matrixStats.benchmarks

t_tx_OP_y() benchmarks on subsetted computation

This report benchmark the performance of t_tx_OP_y() on subsetted computation.

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

> x <- data[["10x10"]]
> y <- x[, 1L]
> xrows <- sample.int(nrow(x), size = nrow(x) * 0.7)
> xcols <- sample.int(ncol(x), size = ncol(x) * 0.7)
> x_S <- x[xrows, xcols]
> yidxs <- xrows
> y_S <- y[yidxs]

> OP
[1] "+"
> stats <- microbenchmark(t_tx_OP_y_x_y_S = t_tx_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)` = t_tx_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)` = t_tx_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() 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
1	t_tx_OP_y_x_y_S	0.008006	0.008522	0.0091496	0.0088645	0.0090175	0.041126
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.008484	0.009713	0.0100697	0.0099435	0.0102665	0.017104
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.010507	0.011278	0.0116504	0.0115740	0.0118230	0.019484

	expr	min	lq	mean	median	uq	max
1	t_tx_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	1.059705	1.139756	1.100565	1.121722	1.138508	0.4158926
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.312391	1.323398	1.273325	1.305657	1.311117	0.4737636

Figure: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() on integer+10x10+add data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "-"
> stats <- microbenchmark(t_tx_OP_y_x_y_S = t_tx_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)` = t_tx_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)` = t_tx_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() 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
1	t_tx_OP_y_x_y_S	0.004784	0.0051055	0.0052904	0.0052105	0.005376	0.007792
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.005463	0.0058210	0.0062530	0.0059385	0.006095	0.031446
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.006135	0.0066710	0.0068426	0.0067835	0.006934	0.009898

	expr	min	lq	mean	median	uq	max
1	t_tx_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	1.141931	1.140143	1.181954	1.139718	1.133743	4.035678
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.282400	1.306630	1.293411	1.301890	1.289807	1.270277

Figure: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() on integer+10x10+sub data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "*"
> stats <- microbenchmark(t_tx_OP_y_x_y_S = t_tx_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)` = t_tx_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)` = t_tx_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() 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
1	t_tx_OP_y_x_y_S	0.004678	0.0049855	0.0051638	0.0051175	0.0052555	0.006907
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.005428	0.0057410	0.0059596	0.0058375	0.0060240	0.010690
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.006130	0.0064965	0.0069076	0.0065970	0.0067250	0.030523

	expr	min	lq	mean	median	uq	max
1	t_tx_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	1.160325	1.151539	1.154107	1.140694	1.146228	1.547705
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.310389	1.303079	1.337689	1.289106	1.279612	4.419140

Figure: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() on integer+10x10+mul data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "/"
> stats <- microbenchmark(t_tx_OP_y_x_y_S = t_tx_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)` = t_tx_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)` = t_tx_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() 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
1	t_tx_OP_y_x_y_S	0.004330	0.0046460	0.0050623	0.0047290	0.0048250	0.032442
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.004937	0.0052585	0.0054215	0.0053510	0.0054715	0.010890
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.005641	0.0058870	0.0061697	0.0060455	0.0062050	0.012675

	expr	min	lq	mean	median	uq	max
1	t_tx_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.00000	1.0000000
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	1.140185	1.131834	1.070954	1.131529	1.13399	0.3356760
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.302771	1.267111	1.218748	1.278389	1.28601	0.3906972

Figure: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() on integer+10x10+div data. Outliers are displayed as crosses. Times are in milliseconds.

100x100 vector

> x <- data[["100x100"]]
> y <- x[, 1L]
> xrows <- sample.int(nrow(x), size = nrow(x) * 0.7)
> xcols <- sample.int(ncol(x), size = ncol(x) * 0.7)
> x_S <- x[xrows, xcols]
> yidxs <- xrows
> y_S <- y[yidxs]

> OP
[1] "+"
> stats <- microbenchmark(t_tx_OP_y_x_y_S = t_tx_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)` = t_tx_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)` = t_tx_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() 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
1	t_tx_OP_y_x_y_S	0.066932	0.0691910	0.0757164	0.0741085	0.0801365	0.112217
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.070416	0.0728570	0.0800701	0.0782990	0.0872430	0.103558
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.076689	0.0792455	0.0877412	0.0875550	0.0947040	0.108898

	expr	min	lq	mean	median	uq	max
1	t_tx_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	1.052053	1.052984	1.057500	1.056545	1.088680	0.9228370
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.145775	1.145315	1.158813	1.181443	1.181784	0.9704234

Figure: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() on integer+100x100+add data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "-"
> stats <- microbenchmark(t_tx_OP_y_x_y_S = t_tx_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)` = t_tx_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)` = t_tx_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() 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
1	t_tx_OP_y_x_y_S	0.066694	0.0691330	0.0746998	0.0741305	0.0797685	0.098784
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.072225	0.0750385	0.0816482	0.0806880	0.0861390	0.100509
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.076281	0.0791210	0.0866068	0.0847035	0.0914165	0.133863

	expr	min	lq	mean	median	uq	max
1	t_tx_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	1.082931	1.085422	1.093019	1.088459	1.079862	1.017462
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.143746	1.144475	1.159399	1.142627	1.146023	1.355108

