matrixStats.benchmarks

x_OP_y() benchmarks on subsetted computation

This report benchmark the performance of x_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(x_OP_y_x_y_S = x_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `x_OP_y(x, y, OP, xrows, xcols, yidxs)` = x_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `x_OP_y(x[xrows, xcols], y[yidxs], OP)` = x_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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	x_OP_y_x_y_S	0.004126	0.0043750	0.0060019	0.0044855	0.0046655	0.150233
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	0.004689	0.0049475	0.0051747	0.0050740	0.0052420	0.008523
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	0.005411	0.0058570	0.0061015	0.0060040	0.0061890	0.011890

	expr	min	lq	mean	median	uq	max
1	x_OP_y_x_y_S	1.000000	1.000000	1.0000000	1.000000	1.000000	1.0000000
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	1.136452	1.130857	0.8621811	1.131201	1.123567	0.0567319
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	1.311440	1.338743	1.0166082	1.338535	1.326546	0.0791437

Figure: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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(x_OP_y_x_y_S = x_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `x_OP_y(x, y, OP, xrows, xcols, yidxs)` = x_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `x_OP_y(x[xrows, xcols], y[yidxs], OP)` = x_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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	x_OP_y_x_y_S	0.004078	0.004568	0.0054825	0.0047260	0.0051505	0.039265
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	0.004974	0.005275	0.0065113	0.0054715	0.0058160	0.073737
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	0.005869	0.006179	0.0068555	0.0063100	0.0066770	0.022122

	expr	min	lq	mean	median	uq	max
1	x_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	1.219715	1.154772	1.187645	1.157744	1.129211	1.8779320
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	1.439186	1.352671	1.250417	1.335167	1.296379	0.5634025

Figure: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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(x_OP_y_x_y_S = x_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `x_OP_y(x, y, OP, xrows, xcols, yidxs)` = x_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `x_OP_y(x[xrows, xcols], y[yidxs], OP)` = x_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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	x_OP_y_x_y_S	0.004023	0.004318	0.0044804	0.0044345	0.0045890	0.005817
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	0.004710	0.004990	0.0052282	0.0051495	0.0053275	0.009631
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	0.005367	0.005856	0.0062994	0.0059930	0.0062070	0.029772

	expr	min	lq	mean	median	uq	max
1	x_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	1.170768	1.155628	1.166903	1.161236	1.160928	1.655664
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	1.334079	1.356183	1.406006	1.351449	1.352582	5.118102

Figure: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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(x_OP_y_x_y_S = x_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `x_OP_y(x, y, OP, xrows, xcols, yidxs)` = x_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `x_OP_y(x[xrows, xcols], y[yidxs], OP)` = x_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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	x_OP_y_x_y_S	0.004027	0.0042930	0.0052990	0.0044830	0.0047475	0.035518
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	0.004764	0.0049545	0.0056419	0.0050945	0.0054015	0.011826
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	0.005427	0.0058150	0.0069937	0.0059830	0.0063290	0.013828

	expr	min	lq	mean	median	uq	max
1	x_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	1.183015	1.154088	1.064709	1.136404	1.137757	0.3329579
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	1.347653	1.354531	1.319824	1.334597	1.333123	0.3893237

Figure: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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(x_OP_y_x_y_S = x_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `x_OP_y(x, y, OP, xrows, xcols, yidxs)` = x_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `x_OP_y(x[xrows, xcols], y[yidxs], OP)` = x_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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	x_OP_y_x_y_S	0.023444	0.0240095	0.0246985	0.0242475	0.0245015	0.048481
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	0.031113	0.0315155	0.0320571	0.0317660	0.0321060	0.044328
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	0.035808	0.0362570	0.0370761	0.0364325	0.0370215	0.053483

	expr	min	lq	mean	median	uq	max
1	x_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	1.327120	1.312626	1.297935	1.310073	1.310369	0.9143376
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	1.527384	1.510111	1.501145	1.502526	1.510989	1.1031744

