560508	2.481482	2.385455

Figure: Benchmarking of x_OP_y() and x_OP_y_R() on integer+10x1000+div data. Outliers are displayed as crosses. Times are in milliseconds.

100x1000 vector

All elements

> x <- data[["100x1000"]]
> y <- x[, 1L]

> OP
[1] "+"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb)  max used  (Mb)
Ncells  5403163 288.6    8529671 455.6   8529671 455.6
Vcells 10494379  80.1   39910282 304.5 101881463 777.3

Table: Benchmarking of x_OP_y() and x_OP_y_R() on integer+100x1000+add data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.201069	0.2018455	0.2442470	0.2343965	0.2809435	0.353473
1	x_OP_y	0.373197	0.3795980	0.4456981	0.4244400	0.4868685	0.646700

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.000000	1.000000	1.000000	1.000000	1.000000	1.00000
1	x_OP_y	1.856064	1.880636	1.824784	1.810778	1.732977	1.82956

Figure: Benchmarking of x_OP_y() and x_OP_y_R() on integer+100x1000+add data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "-"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb)  max used  (Mb)
Ncells  5403226 288.6    8529671 455.6   8529671 455.6
Vcells 10494421  80.1   39910282 304.5 101881463 777.3

Table: Benchmarking of x_OP_y() and x_OP_y_R() on integer+100x1000+sub data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.194085	0.1949615	0.2294495	0.2123840	0.2535775	0.336659
1	x_OP_y	0.347655	0.3492515	0.4170779	0.3993875	0.4535080	0.585131

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	x_OP_y	1.791251	1.791387	1.817733	1.880497	1.788439	1.738053

Figure: Benchmarking of x_OP_y() and x_OP_y_R() on integer+100x1000+sub data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "*"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb)  max used  (Mb)
Ncells  5403289 288.6    8529671 455.6   8529671 455.6
Vcells 10495100  80.1   39910282 304.5 101881463 777.3

Table: Benchmarking of x_OP_y() and x_OP_y_R() on integer+100x1000+mul data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.310607	0.3136910	0.3659744	0.339103	0.399359	0.530073
1	x_OP_y	0.351335	0.3521345	0.4036749	0.381364	0.443125	0.585934

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	x_OP_y	1.131124	1.122552	1.103014	1.124626	1.109591	1.105384

Figure: Benchmarking of x_OP_y() and x_OP_y_R() on integer+100x1000+mul data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "/"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb)  max used  (Mb)
Ncells  5403352 288.6    8529671 455.6   8529671 455.6
Vcells 10495142  80.1   39910282 304.5 101881463 777.3

Table: Benchmarking of x_OP_y() and x_OP_y_R() on integer+100x1000+div data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.166981	0.207848	0.3177664	0.3663985	0.389694	0.467825
1	x_OP_y	0.435427	0.468897	0.5527325	0.5785120	0.593809	0.727670

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.000000	1.000000	1.00000	1.000000	1.000000	1.000000
1	x_OP_y	2.607644	2.255961	1.73943	1.578915	1.523783	1.555432

Figure: Benchmarking of x_OP_y() and x_OP_y_R() on integer+100x1000+div data. Outliers are displayed as crosses. Times are in milliseconds.

1000x100 vector

All elements

> x <- data[["1000x100"]]
> y <- x[, 1L]

> OP
[1] "+"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb)  max used  (Mb)
Ncells  5403415 288.6    8529671 455.6   8529671 455.6
Vcells 10495634  80.1   39910282 304.5 101881463 777.3

Table: Benchmarking of x_OP_y() and x_OP_y_R() on integer+1000x100+add data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.205045	0.208476	0.2521633	0.2419085	0.2751795	0.363857
1	x_OP_y	0.364726	0.366279	0.4398643	0.4318195	0.4816780	0.633653

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	x_OP_y	1.778761	1.756936	1.744363	1.785053	1.750414	1.741489

Figure: Benchmarking of x_OP_y() and x_OP_y_R() on integer+1000x100+add data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "-"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb)  max used  (Mb)
Ncells  5403478 288.6    8529671 455.6   8529671 455.6
Vcells 10495676  80.1   39910282 304.5 101881463 777.3

