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	0.004839	0.0050135	0.0058614	0.005122	0.0052830	0.060994
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.005389	0.0057385	0.0061359	0.005862	0.0060440	0.014533
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.006309	0.0065325	0.0069614	0.006675	0.0068355	0.015305

	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)	1.113660	1.144610	1.046836	1.144475	1.144047	0.2382693
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.303782	1.302982	1.187681	1.303202	1.293867	0.2509263

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	0.004527	0.0048295	0.0051050	0.004960	0.0051625	0.014021
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.005220	0.0055485	0.0062074	0.005671	0.0058415	0.046896
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.005886	0.0063725	0.0066266	0.006511	0.0066830	0.010664

	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)	1.153082	1.148877	1.215937	1.143347	1.131525	3.3446972
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.300199	1.319495	1.298040	1.312702	1.294528	0.7605734

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	0.004579	0.0048560	0.0050149	0.0049370	0.0051145	0.006377
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.005224	0.0055665	0.0058959	0.0056700	0.0058600	0.022449
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.006011	0.0064075	0.0069095	0.0066025	0.0067895	0.032793

	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)	1.140860	1.146314	1.175682	1.148471	1.145762	3.520307
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.312732	1.319502	1.377814	1.337351	1.327500	5.142387

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	0.004506	0.0047470	0.0053508	0.0048385	0.0050400	0.043999
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.005184	0.0054690	0.0057309	0.0055745	0.0057585	0.016683
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.005945	0.0062255	0.0065504	0.0064080	0.0066550	0.011993

	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)	1.150466	1.152096	1.071028	1.152113	1.142559	0.3791677
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.319352	1.311460	1.224190	1.324377	1.320437	0.2725744

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	0.060427	0.0652495	0.0705450	0.0701415	0.076292	0.109903
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.071748	0.0770135	0.0834709	0.0835965	0.090464	0.107146
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.070509	0.0758355	0.0836503	0.0846060	0.088866	0.116212

	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)	1.187350	1.180293	1.183230	1.191826	1.185760	0.9749142
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.166846	1.162239	1.185773	1.206219	1.164814	1.0574052

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	0.062553	0.0673105	0.0735584	0.0733085	0.0767820	0.118695
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.072739	0.0783500	0.0868394	0.0855715	0.0924540	0.139485
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.074338	0.0805200	0.0888135	0.0904030	0.0935005	0.116683

	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	1.000000
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.162838	1.164009	1.180551	1.167279	1.20411	1.175155
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	1.188400	1.196247	1.207388	1.233186	1.21774	0.983049

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	0.062556	0.0672915	0.0737330	0.073308	0.0770635	0.117408
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.074174	0.0799835	0.0878412	0.086875	0.0933550	0.145132
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.072942	0.0757775	0.0844021	0.087412	0.0900125	0.101134

	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)	1.185722	1.188612	1.191342	1.185069	1.211404	1.2361338
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.166027	1.126108	1.144699	1.192394	1.168030	0.8613893

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	0.056433	0.0591840	0.0630156	0.0616570	0.0667595	0.079457
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.062022	0.0651985	0.0697508	0.0679340	0.0735220	0.104592
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.068090	0.0712085	0.0761739	0.0762635	0.0797580	0.095559

	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)	1.099038	1.101624	1.106883	1.101805	1.101296	1.316335
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.206563	1.203172	1.208811	1.236899	1.194706	1.202651

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	0.059878	0.0642155	0.0692556	0.067222	0.0753045	0.107632
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.073212	0.0739040	0.0816868	0.079216	0.0887145	0.100477
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.072088	0.0736580	0.0827625	0.080639	0.0903600	0.139024

	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)	1.222686	1.150875	1.179498	1.178424	1.178077	0.9335235
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.203915	1.147044	1.195030	1.199592	1.199928	1.2916605

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	0.060138	0.0624440	0.0683734	0.065976	0.0743690	0.095672
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.073439	0.0761675	0.0838160	0.080469	0.0922065	0.123136
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.072243	0.0755750	0.0838730	0.082303	0.0910575	0.137714