Figure: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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(x_OP_y_x_y_S = x_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `x_OP_y(x, y, OP, xrows, xcols, yidxs)` = x_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `x_OP_y(x[xrows, xcols], y[yidxs], OP)` = x_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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	x_OP_y_x_y_S	0.020707	0.0211775	0.0221214	0.0214600	0.0223385	0.041162
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	0.031087	0.0314875	0.0322514	0.0316795	0.0328265	0.038178
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	0.032991	0.0334365	0.0347989	0.0337055	0.0353445	0.068193

	expr	min	lq	mean	median	uq	max
1	x_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	1.501280	1.486837	1.457930	1.476212	1.469503	0.927506
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	1.593229	1.578869	1.573086	1.570620	1.582224	1.656698

Figure: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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(x_OP_y_x_y_S = x_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `x_OP_y(x, y, OP, xrows, xcols, yidxs)` = x_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `x_OP_y(x[xrows, xcols], y[yidxs], OP)` = x_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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	x_OP_y_x_y_S	0.023666	0.0240145	0.0249872	0.0243165	0.0252400	0.050692
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	0.030890	0.0315815	0.0326195	0.0318745	0.0333015	0.048478
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	0.035945	0.0362635	0.0374714	0.0366035	0.0383640	0.052564

	expr	min	lq	mean	median	uq	max
1	x_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	1.305248	1.315101	1.305450	1.310818	1.319394	0.9563245
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	1.518846	1.510067	1.499625	1.505295	1.519968	1.0369289

Figure: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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(x_OP_y_x_y_S = x_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `x_OP_y(x, y, OP, xrows, xcols, yidxs)` = x_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `x_OP_y(x[xrows, xcols], y[yidxs], OP)` = x_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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	x_OP_y_x_y_S	0.016605	0.0172130	0.0175279	0.0173795	0.0176275	0.021188
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	0.026172	0.0264785	0.0272389	0.0266840	0.0268830	0.062399
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	0.029360	0.0297965	0.0306435	0.0300845	0.0304605	0.046999

	expr	min	lq	mean	median	uq	max
1	x_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.00000	1.000000
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	1.576152	1.538285	1.554029	1.535372	1.52506	2.945016
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	1.768142	1.731046	1.748269	1.731034	1.72801	2.218189

Figure: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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(x_OP_y_x_y_S = x_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `x_OP_y(x, y, OP, xrows, xcols, yidxs)` = x_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `x_OP_y(x[xrows, xcols], y[yidxs], OP)` = x_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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	x_OP_y_x_y_S	0.022752	0.0232090	0.0238811	0.023463	0.0244060	0.029087
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	0.032672	0.0332005	0.0340622	0.033522	0.0344280	0.039967
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	0.037727	0.0381675	0.0396741	0.038402	0.0403405	0.075408

	expr	min	lq	mean	median	uq	max
1	x_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	1.436006	1.430501	1.426323	1.428718	1.410637	1.374050
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	1.658184	1.644513	1.661315	1.636705	1.652893	2.592498

Figure: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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(x_OP_y_x_y_S = x_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `x_OP_y(x, y, OP, xrows, xcols, yidxs)` = x_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `x_OP_y(x[xrows, xcols], y[yidxs], OP)` = x_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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	x_OP_y_x_y_S	0.020254	0.0207145	0.0215302	0.0209520	0.0221340	0.027000
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	0.032752	0.0332490	0.0341090	0.0335470	0.0349140	0.039605
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	0.035254	0.0356480	0.0376336	0.0359135	0.0379795	0.078998

	expr	min	lq	mean	median	uq	max
1	x_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	1.617063	1.605107	1.584242	1.601136	1.577392	1.466852
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	1.740595	1.720920	1.747949	1.714085	1.715890	2.925852

Figure: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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(x_OP_y_x_y_S = x_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `x_OP_y(x, y, OP, xrows, xcols, yidxs)` = x_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `x_OP_y(x[xrows, xcols], y[yidxs], OP)` = x_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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	x_OP_y_x_y_S	0.022895	0.0232350	0.0237620	0.0234680	0.0237410	0.029507
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	0.032794	0.0332920	0.0342528	0.0335290	0.0349830	0.050653
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	0.037737	0.0382435	0.0397470	0.0385695	0.0404155	0.078579

	expr	min	lq	mean	median	uq	max
1	x_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	1.432365	1.432838	1.441496	1.428711	1.473527	1.716643
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	1.648264	1.645944	1.672712	1.643493	1.702350	2.663063