Table: Benchmarking of x_OP_y() and x_OP_y_R() on integer+1000x100+sub data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.229490	0.2336960	0.2736903	0.2676895	0.2963935	0.445591
1	x_OP_y	0.339242	0.3407585	0.4004085	0.3790170	0.4421515	0.600065

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	x_OP_y	1.478243	1.458127	1.462998	1.415883	1.491772	1.346672

Figure: Benchmarking of x_OP_y() and x_OP_y_R() on integer+1000x100+sub data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "*"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb)  max used  (Mb)
Ncells  5403541 288.6    8529671 455.6   8529671 455.6
Vcells 10496502  80.1   39910282 304.5 101881463 777.3

Table: Benchmarking of x_OP_y() and x_OP_y_R() on integer+1000x100+mul data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.310424	0.3115825	0.3645297	0.336936	0.392807	0.557051
1	x_OP_y	0.343221	0.3447010	0.4001292	0.376380	0.442234	0.577459

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.000000	1.000000	1.000000	1.000000	1.00000	1.000000
1	x_OP_y	1.105652	1.106291	1.097659	1.117067	1.12583	1.036636

Figure: Benchmarking of x_OP_y() and x_OP_y_R() on integer+1000x100+mul data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "/"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb)  max used  (Mb)
Ncells  5403604 288.6    8529671 455.6   8529671 455.6
Vcells 10496544  80.1   39910282 304.5 101881463 777.3

Table: Benchmarking of x_OP_y() and x_OP_y_R() on integer+1000x100+div data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.167526	0.1936995	0.3076920	0.3658255	0.3750190	0.452235
1	x_OP_y	0.426219	0.4777825	0.5498017	0.5717950	0.5981815	0.728082

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.000000	1.000000	1.000000	1.000000	1.00000	1.000000
1	x_OP_y	2.544196	2.466617	1.786857	1.563027	1.59507	1.609964

Figure: Benchmarking of x_OP_y() and x_OP_y_R() on integer+1000x100+div 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)

Results

10x10 vector

All elements

> x <- data[["10x10"]]
> y <- x[, 1L]

> OP
[1] "+"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb)  max used  (Mb)
Ncells  5403667 288.6    8529671 455.6   8529671 455.6
Vcells 10611152  81.0   39910282 304.5 101881463 777.3

Table: Benchmarking of x_OP_y() and x_OP_y_R() on double+10x10+add data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.001736	0.0018625	0.0021396	0.0019765	0.002113	0.013611
1	x_OP_y	0.004265	0.0045190	0.0049070	0.0047005	0.004845	0.022099

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	x_OP_y	2.456797	2.426309	2.293386	2.378194	2.292948	1.623613

Figure: Benchmarking of x_OP_y() and x_OP_y_R() on double+10x10+add data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "-"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb)  max used  (Mb)
Ncells  5403730 288.6    8529671 455.6   8529671 455.6
Vcells 10612118  81.0   39910282 304.5 101881463 777.3

Table: Benchmarking of x_OP_y() and x_OP_y_R() on double+10x10+sub data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.001740	0.001979	0.0022198	0.0020675	0.0021845	0.014084
1	x_OP_y	0.004255	0.004651	0.0049759	0.0047555	0.0049015	0.022229

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	x_OP_y	2.445402	2.350177	2.241606	2.300121	2.243763	1.578316

Figure: Benchmarking of x_OP_y() and x_OP_y_R() on double+10x10+sub data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "*"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb)  max used  (Mb)
Ncells  5403793 288.6    8529671 455.6   8529671 455.6
Vcells 10612160  81.0   39910282 304.5 101881463 777.3

Table: Benchmarking of x_OP_y() and x_OP_y_R() on double+10x10+mul data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.001522	0.0018145	0.0020195	0.0019055	0.0020160	0.008407
1	x_OP_y	0.004053	0.0042320	0.0047225	0.0043345	0.0044895	0.038830

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.000000	1.000000	1.000000	1.000000	1.000000	1.00000
1	x_OP_y	2.662943	2.332323	2.338496	2.274731	2.226935	4.61877

Figure: Benchmarking of x_OP_y() and x_OP_y_R() on double+10x10+mul data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "/"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb)  max used  (Mb)
Ncells  5403856 288.6    8529671 455.6   8529671 455.6
Vcells 10612202  81.0   39910282 304.5 101881463 777.3

Table: Benchmarking of x_OP_y() and x_OP_y_R() on double+10x10+div data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.001732	0.0019615	0.0022150	0.002111	0.0021970	0.012134
1	x_OP_y	0.004311	0.0046495	0.0049534	0.004774	0.0049005	0.020479

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.00000	1.00000	1.000000	1.000000	1.000000	1.000000
1	x_OP_y	2.48903	2.37038	2.236305	2.261487	2.230542	1.687737

Figure: Benchmarking of x_OP_y() and x_OP_y_R() on double+10x10+div data. Outliers are displayed as crosses. Times are in milliseconds.