Figure: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() on integer+100x100+sub data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "*"
> stats <- microbenchmark(t_tx_OP_y_x_y_S = t_tx_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)` = t_tx_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)` = t_tx_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() 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
1	t_tx_OP_y_x_y_S	0.064442	0.0651885	0.0721604	0.0719295	0.0750505	0.107329
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.070768	0.0747400	0.0814489	0.0817405	0.0851090	0.103906
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.072476	0.0749195	0.0832792	0.0834460	0.0873930	0.100930

	expr	min	lq	mean	median	uq	max
1	t_tx_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	1.098166	1.146521	1.128721	1.136397	1.134023	0.9681074
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.124670	1.149275	1.154086	1.160108	1.164456	0.9403796

Figure: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() on integer+100x100+mul data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "/"
> stats <- microbenchmark(t_tx_OP_y_x_y_S = t_tx_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)` = t_tx_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)` = t_tx_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() 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
1	t_tx_OP_y_x_y_S	0.065146	0.0679705	0.0740844	0.0728435	0.0795950	0.095315
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.068269	0.0708355	0.0766363	0.0757170	0.0795515	0.119250
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.075583	0.0789350	0.0861331	0.0841975	0.0909300	0.130286

	expr	min	lq	mean	median	uq	max
1	t_tx_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.0000000	1.000000
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	1.047939	1.042151	1.034446	1.039448	0.9994535	1.251115
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.160209	1.161313	1.162635	1.155868	1.1424084	1.366899

Figure: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() on integer+100x100+div data. Outliers are displayed as crosses. Times are in milliseconds.

1000x10 vector

> x <- data[["1000x10"]]
> y <- x[, 1L]
> xrows <- sample.int(nrow(x), size = nrow(x) * 0.7)
> xcols <- sample.int(ncol(x), size = ncol(x) * 0.7)
> x_S <- x[xrows, xcols]
> yidxs <- xrows
> y_S <- y[yidxs]

> OP
[1] "+"
> stats <- microbenchmark(t_tx_OP_y_x_y_S = t_tx_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)` = t_tx_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)` = t_tx_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() 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
1	t_tx_OP_y_x_y_S	0.066057	0.0686000	0.0760130	0.073529	0.0820135	0.095486
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.074034	0.0755145	0.0823536	0.080933	0.0877415	0.100243
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.078112	0.0805820	0.0899518	0.089463	0.0967520	0.134497

	expr	min	lq	mean	median	uq	max
1	t_tx_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	1.120759	1.100794	1.083415	1.100695	1.069842	1.049819
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.182494	1.174665	1.183374	1.216704	1.179708	1.408552

Figure: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() on integer+1000x10+add data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "-"
> stats <- microbenchmark(t_tx_OP_y_x_y_S = t_tx_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)` = t_tx_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)` = t_tx_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() 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
1	t_tx_OP_y_x_y_S	0.066100	0.0684990	0.0740375	0.0734035	0.0793115	0.087635
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.074534	0.0769560	0.0834896	0.0832155	0.0885980	0.104208
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.077728	0.0805895	0.0891141	0.0868380	0.0932925	0.135669

	expr	min	lq	mean	median	uq	max
1	t_tx_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	1.127595	1.123462	1.127667	1.133672	1.117089	1.189114
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.175915	1.176506	1.203634	1.183023	1.176280	1.548114

Figure: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() on integer+1000x10+sub data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "*"
> stats <- microbenchmark(t_tx_OP_y_x_y_S = t_tx_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)` = t_tx_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)` = t_tx_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() 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
1	t_tx_OP_y_x_y_S	0.061880	0.064128	0.0682230	0.0665320	0.0716065	0.084668
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.073213	0.075902	0.0828148	0.0816315	0.0880925	0.102955
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.073674	0.076598	0.0854856	0.0850130	0.0916370	0.128992

	expr	min	lq	mean	median	uq	max
1	t_tx_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	1.183145	1.183601	1.213885	1.226951	1.230231	1.215985
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.190595	1.194455	1.253032	1.277776	1.279730	1.523504

Figure: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() on integer+1000x10+mul data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "/"
> stats <- microbenchmark(t_tx_OP_y_x_y_S = t_tx_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)` = t_tx_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)` = t_tx_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() 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
1	t_tx_OP_y_x_y_S	0.062798	0.0651920	0.0706914	0.0682175	0.0752170	0.107769
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.068570	0.0711480	0.0772243	0.0759180	0.0818905	0.120033
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.074435	0.0778645	0.0850382	0.0833675	0.0904330	0.108263

	expr	min	lq	mean	median	uq	max
1	t_tx_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	1.091914	1.091361	1.092415	1.112882	1.088723	1.113799
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.185308	1.194387	1.202951	1.222084	1.202295	1.004584

Figure: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() on integer+1000x10+div data. Outliers are displayed as crosses. Times are in milliseconds.