Figure: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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(x_OP_y_x_y_S = x_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `x_OP_y(x, y, OP, xrows, xcols, yidxs)` = x_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `x_OP_y(x[xrows, xcols], y[yidxs], OP)` = x_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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	x_OP_y_x_y_S	0.016879	0.0173770	0.0187214	0.0175540	0.017942	0.040207
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	0.028668	0.0291150	0.0310811	0.0293150	0.029582	0.064715
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	0.032403	0.0328755	0.0359407	0.0330655	0.034108	0.072881

	expr	min	lq	mean	median	uq	max
1	x_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	1.698442	1.675491	1.660192	1.669990	1.648757	1.609546
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	1.919723	1.891897	1.919769	1.883645	1.901014	1.812645

Figure: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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(x_OP_y_x_y_S = x_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `x_OP_y(x, y, OP, xrows, xcols, yidxs)` = x_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `x_OP_y(x[xrows, xcols], y[yidxs], OP)` = x_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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	x_OP_y_x_y_S	0.023237	0.0239235	0.0245660	0.0242155	0.0247495	0.030548
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	0.032647	0.0331905	0.0343081	0.0334975	0.0343275	0.050281
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	0.037625	0.0381235	0.0397804	0.0384225	0.0400085	0.079630

	expr	min	lq	mean	median	uq	max
1	x_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	1.404958	1.387360	1.396567	1.383308	1.386998	1.645967
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	1.619185	1.593559	1.619326	1.586690	1.616538	2.606717

Figure: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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(x_OP_y_x_y_S = x_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `x_OP_y(x, y, OP, xrows, xcols, yidxs)` = x_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `x_OP_y(x[xrows, xcols], y[yidxs], OP)` = x_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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	x_OP_y_x_y_S	0.020254	0.0208155	0.0221271	0.0210175	0.0214505	0.066816
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	0.032489	0.0332015	0.0341302	0.0335080	0.0346720	0.052668
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	0.034731	0.0351395	0.0358955	0.0354075	0.0359785	0.041454

	expr	min	lq	mean	median	uq	max
1	x_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	1.604078	1.595037	1.542458	1.594291	1.616373	0.7882543
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	1.714772	1.688141	1.622239	1.684667	1.677280	0.6204203

Figure: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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(x_OP_y_x_y_S = x_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `x_OP_y(x, y, OP, xrows, xcols, yidxs)` = x_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `x_OP_y(x[xrows, xcols], y[yidxs], OP)` = x_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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	x_OP_y_x_y_S	0.020719	0.0214755	0.0237694	0.0222345	0.0239565	0.055658
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	0.028949	0.0301640	0.0319103	0.0311455	0.0330055	0.047355
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	0.033734	0.0344565	0.0366302	0.0356365	0.0378930	0.055127

	expr	min	lq	mean	median	uq	max
1	x_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	1.397220	1.404577	1.342498	1.400774	1.377726	0.8508211
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	1.628167	1.604456	1.541068	1.602757	1.581742	0.9904596

Figure: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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(x_OP_y_x_y_S = x_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `x_OP_y(x, y, OP, xrows, xcols, yidxs)` = x_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `x_OP_y(x[xrows, xcols], y[yidxs], OP)` = x_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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	x_OP_y_x_y_S	0.017600	0.0180920	0.0189505	0.0184445	0.0191975	0.024900
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	0.028167	0.0286755	0.0298966	0.0291500	0.0303085	0.044259
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	0.032549	0.0330820	0.0360233	0.0339645	0.0354165	0.079638

	expr	min	lq	mean	median	uq	max
1	x_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	1.600398	1.584982	1.577617	1.580417	1.578773	1.777470
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	1.849375	1.828543	1.900917	1.841443	1.844850	3.198313

Figure: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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(x_OP_y_x_y_S = x_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `x_OP_y(x, y, OP, xrows, xcols, yidxs)` = x_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `x_OP_y(x[xrows, xcols], y[yidxs], OP)` = x_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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	x_OP_y_x_y_S	0.129126	0.145434	0.1623397	0.1557460	0.1772155	0.224476
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	0.174505	0.201165	0.2186318	0.2150450	0.2299000	0.307454
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	0.200467	0.223690	0.2539445	0.2444265	0.2744855	0.426161

	expr	min	lq	mean	median	uq	max
1	x_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	1.351432	1.383205	1.346755	1.380742	1.297291	1.369652
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	1.552491	1.538086	1.564278	1.569392	1.548880	1.898470