100x100 vector

All elements

> x <- data[["100x100"]]
> y <- x[, 1L]

> OP
[1] "+"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb)  max used  (Mb)
Ncells  5403919 288.7    8529671 455.6   8529671 455.6
Vcells 10612328  81.0   39910282 304.5 101881463 777.3

Table: Benchmarking of x_OP_y() and x_OP_y_R() on double+100x100+add data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.015893	0.0165795	0.022736	0.017249	0.0292710	0.064716
1	x_OP_y	0.033253	0.0339055	0.039120	0.035321	0.0401435	0.108973

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	x_OP_y	2.092305	2.045025	1.720624	2.047713	1.371443	1.683865

Figure: Benchmarking of x_OP_y() and x_OP_y_R() on double+100x100+add data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "-"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb)  max used  (Mb)
Ncells  5403982 288.7    8529671 455.6   8529671 455.6
Vcells 10612370  81.0   39910282 304.5 101881463 777.3

Table: Benchmarking of x_OP_y() and x_OP_y_R() on double+100x100+sub data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.017929	0.0189055	0.0198866	0.0193445	0.0202875	0.032878
1	x_OP_y	0.037223	0.0386940	0.0409129	0.0393990	0.0414005	0.070939

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.000000	1.000000	1.000000	1.000000	1.00000	1.000000
1	x_OP_y	2.076134	2.046706	2.057306	2.036703	2.04069	2.157643

Figure: Benchmarking of x_OP_y() and x_OP_y_R() on double+100x100+sub data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "*"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb)  max used  (Mb)
Ncells  5404045 288.7    8529671 455.6   8529671 455.6
Vcells 10612412  81.0   39910282 304.5 101881463 777.3

Table: Benchmarking of x_OP_y() and x_OP_y_R() on double+100x100+mul data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.016414	0.0173770	0.0184142	0.017963	0.0184160	0.037957
1	x_OP_y	0.030636	0.0326165	0.0350945	0.035042	0.0369455	0.061260

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	x_OP_y	1.866456	1.876993	1.905844	1.950788	2.006163	1.613932

Figure: Benchmarking of x_OP_y() and x_OP_y_R() on double+100x100+mul data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "/"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb)  max used  (Mb)
Ncells  5404108 288.7    8529671 455.6   8529671 455.6
Vcells 10613562  81.0   39910282 304.5 101881463 777.3

Table: Benchmarking of x_OP_y() and x_OP_y_R() on double+100x100+div data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.017488	0.0182205	0.0191984	0.0185830	0.019008	0.037906
1	x_OP_y	0.035856	0.0371475	0.0386060	0.0377585	0.038189	0.069325

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.00000	1.000000	1.000000	1.000000	1.000000	1.000000
1	x_OP_y	2.05032	2.038775	2.010897	2.031884	2.009101	1.828866

Figure: Benchmarking of x_OP_y() and x_OP_y_R() on double+100x100+div data. Outliers are displayed as crosses. Times are in milliseconds.

1000x10 vector

All elements

> x <- data[["1000x10"]]
> y <- x[, 1L]

> OP
[1] "+"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb)  max used  (Mb)
Ncells  5404171 288.7    8529671 455.6   8529671 455.6
Vcells 10614504  81.0   39910282 304.5 101881463 777.3

Table: Benchmarking of x_OP_y() and x_OP_y_R() on double+1000x10+add data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.016390	0.0175035	0.0184785	0.0180900	0.0187235	0.038247
1	x_OP_y	0.033524	0.0342110	0.0360122	0.0350345	0.0358615	0.070558