	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)	1.221175	1.219773	1.225858	1.219671	1.239851	1.287064
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.201287	1.210284	1.226691	1.247469	1.224401	1.439439

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	0.060141	0.0659525	0.0691196	0.0678685	0.0725495	0.083481
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.073878	0.0816365	0.0855777	0.0851410	0.0922085	0.098057
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.073009	0.0808140	0.0863548	0.0873230	0.0908820	0.128391

	expr	min	lq	mean	median	uq	max
1	t_tx_OP_y_x_y_S	1.000000	1.000000	1.000000	1.00000	1.000000	1.000000
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	1.228413	1.237807	1.238111	1.25450	1.270974	1.174603
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.213964	1.225336	1.249354	1.28665	1.252690	1.537967

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	0.052509	0.0548175	0.0602619	0.060235	0.0658810	0.071399
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.060365	0.0648260	0.0699844	0.069716	0.0746405	0.112055
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.064654	0.0697945	0.0759714	0.075630	0.0816635	0.090867

	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)	1.149612	1.182579	1.161336	1.157400	1.132959	1.569420
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.231294	1.273216	1.260687	1.255582	1.239561	1.272665

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	0.061382	0.0644760	0.0708796	0.0703615	0.0775525	0.088531
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.072925	0.0762790	0.0834276	0.0815495	0.0916125	0.130821
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.073532	0.0752435	0.0833186	0.0824880	0.0899710	0.104942

	expr	min	lq	mean	median	uq	max
1	t_tx_OP_y_x_y_S	1.000000	1.00000	1.000000	1.000000	1.000000	1.000000
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.188052	1.18306	1.177033	1.159007	1.181296	1.477686
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	1.197941	1.16700	1.175495	1.172346	1.160130	1.185370

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	0.060560	0.0630490	0.0691737	0.0679595	0.0749940	0.099520
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.071770	0.0744840	0.0816013	0.0809460	0.0900350	0.095746
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.073676	0.0769405	0.0856078	0.0856040	0.0929025	0.109123

	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
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.185106	1.181367	1.179658	1.191092	1.200563	0.962078
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	1.216579	1.220329	1.237577	1.259633	1.238799	1.096493

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	0.061695	0.0663655	0.0717965	0.0694425	0.0775190	0.112296
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.073594	0.0786705	0.0885077	0.0849105	0.0923745	0.139794
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.074083	0.0798530	0.0901737	0.0891080	0.0931750	0.140563

	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
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.192868	1.185413	1.232758	1.222745	1.191637	1.244871
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	1.200794	1.203231	1.255962	1.283191	1.201963	1.251719

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	0.053779	0.0580065	0.0628827	0.062581	0.0674460	0.079259
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.060473	0.0645585	0.0702413	0.070128	0.0756015	0.088779
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.065780	0.0707940	0.0777507	0.077749	0.0824990	0.122063

	expr	min	lq	mean	median	uq	max
1	t_tx_OP_y_x_y_S	1.000000	1.000000	1.00000	1.000000	1.000000	1.000000
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	1.124472	1.112953	1.11702	1.120596	1.120919	1.120113
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.223154	1.220449	1.23644	1.242374	1.223186	1.540052

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	0.442223	0.4427690	0.4864545	0.4439255	0.4951345	0.770633
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.508559	0.5098095	0.5628570	0.5113210	0.5531765	0.965419
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.525689	0.5263810	0.5673450	0.5274805	0.5439225	0.914107

	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
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.150006	1.151412	1.157060	1.151817	1.117225	1.252761
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	1.188742	1.188839	1.166286	1.188219	1.098535	1.186177

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	0.440894	0.4418335	0.4831281	0.4423455	0.5070895	0.731417
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.507351	0.5090810	0.5607251	0.5109395	0.5757610	0.920984
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.525975	0.5266600	0.5697822	0.5286300	0.5528420	0.905270