10x1000 vector

> x <- data[["10x1000"]]
> y <- x[, 1L]
> xrows <- sample.int(nrow(x), size = nrow(x) * 0.7)
> xcols <- sample.int(ncol(x), size = ncol(x) * 0.7)
> x_S <- x[xrows, xcols]
> yidxs <- xrows
> y_S <- y[yidxs]

> OP
[1] "+"
> stats <- microbenchmark(t_tx_OP_y_x_y_S = t_tx_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)` = t_tx_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)` = t_tx_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() 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
1	t_tx_OP_y_x_y_S	0.068107	0.0709745	0.0788262	0.0785585	0.0854270	0.100325
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.072232	0.0748330	0.0810451	0.0802755	0.0862975	0.101410
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.079395	0.0826220	0.0908979	0.0887095	0.0954435	0.138446

	expr	min	lq	mean	median	uq	max
1	t_tx_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	1.060566	1.054365	1.028148	1.021856	1.010190	1.010815
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.165739	1.164108	1.153143	1.129216	1.117252	1.379975

Figure: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() on integer+10x1000+add data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "-"
> stats <- microbenchmark(t_tx_OP_y_x_y_S = t_tx_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)` = t_tx_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)` = t_tx_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() 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
1	t_tx_OP_y_x_y_S	0.068036	0.0709310	0.0771976	0.0759415	0.0792530	0.114920
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.073841	0.0765895	0.0857178	0.0850455	0.0919800	0.134720
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.079270	0.0822740	0.0889209	0.0858770	0.0951435	0.112973

	expr	min	lq	mean	median	uq	max
1	t_tx_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	1.085322	1.079775	1.110369	1.119882	1.160587	1.1722938
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.165118	1.159916	1.151862	1.130831	1.200503	0.9830578

Figure: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() on integer+10x1000+sub data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "*"
> stats <- microbenchmark(t_tx_OP_y_x_y_S = t_tx_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)` = t_tx_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)` = t_tx_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() 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
1	t_tx_OP_y_x_y_S	0.063739	0.065959	0.0717249	0.068604	0.076329	0.111245
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.074685	0.077823	0.0848930	0.083335	0.089863	0.116042
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.073283	0.076066	0.0834703	0.084213	0.091037	0.098852

	expr	min	lq	mean	median	uq	max
1	t_tx_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.171732	1.179869	1.183592	1.214725	1.177311	1.0431210
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	1.149736	1.153232	1.163757	1.227523	1.192692	0.8885972

Figure: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() on integer+10x1000+mul data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "/"
> stats <- microbenchmark(t_tx_OP_y_x_y_S = t_tx_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)` = t_tx_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)` = t_tx_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() 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
1	t_tx_OP_y_x_y_S	0.065512	0.0681810	0.0729350	0.0706970	0.0783065	0.089225
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.070127	0.0729015	0.0791056	0.0780485	0.0840500	0.100898
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.076770	0.0802085	0.0874565	0.0844550	0.0930050	0.139358

	expr	min	lq	mean	median	uq	max
1	t_tx_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	1.070445	1.069235	1.084603	1.103986	1.073346	1.130827
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.171846	1.176405	1.199102	1.194605	1.187705	1.561872

Figure: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() on integer+10x1000+div data. Outliers are displayed as crosses. Times are in milliseconds.

100x1000 vector

> x <- data[["100x1000"]]
> y <- x[, 1L]
> xrows <- sample.int(nrow(x), size = nrow(x) * 0.7)
> xcols <- sample.int(ncol(x), size = ncol(x) * 0.7)
> x_S <- x[xrows, xcols]
> yidxs <- xrows
> y_S <- y[yidxs]

> OP
[1] "+"
> stats <- microbenchmark(t_tx_OP_y_x_y_S = t_tx_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)` = t_tx_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)` = t_tx_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() 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
1	t_tx_OP_y_x_y_S	0.502257	0.5069655	0.5480154	0.529796	0.5446245	0.828126
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.520385	0.5219525	0.5623897	0.549449	0.5642035	0.858174
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.568957	0.5843420	0.6247666	0.600109	0.6169895	1.022752

	expr	min	lq	mean	median	uq	max
1	t_tx_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	1.036093	1.029562	1.026230	1.037095	1.035950	1.036284
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.132801	1.152627	1.140053	1.132717	1.132871	1.235020

Figure: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() on integer+100x1000+add data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "-"
> stats <- microbenchmark(t_tx_OP_y_x_y_S = t_tx_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)` = t_tx_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)` = t_tx_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() 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
1	t_tx_OP_y_x_y_S	0.502344	0.5160055	0.5506338	0.5300335	0.5465385	0.832716
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.532940	0.5364720	0.5759884	0.5625485	0.5730170	0.813455
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.569139	0.5725060	0.6207185	0.6005045	0.6183275	1.020565

	expr	min	lq	mean	median	uq	max
1	t_tx_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	1.060906	1.039663	1.046046	1.061345	1.048448	0.9768697
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.132967	1.109496	1.127280	1.132956	1.131352	1.2255859

Figure: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() on integer+100x1000+sub data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "*"
> stats <- microbenchmark(t_tx_OP_y_x_y_S = t_tx_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)` = t_tx_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)` = t_tx_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() 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
1	t_tx_OP_y_x_y_S	0.470624	0.4726730	0.5089969	0.4963045	0.5050730	0.763992
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.525082	0.5265205	0.5784193	0.5543410	0.5869120	0.910976
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.536984	0.5395150	0.5788292	0.5626990	0.5760505	0.847790

	expr	min	lq	mean	median	uq	max
1	t_tx_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	1.115714	1.113921	1.136391	1.116937	1.162034	1.192389
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.141004	1.141413	1.137196	1.133778	1.140529	1.109684

Figure: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() on integer+100x1000+mul data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "/"
> stats <- microbenchmark(t_tx_OP_y_x_y_S = t_tx_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)` = t_tx_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)` = t_tx_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() 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
1	t_tx_OP_y_x_y_S	0.475115	0.4761720	0.5130269	0.4881745	0.511375	0.799978
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.486354	0.4874705	0.5305048	0.4915220	0.546298	0.886417
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.542580	0.5456425	0.5820149	0.5566305	0.572625	0.895267

	expr	min	lq	mean	median	uq	max
1	t_tx_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	1.023655	1.023728	1.034068	1.006857	1.068292	1.108052
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.141997	1.145894	1.134472	1.140229	1.119775	1.119115

Figure: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() on integer+100x1000+div data. Outliers are displayed as crosses. Times are in milliseconds.