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.000000	1.000000	1.00000	1.000000	1.00000	1.000000
1	x_OP_y	2.045393	1.954523	1.94887	1.936678	1.91532	1.844798

Figure: Benchmarking of x_OP_y() and x_OP_y_R() on double+1000x10+add data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "-"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb)  max used  (Mb)
Ncells  5404234 288.7    8529671 455.6   8529671 455.6
Vcells 10614546  81.0   39910282 304.5 101881463 777.3

Table: Benchmarking of x_OP_y() and x_OP_y_R() on double+1000x10+sub data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.018027	0.0192205	0.0213826	0.019984	0.0205655	0.051183
1	x_OP_y	0.036124	0.0380460	0.0398782	0.038543	0.0402620	0.061086

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	x_OP_y	2.003883	1.979449	1.864982	1.928693	1.957745	1.193482

Figure: Benchmarking of x_OP_y() and x_OP_y_R() on double+1000x10+sub data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "*"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb)  max used  (Mb)
Ncells  5404297 288.7    8529671 455.6   8529671 455.6
Vcells 10614588  81.0   39910282 304.5 101881463 777.3

Table: Benchmarking of x_OP_y() and x_OP_y_R() on double+1000x10+mul data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.015948	0.0167785	0.0179647	0.0173725	0.0180780	0.048760
1	x_OP_y	0.028334	0.0306305	0.0332469	0.0329195	0.0340515	0.066041

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.000000	1.00000	1.000000	1.00000	1.000000	1.000000
1	x_OP_y	1.776649	1.82558	1.850678	1.89492	1.883588	1.354409

Figure: Benchmarking of x_OP_y() and x_OP_y_R() on double+1000x10+mul data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "/"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb)  max used  (Mb)
Ncells  5404360 288.7    8529671 455.6   8529671 455.6
Vcells 10614630  81.0   39910282 304.5 101881463 777.3

Table: Benchmarking of x_OP_y() and x_OP_y_R() on double+1000x10+div data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.017949	0.0192365	0.0211234	0.0199365	0.020605	0.052354
1	x_OP_y	0.036491	0.0383605	0.0399859	0.0398610	0.040336	0.062228

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	x_OP_y	2.033038	1.994152	1.892964	1.999398	1.957583	1.188601

Figure: Benchmarking of x_OP_y() and x_OP_y_R() on double+1000x10+div data. Outliers are displayed as crosses. Times are in milliseconds.

10x1000 vector

All elements

> x <- data[["10x1000"]]
> y <- x[, 1L]

> OP
[1] "+"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb)  max used  (Mb)
Ncells  5404423 288.7    8529671 455.6   8529671 455.6
Vcells 10613688  81.0   39910282 304.5 101881463 777.3

Table: Benchmarking of x_OP_y() and x_OP_y_R() on double+10x1000+add data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.016603	0.0179705	0.0186015	0.0183595	0.0187405	0.032677
1	x_OP_y	0.036541	0.0380760	0.0394199	0.0385505	0.0389205	0.069394

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	x_OP_y	2.200867	2.118806	2.119175	2.099758	2.076812	2.123634

Figure: Benchmarking of x_OP_y() and x_OP_y_R() on double+10x1000+add data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "-"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb)  max used  (Mb)
Ncells  5404486 288.7    8529671 455.6   8529671 455.6
Vcells 10615058  81.0   39910282 304.5 101881463 777.3

Table: Benchmarking of x_OP_y() and x_OP_y_R() on double+10x1000+sub data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.017968	0.0189575	0.0205455	0.0196865	0.020471	0.044544
1	x_OP_y	0.038308	0.0401340	0.0421595	0.0408940	0.042429	0.068975

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.000000	1.000000	1.000	1.000000	1.000000	1.000000
1	x_OP_y	2.132013	2.117051	2.052	2.077261	2.072639	1.548469

Figure: Benchmarking of x_OP_y() and x_OP_y_R() on double+10x1000+sub data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "*"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb)  max used  (Mb)
Ncells  5404549 288.7    8529671 455.6   8529671 455.6
Vcells 10615100  81.0   39910282 304.5 101881463 777.3

Table: Benchmarking of x_OP_y() and x_OP_y_R() on double+10x1000+mul data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.015686	0.0168935	0.0178450	0.0174455	0.018304	0.026303
1	x_OP_y	0.032617	0.0346235	0.0372367	0.0362785	0.037914	0.070082