	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
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.150732	1.152201	1.160614	1.155069	1.135423	1.259178
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	1.192974	1.191987	1.179360	1.195061	1.090226	1.237693

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	0.442185	0.4427660	0.4812746	0.4433905	0.4879385	0.748343
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.508983	0.5102655	0.5544187	0.5122305	0.5475920	0.834952
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.525674	0.5264160	0.5887506	0.5289195	0.6425875	0.925014

	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
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.151064	1.152450	1.151980	1.155258	1.122256	1.115734
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	1.188810	1.188926	1.223315	1.192898	1.316944	1.236083

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	0.383379	0.3840575	0.4303420	0.3852235	0.4552010	0.647047
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.419385	0.4201300	0.4853537	0.4243440	0.5397575	0.787023
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.450225	0.4522365	0.5021044	0.4543885	0.5049700	1.003035

	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)	1.093918	1.093925	1.127833	1.101553	1.185756	1.216330
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.174360	1.177523	1.166757	1.179545	1.109334	1.550173

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	0.437524	0.4381080	0.4810173	0.4394085	0.4904385	0.750611
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.502471	0.5036705	0.5574160	0.5052555	0.5718145	1.009826
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.521555	0.5223940	0.5713188	0.5253230	0.5842815	0.853970

	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
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.148442	1.149649	1.158827	1.149854	1.165925	1.345339
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	1.192060	1.192386	1.187730	1.195523	1.191345	1.137700

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	0.439078	0.4401030	0.4928529	0.4509720	0.5241445	0.764198
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.504546	0.5088070	0.5583792	0.5228385	0.5694745	0.845744
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.521569	0.5229075	0.5759360	0.5340430	0.6095910	0.927927

	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
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.149103	1.156109	1.132953	1.159359	1.086484	1.106708
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	1.187873	1.188148	1.168576	1.184204	1.163021	1.214249

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	0.437606	0.4383500	0.4813024	0.4434955	0.5029225	0.682179
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.502695	0.5040935	0.5559664	0.5091195	0.5495360	1.228836
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.521664	0.5222845	0.5634080	0.5237945	0.5644675	0.936265

	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
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.148739	1.149979	1.155129	1.147970	1.092685	1.801340
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	1.192086	1.191478	1.170591	1.181059	1.122375	1.372462

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	0.379070	0.3796205	0.4160531	0.3814415	0.4359155	0.625866
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.418130	0.4204610	0.4630495	0.4233355	0.4629240	0.895336
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.444949	0.4464470	0.5019800	0.4488990	0.5313185	0.835814

	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)	1.103042	1.107582	1.112958	1.109831	1.061958	1.430555
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.173791	1.176035	1.206529	1.176849	1.218857	1.335452

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	0.004546	0.0046730	0.0054554	0.0048110	0.0049995	0.059004
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.005079	0.0053370	0.0055913	0.0054765	0.0056645	0.012999
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.006036	0.0062855	0.0066550	0.0064445	0.0066555	0.015726

	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)	1.117246	1.142093	1.024904	1.138329	1.133013	0.2203071
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.327761	1.345067	1.219878	1.339534	1.331233	0.2665243

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	0.004282	0.0046900	0.0049209	0.0048355	0.005007	0.008101
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.005003	0.0053220	0.0059091	0.0054285	0.005598	0.040806
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.005994	0.0062415	0.0064669	0.0064105	0.006593	0.009215

	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)	1.168379	1.134755	1.200820	1.122635	1.118035	5.037156
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.399813	1.330810	1.314182	1.325716	1.316757	1.137514

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	0.004493	0.004758	0.0049441	0.0048735	0.0050790	0.007074
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.005050	0.005377	0.0056373	0.0054865	0.0056775	0.010517
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.006039	0.006376	0.0068678	0.0065060	0.0066445	0.036697

	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)	1.123971	1.130097	1.140192	1.125782	1.117838	1.486712
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.344091	1.340059	1.389077	1.334975	1.308230	5.187588