1000x100 vector

> x <- data[["1000x100"]]
> y <- x[, 1L]
> xrows <- sample.int(nrow(x), size = nrow(x) * 0.7)
> xcols <- sample.int(ncol(x), size = ncol(x) * 0.7)
> x_S <- x[xrows, xcols]
> yidxs <- xrows
> y_S <- y[yidxs]

> OP
[1] "+"
> stats <- microbenchmark(t_tx_OP_y_x_y_S = t_tx_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)` = t_tx_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)` = t_tx_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() 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
1	t_tx_OP_y_x_y_S	0.496798	0.4978660	0.5430382	0.523656	0.5364555	0.827474
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.517647	0.5206975	0.5614016	0.546105	0.5558825	0.847570
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.561919	0.5636265	0.6169219	0.592539	0.6093920	0.992817

	expr	min	lq	mean	median	uq	max
1	t_tx_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	1.041967	1.045859	1.033816	1.042870	1.036214	1.024286
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.131081	1.132085	1.136056	1.131542	1.135960	1.199817

Figure: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() on integer+1000x100+add data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "-"
> stats <- microbenchmark(t_tx_OP_y_x_y_S = t_tx_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)` = t_tx_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)` = t_tx_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() 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
1	t_tx_OP_y_x_y_S	0.496900	0.4980255	0.5427837	0.524102	0.5349890	0.834041
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.528310	0.5359485	0.5739539	0.551276	0.5680115	0.928818
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.561811	0.5650790	0.6093841	0.587129	0.5971750	0.936307

	expr	min	lq	mean	median	uq	max
1	t_tx_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	1.063212	1.076147	1.057426	1.051849	1.061726	1.113636
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.130632	1.134639	1.122701	1.120257	1.116238	1.122615

Figure: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() on integer+1000x100+sub data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "*"
> stats <- microbenchmark(t_tx_OP_y_x_y_S = t_tx_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)` = t_tx_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)` = t_tx_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() 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
1	t_tx_OP_y_x_y_S	0.465137	0.4658305	0.5089532	0.4875805	0.5150545	0.767161
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.521206	0.5243455	0.5638187	0.5501035	0.5629515	0.828756
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.529879	0.5354060	0.5808337	0.5591105	0.5732210	0.940286

	expr	min	lq	mean	median	uq	max
1	t_tx_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	1.120543	1.125614	1.107800	1.128231	1.092994	1.080289
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.139189	1.149358	1.141232	1.146704	1.112933	1.225670

Figure: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() on integer+1000x100+mul data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "/"
> stats <- microbenchmark(t_tx_OP_y_x_y_S = t_tx_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)` = t_tx_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)` = t_tx_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() 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
1	t_tx_OP_y_x_y_S	0.468640	0.4701740	0.5026976	0.4740015	0.4985415	0.744639
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.482614	0.4842225	0.5205958	0.4955650	0.5110430	0.821722
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.534387	0.5383485	0.5859340	0.5507170	0.5784035	0.954191

	expr	min	lq	mean	median	uq	max
1	t_tx_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	1.029818	1.029879	1.035604	1.045492	1.025076	1.103517
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.140293	1.144998	1.165579	1.161847	1.160191	1.281414

Figure: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() 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

> x <- data[["10x10"]]
> y <- x[, 1L]
> xrows <- sample.int(nrow(x), size = nrow(x) * 0.7)
> xcols <- sample.int(ncol(x), size = ncol(x) * 0.7)
> x_S <- x[xrows, xcols]
> yidxs <- xrows
> y_S <- y[yidxs]

> OP
[1] "+"
> stats <- microbenchmark(t_tx_OP_y_x_y_S = t_tx_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)` = t_tx_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)` = t_tx_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() 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
1	t_tx_OP_y_x_y_S	0.004714	0.0049125	0.0056089	0.0050425	0.0052415	0.025612
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.005237	0.0055450	0.0059314	0.0056770	0.0058665	0.012713
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.006214	0.0064250	0.0068116	0.0065260	0.0066465	0.027010

	expr	min	lq	mean	median	uq	max
1	t_tx_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	1.110946	1.128753	1.057500	1.125830	1.119241	0.4963689
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.318201	1.307888	1.214421	1.294199	1.268053	1.0545838