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.00000	1.000000	1.000000	1.000000	1.00000	1.000000
1	x_OP_y	2.07937	2.049516	2.086674	2.079533	2.07135	2.664411

Figure: Benchmarking of x_OP_y() and x_OP_y_R() on double+10x1000+mul data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "/"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb)  max used  (Mb)
Ncells  5404612 288.7    8529671 455.6   8529671 455.6
Vcells 10615142  81.0   39910282 304.5 101881463 777.3

Table: Benchmarking of x_OP_y() and x_OP_y_R() on double+10x1000+div data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.018281	0.0191055	0.0198625	0.019436	0.020127	0.028389
1	x_OP_y	0.037990	0.0393660	0.0412304	0.040048	0.041905	0.077980

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	x_OP_y	2.078114	2.060454	2.075796	2.060506	2.082029	2.746839

Figure: Benchmarking of x_OP_y() and x_OP_y_R() on double+10x1000+div data. Outliers are displayed as crosses. Times are in milliseconds.

100x1000 vector

All elements

> x <- data[["100x1000"]]
> y <- x[, 1L]

> OP
[1] "+"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb)  max used  (Mb)
Ncells  5404675 288.7    8529671 455.6   8529671 455.6
Vcells 10615268  81.0   39910282 304.5 101881463 777.3

Table: Benchmarking of x_OP_y() and x_OP_y_R() on double+100x1000+add data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.121580	0.1476155	0.2878855	0.3346335	0.3639565	0.415505
1	x_OP_y	0.251263	0.2867760	0.4036696	0.4477310	0.4984100	0.554797

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	x_OP_y	2.066648	1.942723	1.402188	1.337974	1.369422	1.335235

Figure: Benchmarking of x_OP_y() and x_OP_y_R() on double+100x1000+add data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "-"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb)  max used  (Mb)
Ncells  5404738 288.7    8529671 455.6   8529671 455.6
Vcells 10615310  81.0   39910282 304.5 101881463 777.3

Table: Benchmarking of x_OP_y() and x_OP_y_R() on double+100x1000+sub data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.137563	0.1713525	0.2993443	0.3412560	0.3820850	0.442008
1	x_OP_y	0.283061	0.3217805	0.4296769	0.4561965	0.5112585	0.574839

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	x_OP_y	2.057683	1.877886	1.435393	1.336816	1.338075	1.300517

Figure: Benchmarking of x_OP_y() and x_OP_y_R() on double+100x1000+sub data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "*"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb)  max used  (Mb)
Ncells  5404801 288.7    8529671 455.6   8529671 455.6
Vcells 10615352  81.0   39910282 304.5 101881463 777.3

Table: Benchmarking of x_OP_y() and x_OP_y_R() on double+100x1000+mul data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.126562	0.1467355	0.2928854	0.3470035	0.388368	0.435101
1	x_OP_y	0.225885	0.2710410	0.3842160	0.4197910	0.465263	0.530293

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.000000	1.00000	1.00000	1.00000	1.000000	1.000000
1	x_OP_y	1.784777	1.84714	1.31183	1.20976	1.197995	1.218781

Figure: Benchmarking of x_OP_y() and x_OP_y_R() on double+100x1000+mul data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "/"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb)  max used  (Mb)
Ncells  5404864 288.7    8529671 455.6   8529671 455.6
Vcells 10615394  81.0   39910282 304.5 101881463 777.3

Table: Benchmarking of x_OP_y() and x_OP_y_R() on double+100x1000+div data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.138126	0.174116	0.3101232	0.355235	0.3943095	0.524076
1	x_OP_y	0.282957	0.326615	0.4362543	0.462468	0.5260790	0.774445

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	x_OP_y	2.048543	1.875847	1.406713	1.301865	1.334178	1.477734

Figure: Benchmarking of x_OP_y() and x_OP_y_R() on double+100x1000+div data. Outliers are displayed as crosses. Times are in milliseconds.