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	0.004433	0.0046670	0.005219	0.0047800	0.0049890	0.040087
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.005074	0.0053285	0.005565	0.0054275	0.0056280	0.011366
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.005773	0.0062325	0.006485	0.0063995	0.0066275	0.011076

	expr	min	lq	mean	median	uq	max
1	t_tx_OP_y_x_y_S	1.000000	1.00000	1.000000	1.000000	1.000000	1.0000000
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	1.144597	1.14174	1.066298	1.135460	1.128082	0.2835333
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.302278	1.33544	1.242568	1.338807	1.328423	0.2762990

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	0.049975	0.0538255	0.0568394	0.0561895	0.0609210	0.093189
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.056273	0.0603410	0.0643460	0.0633030	0.0686910	0.101200
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.064552	0.0697275	0.0746775	0.0732195	0.0787125	0.103356

	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)	1.126023	1.121049	1.132066	1.126598	1.127542	1.085965
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.291686	1.295436	1.313832	1.303082	1.292042	1.109101

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	0.048161	0.0501965	0.0546075	0.0539135	0.0585470	0.072487
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.054366	0.0586430	0.0626809	0.0630715	0.0663705	0.076306
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.062291	0.0647715	0.0711381	0.0692065	0.0756895	0.114205

	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)	1.128839	1.168269	1.147845	1.169865	1.133628	1.052685
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.293391	1.290359	1.302717	1.283658	1.292799	1.575524

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	0.048450	0.0518955	0.0559085	0.054417	0.0589170	0.102610
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.054456	0.0585370	0.0628715	0.061106	0.0676395	0.095720
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.062415	0.0652145	0.0721070	0.072196	0.0773345	0.132028

	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)	1.123963	1.127978	1.124543	1.122921	1.148047	0.9328525
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.288235	1.256650	1.289731	1.326718	1.312601	1.2866972

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	0.046763	0.0474120	0.0526433	0.0507770	0.0564300	0.102233
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.054534	0.0556715	0.0612684	0.0592955	0.0644155	0.118173
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.060906	0.0632220	0.0686207	0.0679985	0.0733440	0.087320

	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)	1.166178	1.174207	1.163841	1.167763	1.141512	1.1559183
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.302440	1.333460	1.303503	1.339160	1.299734	0.8541273

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	0.048505	0.0505245	0.0538257	0.0526055	0.0569635	0.06867
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.056467	0.0569990	0.0615247	0.0609280	0.0656010	0.07920
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.064554	0.0664695	0.0726736	0.0698965	0.0755860	0.14476

	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)	1.164148	1.128146	1.143035	1.158206	1.151632	1.153342
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.330873	1.315589	1.350165	1.328692	1.326920	2.108053

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	0.049263	0.0515310	0.0557748	0.0543810	0.0584720	0.099491
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.056536	0.0588565	0.0638625	0.0631425	0.0680310	0.106728
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.064634	0.0694685	0.0762673	0.0750755	0.0797465	0.146635

	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)	1.147636	1.142157	1.145007	1.161113	1.163480	1.072740
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.312019	1.348091	1.367416	1.380546	1.363841	1.473852

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	0.056095	0.0589065	0.0722649	0.0626245	0.090119	0.105123
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.065214	0.0700645	0.0867841	0.0721105	0.110586	0.164214
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.074760	0.0813580	0.0964002	0.0823595	0.120177	0.159534

	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)	1.162564	1.189419	1.200916	1.151474	1.227111	1.562113
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.332739	1.381138	1.333983	1.315132	1.333537	1.517594

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	0.048422	0.0505580	0.0545313	0.0542715	0.0587155	0.078138
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.058706	0.0615925	0.0678611	0.0659500	0.0714640	0.112189
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.064829	0.0694390	0.0750699	0.0748480	0.0790660	0.102173

	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)	1.212383	1.218254	1.244444	1.215187	1.217123	1.435780
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.338834	1.373452	1.376640	1.379140	1.346595	1.307597