Figure: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() on double+10x10+add data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "-"
> stats <- microbenchmark(t_tx_OP_y_x_y_S = t_tx_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)` = t_tx_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)` = t_tx_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() 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
1	t_tx_OP_y_x_y_S	0.004498	0.0047280	0.0051242	0.0048265	0.0050470	0.011457
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.005087	0.0054125	0.0059918	0.0055160	0.0056965	0.031069
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.005697	0.0062290	0.0066761	0.0063600	0.0065630	0.017041

	expr	min	lq	mean	median	uq	max
1	t_tx_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	1.130947	1.144776	1.169331	1.142857	1.128690	2.711792
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.266563	1.317470	1.302876	1.317725	1.300377	1.487388

Figure: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() on double+10x10+sub data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "*"
> stats <- microbenchmark(t_tx_OP_y_x_y_S = t_tx_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)` = t_tx_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)` = t_tx_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() 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
1	t_tx_OP_y_x_y_S	0.004548	0.004943	0.0055518	0.0050475	0.0051940	0.014705
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.005265	0.005580	0.0062827	0.0057295	0.0059225	0.017764
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.006151	0.006441	0.0074242	0.0066065	0.0068025	0.042068

	expr	min	lq	mean	median	uq	max
1	t_tx_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	1.157652	1.128869	1.131650	1.135116	1.140258	1.208025
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.352463	1.303055	1.337262	1.308866	1.309684	2.860796

Figure: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() on double+10x10+mul data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "/"
> stats <- microbenchmark(t_tx_OP_y_x_y_S = t_tx_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)` = t_tx_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)` = t_tx_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() 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
1	t_tx_OP_y_x_y_S	0.004626	0.0048910	0.0052836	0.0050145	0.0051195	0.029232
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.005337	0.0056015	0.0057957	0.0056970	0.0058640	0.011271
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.005829	0.0064110	0.0065931	0.0065175	0.0066550	0.011833

	expr	min	lq	mean	median	uq	max
1	t_tx_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	1.153696	1.145267	1.096911	1.136105	1.145424	0.3855706
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.260052	1.310775	1.247843	1.299731	1.299932	0.4047961

Figure: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() on double+10x10+div data. Outliers are displayed as crosses. Times are in milliseconds.

100x100 vector

> x <- data[["100x100"]]
> y <- x[, 1L]
> xrows <- sample.int(nrow(x), size = nrow(x) * 0.7)
> xcols <- sample.int(ncol(x), size = ncol(x) * 0.7)
> x_S <- x[xrows, xcols]
> yidxs <- xrows
> y_S <- y[yidxs]

> OP
[1] "+"
> stats <- microbenchmark(t_tx_OP_y_x_y_S = t_tx_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)` = t_tx_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)` = t_tx_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() 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
1	t_tx_OP_y_x_y_S	0.059405	0.0618665	0.0665810	0.0645725	0.0697065	0.099230
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.061235	0.0634700	0.0688189	0.0663420	0.0737245	0.094231
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.073301	0.0774260	0.0838199	0.0846460	0.0884860	0.102893

	expr	min	lq	mean	median	uq	max
1	t_tx_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	1.030806	1.025919	1.033611	1.027403	1.057642	0.9496221
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.233920	1.251501	1.258915	1.310868	1.269408	1.0369142

Figure: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() on double+100x100+add data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "-"
> stats <- microbenchmark(t_tx_OP_y_x_y_S = t_tx_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)` = t_tx_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)` = t_tx_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() 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
1	t_tx_OP_y_x_y_S	0.059666	0.0606825	0.0664459	0.0648065	0.071760	0.079209
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.060902	0.0636540	0.0686206	0.0680325	0.072908	0.090080
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.073679	0.0746640	0.0822978	0.0804925	0.088672	0.127537

	expr	min	lq	mean	median	uq	max
1	t_tx_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	1.020715	1.048968	1.032729	1.049779	1.015998	1.137244
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.234857	1.230404	1.238568	1.242044	1.235675	1.610133

Figure: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() on double+100x100+sub data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "*"
> stats <- microbenchmark(t_tx_OP_y_x_y_S = t_tx_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)` = t_tx_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)` = t_tx_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() 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
1	t_tx_OP_y_x_y_S	0.056496	0.0574745	0.0632066	0.0627115	0.067402	0.092435
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.059595	0.0604020	0.0665880	0.0662960	0.071627	0.091246
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.070294	0.0712405	0.0792767	0.0784340	0.085293	0.101729

	expr	min	lq	mean	median	uq	max
1	t_tx_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	1.054853	1.050936	1.053496	1.057159	1.062684	0.9871369
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.244230	1.239515	1.254246	1.250712	1.265437	1.1005463

Figure: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() on double+100x100+mul data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "/"
> stats <- microbenchmark(t_tx_OP_y_x_y_S = t_tx_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)` = t_tx_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)` = t_tx_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() 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
1	t_tx_OP_y_x_y_S	0.056509	0.0571605	0.0625492	0.0616135	0.0677975	0.082179
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.058420	0.0592415	0.0651659	0.0651560	0.0685880	0.109635
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.070515	0.0736210	0.0786674	0.0785320	0.0830820	0.101507

	expr	min	lq	mean	median	uq	max
1	t_tx_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	1.033818	1.036406	1.041834	1.057495	1.011660	1.334100
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.247854	1.287970	1.257688	1.274591	1.225443	1.235194

Figure: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() on double+100x100+div data. Outliers are displayed as crosses. Times are in milliseconds.