1000x100 vector

All elements

> x <- data[["1000x100"]]
> y <- x[, 1L]

> OP
[1] "+"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb)  max used  (Mb)
Ncells  5404927 288.7    8529671 455.6   8529671 455.6
Vcells 10616336  81.0   39910282 304.5 101881463 777.3

Table: Benchmarking of x_OP_y() and x_OP_y_R() on double+1000x100+add data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.126812	0.1601815	0.3121611	0.393283	0.4040635	0.442037
1	x_OP_y	0.251481	0.3101260	0.4487157	0.510826	0.5185300	0.622470

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	x_OP_y	1.983101	1.936091	1.437449	1.298876	1.283288	1.408185

Figure: Benchmarking of x_OP_y() and x_OP_y_R() on double+1000x100+add data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "-"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb)  max used  (Mb)
Ncells  5404990 288.7    8529671 455.6   8529671 455.6
Vcells 10617972  81.1   39910282 304.5 101881463 777.3

Table: Benchmarking of x_OP_y() and x_OP_y_R() on double+1000x100+sub data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.137703	0.1617695	0.3061013	0.373124	0.396119	0.45186
1	x_OP_y	0.274140	0.3288765	0.4469109	0.502076	0.528062	0.56548

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.000000	1.000000	1.00000	1.000000	1.000000	1.00000
1	x_OP_y	1.990806	2.032995	1.46001	1.345601	1.333089	1.25145

Figure: Benchmarking of x_OP_y() and x_OP_y_R() on double+1000x100+sub data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "*"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb)  max used  (Mb)
Ncells  5405053 288.7    8529671 455.6   8529671 455.6
Vcells 10618014  81.1   39910282 304.5 101881463 777.3

Table: Benchmarking of x_OP_y() and x_OP_y_R() on double+1000x100+mul data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.126749	0.1527970	0.2885371	0.340066	0.3847800	0.422040
1	x_OP_y	0.223740	0.2767165	0.3884440	0.418240	0.4707875	0.566416

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	x_OP_y	1.765221	1.811007	1.346253	1.229879	1.223524	1.342091

Figure: Benchmarking of x_OP_y() and x_OP_y_R() on double+1000x100+mul data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "/"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb)  max used  (Mb)
Ncells  5405116 288.7    8529671 455.6   8529671 455.6
Vcells 10618056  81.1   39910282 304.5 101881463 777.3

Table: Benchmarking of x_OP_y() and x_OP_y_R() on double+1000x100+div data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.139674	0.1659740	0.2949434	0.3439430	0.3742185	0.443997
1	x_OP_y	0.275361	0.3295195	0.4321805	0.4527835	0.5192995	0.577209

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.000000	1.000000	1.0000	1.000000	1.000000	1.000000
1	x_OP_y	1.971455	1.985368	1.4653	1.316449	1.387691	1.300029

Figure: Benchmarking of x_OP_y() and x_OP_y_R() on double+1000x100+div 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         limma_3.48.3           
[16] glue_1.4.2              digest_0.6.27           XVector_0.32.0         
[19] colorspace_2.0-2        Matrix_1.3-4            XML_3.99-0.7           
[22] pkgconfig_2.0.3         zlibbioc_1.38.0         genefilter_1.74.0      
[25] purrr_0.3.4             ergm_4.1.2              xtable_1.8-4           
[28] scales_1.1.1            tibble_3.1.4            annotate_1.70.0        
[31] KEGGREST_1.32.0         farver_2.1.0            generics_0.1.0         
[34] IRanges_2.26.0          ellipsis_0.3.2          cachem_1.0.6           
[37] withr_2.4.2             BiocGenerics_0.38.0     mime_0.11              
[40] survival_3.2-13         magrittr_2.0.1          crayon_1.4.1           
[43] statnet.common_4.5.0    memoise_2.0.0           laeken_0.5.1           
[46] fansi_0.5.0             R.cache_0.15.0          MASS_7.3-54            
[49] R.rsp_0.44.0            progressr_0.8.0         tools_4.1.1            
[52] lifecycle_1.0.0         S4Vectors_0.30.0        trust_0.1-8            
[55] munsell_0.5.0           tabby_0.0.1-9001        AnnotationDbi_1.54.1   
[58] Biostrings_2.60.2       compiler_4.1.1          GenomeInfoDb_1.28.1    
[61] rlang_0.4.11            grid_4.1.1              RCurl_1.98-1.4         
[64] cwhmisc_6.6             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 1.14 mins.

Reproducibility

To reproduce this report, do:

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