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	0.047245	0.0525450	0.0553080	0.0548110	0.0593315	0.071047
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.056300	0.0606885	0.0656068	0.0656490	0.0710595	0.093333
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.063640	0.0697045	0.0745843	0.0737315	0.0797685	0.118465

	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)	1.191661	1.154981	1.186208	1.197734	1.197669	1.313680
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.347021	1.326568	1.348527	1.345195	1.344455	1.667417

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	0.047104	0.0518165	0.0550787	0.0544565	0.0588840	0.085060
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.057113	0.0617175	0.0675463	0.0665635	0.0718080	0.095088
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.063323	0.0679205	0.0734536	0.0731165	0.0791765	0.092179

	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)	1.212487	1.191078	1.226361	1.222324	1.219482	1.117893
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.344323	1.310789	1.333612	1.342659	1.344618	1.083694

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	0.050613	0.0536790	0.0581531	0.0572785	0.0608260	0.088474
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.059709	0.0633380	0.0672023	0.0657980	0.0699665	0.111949
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.068367	0.0732195	0.0777656	0.0751085	0.0801315	0.124477

	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)	1.179717	1.179940	1.155610	1.148738	1.150273	1.265332
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.350779	1.364025	1.337257	1.311286	1.317389	1.406933

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	0.047106	0.0508545	0.0542101	0.0532610	0.0574825	0.072643
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.055514	0.0597390	0.0641426	0.0645310	0.0685245	0.074930
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.063844	0.0682475	0.0741906	0.0738605	0.0799115	0.120184

	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)	1.178491	1.174704	1.183224	1.211599	1.192093	1.031483
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.355326	1.342015	1.368576	1.386765	1.390188	1.654447

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
1	t_tx_OP_y_x_y_S	0.348075	0.3490700	0.4104105	0.4004995	0.4625495	0.578555
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.390028	0.3906425	0.4435472	0.4014530	0.5053280	0.646086
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.444624	0.4471725	0.5472265	0.4964975	0.6781905	0.846435

	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)	1.120529	1.119095	1.080740	1.002381	1.092484	1.116724
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.277380	1.281040	1.333364	1.239696	1.466201	1.463016

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	0.346338	0.3471270	0.3979004	0.3557510	0.4362555	0.673924
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.388846	0.3905450	0.4434977	0.4073530	0.4566925	0.798883
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.442589	0.4456995	0.5112246	0.4690385	0.5371475	0.966125

	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)	1.122736	1.125078	1.114595	1.145051	1.046846	1.185420
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.277911	1.283967	1.284806	1.318446	1.231268	1.433582

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	0.348064	0.3511070	0.3956803	0.3781955	0.4232820	0.603475
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.389184	0.3909575	0.4518263	0.4174880	0.4910790	0.772586
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.444185	0.4525845	0.5180124	0.4967265	0.5661515	0.729619

	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)	1.118139	1.113500	1.141897	1.103895	1.160170	1.280229
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.276159	1.289022	1.309169	1.313412	1.337528	1.209029

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
1	t_tx_OP_y_x_y_S	0.335606	0.3373330	0.3706286	0.3445740	0.3954670	0.547959
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.390150	0.3921210	0.4387449	0.3996455	0.4724825	1.165456
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.431103	0.4344625	0.4735624	0.4445845	0.4867870	0.690457

	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)	1.162524	1.162415	1.183786	1.159825	1.194746	2.126904
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.284551	1.287934	1.277728	1.290244	1.230917	1.260052

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	0.349554	0.3501865	0.3918041	0.3557000	0.4271525	0.582189
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.390369	0.3910525	0.4379990	0.3942280	0.4689600	0.654227
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.446108	0.4480705	0.5040946	0.4582485	0.5452200	0.866499

	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)	1.116763	1.116698	1.117903	1.108316	1.097875	1.123736
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.276221	1.279520	1.286598	1.288301	1.276406	1.488347