1000x10 vector

> x <- data[["1000x10"]]
> y <- x[, 1L]
> xrows <- sample.int(nrow(x), size = nrow(x) * 0.7)
> xcols <- sample.int(ncol(x), size = ncol(x) * 0.7)
> x_S <- x[xrows, xcols]
> yidxs <- xrows
> y_S <- y[yidxs]

> OP
[1] "+"
> stats <- microbenchmark(t_tx_OP_y_x_y_S = t_tx_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)` = t_tx_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)` = t_tx_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() 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
1	t_tx_OP_y_x_y_S	0.057157	0.059307	0.0624272	0.0611975	0.0645890	0.091373
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.061953	0.062901	0.0666082	0.0652140	0.0690740	0.082430
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.072429	0.074459	0.0795758	0.0777880	0.0830935	0.135063

	expr	min	lq	mean	median	uq	max
1	t_tx_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	1.083909	1.060600	1.066974	1.065632	1.069439	0.9021264
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.267194	1.255484	1.274698	1.271098	1.286496	1.4781500

Figure: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() on double+1000x10+add data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "-"
> stats <- microbenchmark(t_tx_OP_y_x_y_S = t_tx_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)` = t_tx_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)` = t_tx_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() 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
1	t_tx_OP_y_x_y_S	0.057743	0.0597300	0.0640394	0.0625225	0.0676810	0.081327
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.062836	0.0648925	0.0696485	0.0682905	0.0741095	0.084641
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.072923	0.0759840	0.0824823	0.0808950	0.0876560	0.126929

	expr	min	lq	mean	median	uq	max
1	t_tx_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	1.088201	1.086431	1.087588	1.092255	1.094982	1.040749
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.262889	1.272125	1.287994	1.293854	1.295135	1.560724

Figure: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() on double+1000x10+sub data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "*"
> stats <- microbenchmark(t_tx_OP_y_x_y_S = t_tx_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)` = t_tx_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)` = t_tx_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() 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
1	t_tx_OP_y_x_y_S	0.055858	0.0564525	0.0605266	0.0603505	0.0625115	0.076095
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.062579	0.0636030	0.0694847	0.0679820	0.0733450	0.092625
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.071663	0.0754435	0.0816792	0.0801010	0.0865350	0.129606

	expr	min	lq	mean	median	uq	max
1	t_tx_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	1.120323	1.126664	1.148003	1.126453	1.173304	1.217229
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.282950	1.336407	1.349476	1.327263	1.384305	1.703213

Figure: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() on double+1000x10+mul data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "/"
> stats <- microbenchmark(t_tx_OP_y_x_y_S = t_tx_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)` = t_tx_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)` = t_tx_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() 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
1	t_tx_OP_y_x_y_S	0.055835	0.0564040	0.0614545	0.0605510	0.0651060	0.088472
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.061474	0.0628315	0.0686917	0.0669705	0.0719815	0.117751
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.071874	0.0743395	0.0802377	0.0797800	0.0859925	0.095653

	expr	min	lq	mean	median	uq	max
1	t_tx_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	1.100994	1.113955	1.117765	1.106018	1.105605	1.330941
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.287257	1.317983	1.305644	1.317567	1.320808	1.081167

Figure: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() on double+1000x10+div data. Outliers are displayed as crosses. Times are in milliseconds.

10x1000 vector

> x <- data[["10x1000"]]
> y <- x[, 1L]
> xrows <- sample.int(nrow(x), size = nrow(x) * 0.7)
> xcols <- sample.int(ncol(x), size = ncol(x) * 0.7)
> x_S <- x[xrows, xcols]
> yidxs <- xrows
> y_S <- y[yidxs]

> OP
[1] "+"
> stats <- microbenchmark(t_tx_OP_y_x_y_S = t_tx_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)` = t_tx_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)` = t_tx_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() 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
1	t_tx_OP_y_x_y_S	0.058458	0.0611545	0.0666242	0.0655860	0.0715000	0.086094
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.060351	0.0629610	0.0687001	0.0688980	0.0727810	0.085164
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.074641	0.0773930	0.0847887	0.0832835	0.0893485	0.133132

	expr	min	lq	mean	median	uq	max
1	t_tx_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	1.032382	1.029540	1.031159	1.050499	1.017916	0.9891979
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.276831	1.265532	1.272642	1.269837	1.249629	1.5463563

Figure: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() on double+10x1000+add data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "-"
> stats <- microbenchmark(t_tx_OP_y_x_y_S = t_tx_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)` = t_tx_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)` = t_tx_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() 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
1	t_tx_OP_y_x_y_S	0.058380	0.0608580	0.0671174	0.0648325	0.0715235	0.104256
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.061885	0.0640030	0.0707960	0.0689855	0.0756160	0.102276
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.074839	0.0773815	0.0845697	0.0824650	0.0892275	0.133227

	expr	min	lq	mean	median	uq	max
1	t_tx_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	1.060038	1.051678	1.054808	1.064057	1.057219	0.9810083
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.281929	1.271509	1.260026	1.271970	1.247527	1.2778833

Figure: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() on double+10x1000+sub data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "*"
> stats <- microbenchmark(t_tx_OP_y_x_y_S = t_tx_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)` = t_tx_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)` = t_tx_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() 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
1	t_tx_OP_y_x_y_S	0.056406	0.0586555	0.0632272	0.0621860	0.0670475	0.091878
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.060612	0.0630995	0.0685041	0.0674890	0.0726550	0.083026
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.073175	0.0747265	0.0812757	0.0798125	0.0858800	0.107787

	expr	min	lq	mean	median	uq	max
1	t_tx_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	1.074566	1.075764	1.083459	1.085276	1.083635	0.9036548
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.297291	1.273990	1.285454	1.283448	1.280883	1.1731535

Figure: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() on double+10x1000+mul data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "/"
> stats <- microbenchmark(t_tx_OP_y_x_y_S = t_tx_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)` = t_tx_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)` = t_tx_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() 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
1	t_tx_OP_y_x_y_S	0.055728	0.0580025	0.0624254	0.0615695	0.0661410	0.087024
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.059503	0.0613890	0.0664289	0.0651885	0.0704515	0.085297
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.072403	0.0743080	0.0816066	0.0794055	0.0880050	0.128598

	expr	min	lq	mean	median	uq	max
1	t_tx_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	1.067740	1.058385	1.064132	1.058779	1.065171	0.9801549
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.299221	1.281117	1.307264	1.289689	1.330566	1.4777303

Figure: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() on double+10x1000+div data. Outliers are displayed as crosses. Times are in milliseconds.

100x1000 vector

> x <- data[["100x1000"]]
> y <- x[, 1L]
> xrows <- sample.int(nrow(x), size = nrow(x) * 0.7)
> xcols <- sample.int(ncol(x), size = ncol(x) * 0.7)
> x_S <- x[xrows, xcols]
> yidxs <- xrows
> y_S <- y[yidxs]

> OP
[1] "+"
> stats <- microbenchmark(t_tx_OP_y_x_y_S = t_tx_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)` = t_tx_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)` = t_tx_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() 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	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.432330	0.433116	0.4732650	0.4370160	0.5003515	0.735591
1	t_tx_OP_y_x_y_S	0.429102	0.430526	0.4806906	0.4421985	0.5321930	0.714597
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.524408	0.527738	0.6071238	0.5442675	0.6784020	0.984593

	expr	min	lq	mean	median	uq	max
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	1.0000000	1.0000000	1.000000	1.000000	1.000000	1.0000000
1	t_tx_OP_y_x_y_S	0.9925335	0.9940201	1.015690	1.011859	1.063638	0.9714597
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.2129808	1.2184680	1.282841	1.245418	1.355851	1.3385060

Figure: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() on double+100x1000+add data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "-"
> stats <- microbenchmark(t_tx_OP_y_x_y_S = t_tx_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)` = t_tx_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)` = t_tx_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() 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
1	t_tx_OP_y_x_y_S	0.431633	0.4326715	0.4726695	0.4436840	0.4694535	0.720843
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.432074	0.4335790	0.4656297	0.4443340	0.4568640	0.695127
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.528133	0.5297385	0.5790080	0.5428995	0.5837540	1.005175

	expr	min	lq	mean	median	uq	max
1	t_tx_OP_y_x_y_S	1.000000	1.000000	1.0000000	1.000000	1.0000000	1.0000000
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	1.001022	1.002097	0.9851062	1.001465	0.9731826	0.9643251
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.223570	1.224343	1.2249742	1.223618	1.2434757	1.3944437

Figure: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() on double+100x1000+sub data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "*"
> stats <- microbenchmark(t_tx_OP_y_x_y_S = t_tx_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)` = t_tx_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)` = t_tx_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() 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
1	t_tx_OP_y_x_y_S	0.406550	0.4075720	0.4421570	0.4184760	0.4458260	0.693402
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.421291	0.4224940	0.4691252	0.4348985	0.5016745	0.818105
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.503035	0.5048115	0.5477064	0.5174640	0.5481755	0.833620

	expr	min	lq	mean	median	uq	max
1	t_tx_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	1.036259	1.036612	1.060992	1.039244	1.125270	1.179842
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.237326	1.238582	1.238715	1.236544	1.229573	1.202217

Figure: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() on double+100x1000+mul data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "/"
> stats <- microbenchmark(t_tx_OP_y_x_y_S = t_tx_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)` = t_tx_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)` = t_tx_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() 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	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.412818	0.413487	0.4546329	0.4153910	0.4882285	0.825380
1	t_tx_OP_y_x_y_S	0.407737	0.408645	0.4425742	0.4185355	0.4407200	0.680413
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.503711	0.505875	0.5433715	0.5177320	0.5357835	0.799942

	expr	min	lq	mean	median	uq	max
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	1.0000000	1.0000000	1.0000000	1.000000	1.0000000	1.0000000
1	t_tx_OP_y_x_y_S	0.9876919	0.9882898	0.9734761	1.007570	0.9026921	0.8243633
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.2201769	1.2234363	1.1951874	1.246373	1.0974032	0.9691803

Figure: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() on double+100x1000+div data. Outliers are displayed as crosses. Times are in milliseconds.