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
1	t_tx_OP_y_x_y_S	0.347878	0.3488850	0.3868300	0.3511655	0.4058795	0.602526
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.388801	0.3896885	0.4300424	0.3904860	0.4603555	0.786687
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.444393	0.4467095	0.4968690	0.4541630	0.5267485	0.775208

	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)	1.117636	1.116954	1.111709	1.111971	1.134217	1.305648
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.277439	1.280392	1.284463	1.293302	1.297795	1.286597

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	0.349571	0.3502475	0.3975803	0.3543405	0.4407550	0.587000
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.390430	0.3909675	0.4274288	0.3916075	0.4306250	0.641149
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.446028	0.4476725	0.5011140	0.4494010	0.5286215	0.883178

	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)	1.116883	1.116261	1.075075	1.105173	0.9770167	1.092247
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.275930	1.278160	1.260409	1.268274	1.1993545	1.504562

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	0.335548	0.3363205	0.3718342	0.3383650	0.3981345	0.567639
2	t_tx_OP_y(x, y, OP, xrows, xcols, yidxs)	0.391730	0.3924750	0.4355149	0.3932025	0.4458760	0.735393
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	0.432729	0.4339990	0.4899861	0.4365980	0.5369700	0.853674

	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)	1.167434	1.166967	1.171261	1.162066	1.119913	1.295529
3	t_tx_OP_y(x[xrows, xcols], y[yidxs], OP)	1.289619	1.290433	1.317754	1.290317	1.348715	1.503903

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_0.60.0     ggplot2_3.3.5         
[4] knitr_1.33             R.devices_2.17.0       R.utils_2.10.1        
[7] R.oo_1.24.0            R.methodsS3_1.8.1-9001 history_0.0.1-9000    

loaded via a namespace (and not attached):
 [1] Biobase_2.52.0          httr_1.4.2              splines_4.1.1          
 [4] bit64_4.0.5             network_1.17.1          assertthat_0.2.1       
 [7] highr_0.9               stats4_4.1.1            blob_1.2.2             
[10] GenomeInfoDbData_1.2.6  robustbase_0.93-8       pillar_1.6.2           
[13] RSQLite_2.2.8           lattice_0.20-44         glue_1.4.2             
[16] digest_0.6.27           XVector_0.32.0          colorspace_2.0-2       
[19] Matrix_1.3-4            XML_3.99-0.7            pkgconfig_2.0.3        
[22] zlibbioc_1.38.0         genefilter_1.74.0       purrr_0.3.4            
[25] ergm_4.1.2              xtable_1.8-4            scales_1.1.1           
[28] tibble_3.1.4            annotate_1.70.0         KEGGREST_1.32.0        
[31] farver_2.1.0            generics_0.1.0          IRanges_2.26.0         
[34] ellipsis_0.3.2          cachem_1.0.6            withr_2.4.2            
[37] BiocGenerics_0.38.0     mime_0.11               survival_3.2-13        
[40] magrittr_2.0.1          crayon_1.4.1            statnet.common_4.5.0   
[43] memoise_2.0.0           laeken_0.5.1            fansi_0.5.0            
[46] R.cache_0.15.0          MASS_7.3-54             R.rsp_0.44.0           
[49] progressr_0.8.0         tools_4.1.1             lifecycle_1.0.0        
[52] S4Vectors_0.30.0        trust_0.1-8             munsell_0.5.0          
[55] tabby_0.0.1-9001        AnnotationDbi_1.54.1    Biostrings_2.60.2      
[58] compiler_4.1.1          GenomeInfoDb_1.28.1     rlang_0.4.11           
[61] grid_4.1.1              RCurl_1.98-1.4          cwhmisc_6.6            
[64] rstudioapi_0.13         rappdirs_0.3.3          startup_0.15.0-9000    
[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 48.79 secs.

Reproducibility

To reproduce this report, do:

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

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Copyright Dongcan Jiang. Last updated on 2021-08-25 22:50:21 (+0200 UTC). Powered by RSP.