1000x100 vector

> x <- data[["1000x100"]]
> y <- x[, 1L]
> xrows <- sample.int(nrow(x), size = nrow(x) * 0.7)
> xcols <- sample.int(ncol(x), size = ncol(x) * 0.7)
> x_S <- x[xrows, xcols]
> yidxs <- xrows
> y_S <- y[yidxs]

> OP
[1] "+"
> stats <- microbenchmark(t_tx_OP_y_x_y_S = t_tx_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)` = t_tx_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)` = t_tx_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() 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
1	t_tx_OP_y_x_y_S	0.424907	0.4263200	0.4688398	0.4367385	0.4662495	0.710059
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.431651	0.4334905	0.4705902	0.4455935	0.4693495	0.708392
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.521345	0.5237510	0.5799121	0.5468185	0.5955425	0.985367

	expr	min	lq	mean	median	uq	max
1	t_tx_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	1.015872	1.016819	1.003734	1.020275	1.006649	0.9976523
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.226963	1.228540	1.236909	1.252050	1.277304	1.3877255

Figure: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() on double+1000x100+add data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "-"
> stats <- microbenchmark(t_tx_OP_y_x_y_S = t_tx_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)` = t_tx_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)` = t_tx_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() 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	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.432042	0.4348405	0.4762044	0.4510435	0.4877480	0.847998
1	t_tx_OP_y_x_y_S	0.427418	0.4298330	0.4730982	0.4511940	0.4646825	0.730370
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.523119	0.5282610	0.5789253	0.5526905	0.5880585	0.928281

	expr	min	lq	mean	median	uq	max
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	1.0000000	1.0000000	1.0000000	1.000000	1.0000000	1.0000000
1	t_tx_OP_y_x_y_S	0.9892973	0.9884843	0.9934771	1.000334	0.9527102	0.8612874
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.2108059	1.2148385	1.2157074	1.225360	1.2056605	1.0946736

Figure: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() on double+1000x100+sub data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "*"
> stats <- microbenchmark(t_tx_OP_y_x_y_S = t_tx_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)` = t_tx_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)` = t_tx_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() 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
1	t_tx_OP_y_x_y_S	0.402175	0.4033580	0.4513188	0.4249460	0.4736370	0.683383
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.421028	0.4235420	0.4635841	0.4449125	0.4656605	0.679354
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.499376	0.5066195	0.5584307	0.5267170	0.5751920	0.959359

	expr	min	lq	mean	median	uq	max
1	t_tx_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	1.046878	1.050040	1.027177	1.046986	0.983159	0.9941043
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.241688	1.256005	1.237331	1.239492	1.214415	1.4038380

Figure: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() on double+1000x100+mul data. Outliers are displayed as crosses. Times are in milliseconds.

> OP
[1] "/"
> stats <- microbenchmark(t_tx_OP_y_x_y_S = t_tx_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)` = t_tx_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)` = t_tx_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() 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
1	t_tx_OP_y_x_y_S	0.402566	0.4038925	0.4436665	0.4166555	0.4505205	0.867445
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.414122	0.4162915	0.4613479	0.4291695	0.4527780	0.932231
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.499078	0.5057565	0.5706905	0.5272940	0.5823695	1.103565

	expr	min	lq	mean	median	uq	max
1	t_tx_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	1.028706	1.030699	1.039853	1.030034	1.005011	1.074686
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.239742	1.252206	1.286305	1.265540	1.292659	1.272202

Figure: Benchmarking of t_tx_OP_y_x_y_S(), t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)() and t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)() 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         glue_1.4.2             
[16] digest_0.6.27           XVector_0.32.0          colorspace_2.0-2       
[19] Matrix_1.3-4            XML_3.99-0.7            pkgconfig_2.0.3        
[22] zlibbioc_1.38.0         genefilter_1.74.0       purrr_0.3.4            
[25] ergm_4.1.2              xtable_1.8-4            scales_1.1.1           
[28] tibble_3.1.4            annotate_1.70.0         KEGGREST_1.32.0        
[31] farver_2.1.0            generics_0.1.0          IRanges_2.26.0         
[34] ellipsis_0.3.2          cachem_1.0.6            withr_2.4.2            
[37] BiocGenerics_0.38.0     mime_0.11               survival_3.2-13        
[40] magrittr_2.0.1          crayon_1.4.1            statnet.common_4.5.0   
[43] memoise_2.0.0           laeken_0.5.1            fansi_0.5.0            
[46] R.cache_0.15.0          MASS_7.3-54             R.rsp_0.44.0           
[49] progressr_0.8.0         tools_4.1.1             lifecycle_1.0.0        
[52] S4Vectors_0.30.0        trust_0.1-8             munsell_0.5.0          
[55] tabby_0.0.1-9001        AnnotationDbi_1.54.1    Biostrings_2.60.2      
[58] compiler_4.1.1          GenomeInfoDb_1.28.1     rlang_0.4.11           
[61] grid_4.1.1              RCurl_1.98-1.4          cwhmisc_6.6            
[64] rappdirs_0.3.3          startup_0.15.0          labeling_0.4.2         
[67] bitops_1.0-7            base64enc_0.1-3         boot_1.3-28            
[70] gtable_0.3.0            DBI_1.1.1               markdown_1.1           
[73] R6_2.5.1                lpSolveAPI_5.5.2.0-17.7 rle_0.9.2              
[76] dplyr_1.0.7             fastmap_1.1.0           bit_4.0.4              
[79] utf8_1.2.2              parallel_4.1.1          Rcpp_1.0.7             
[82] vctrs_0.3.8             png_0.1-7               DEoptimR_1.0-9         
[85] tidyselect_1.1.1        xfun_0.25               coda_0.19-4            

Total processing time was 46.66 secs.

Reproducibility

To reproduce this report, do:

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