Figure: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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(x_OP_y_x_y_S = x_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `x_OP_y(x, y, OP, xrows, xcols, yidxs)` = x_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `x_OP_y(x[xrows, xcols], y[yidxs], OP)` = x_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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	x_OP_y_x_y_S	0.117229	0.1249880	0.1428005	0.1369880	0.1631180	0.189287
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	0.184766	0.2024055	0.2263585	0.2163345	0.2501095	0.302907
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	0.192244	0.2169615	0.2421038	0.2277575	0.2751875	0.394571

	expr	min	lq	mean	median	uq	max
1	x_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	1.576112	1.619400	1.585138	1.579222	1.533304	1.600253
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	1.639901	1.735859	1.695399	1.662609	1.687046	2.084512

Figure: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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(x_OP_y_x_y_S = x_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `x_OP_y(x, y, OP, xrows, xcols, yidxs)` = x_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `x_OP_y(x[xrows, xcols], y[yidxs], OP)` = x_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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	x_OP_y_x_y_S	0.122550	0.1365335	0.1587696	0.1540485	0.1683540	0.344538
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	0.165662	0.1879040	0.2191768	0.2084625	0.2499565	0.388146
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	0.190418	0.2178485	0.2501351	0.2461650	0.2663545	0.407773

	expr	min	lq	mean	median	uq	max
1	x_OP_y_x_y_S	1.000000	1.000000	1.00000	1.000000	1.000000	1.000000
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	1.351791	1.376248	1.38047	1.353226	1.484708	1.126570
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	1.553798	1.595568	1.57546	1.597971	1.582110	1.183536

Figure: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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(x_OP_y_x_y_S = x_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `x_OP_y(x, y, OP, xrows, xcols, yidxs)` = x_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `x_OP_y(x[xrows, xcols], y[yidxs], OP)` = x_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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	x_OP_y_x_y_S	0.087277	0.0973845	0.1156346	0.1037500	0.1129455	0.268237
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	0.147527	0.1625230	0.1872013	0.1731985	0.1922865	0.456080
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	0.163898	0.1810995	0.2025351	0.1892255	0.2078955	0.377228

	expr	min	lq	mean	median	uq	max
1	x_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	1.690331	1.668880	1.618903	1.669383	1.702471	1.700287
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	1.877906	1.859634	1.751508	1.823860	1.840671	1.406324

Figure: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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(x_OP_y_x_y_S = x_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `x_OP_y(x, y, OP, xrows, xcols, yidxs)` = x_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `x_OP_y(x[xrows, xcols], y[yidxs], OP)` = x_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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	x_OP_y_x_y_S	0.120604	0.1327595	0.1521894	0.1480815	0.1594145	0.258066
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	0.160871	0.1790440	0.2071493	0.2019120	0.2207380	0.346446
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	0.183030	0.2035455	0.2329213	0.2259345	0.2481010	0.386030

	expr	min	lq	mean	median	uq	max
1	x_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	1.333878	1.348634	1.361129	1.363519	1.384680	1.342470
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	1.517611	1.533190	1.530470	1.525744	1.556326	1.495858

Figure: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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(x_OP_y_x_y_S = x_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `x_OP_y(x, y, OP, xrows, xcols, yidxs)` = x_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `x_OP_y(x[xrows, xcols], y[yidxs], OP)` = x_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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	x_OP_y_x_y_S	0.107355	0.1138780	0.1326736	0.1306685	0.1428555	0.224089
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	0.168996	0.1845580	0.2091406	0.2023935	0.2304765	0.352488
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	0.176319	0.1896305	0.2160171	0.2109385	0.2268080	0.303434

	expr	min	lq	mean	median	uq	max
1	x_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	1.574179	1.620664	1.576355	1.548908	1.613354	1.572982
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	1.642392	1.665207	1.628185	1.614303	1.587674	1.354078

Figure: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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(x_OP_y_x_y_S = x_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `x_OP_y(x, y, OP, xrows, xcols, yidxs)` = x_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `x_OP_y(x[xrows, xcols], y[yidxs], OP)` = x_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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	x_OP_y_x_y_S	0.128065	0.1475090	0.1613751	0.1535800	0.1771225	0.220726
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	0.173772	0.1893765	0.2101063	0.2023525	0.2172820	0.299435
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	0.197852	0.2165645	0.2451639	0.2309380	0.2678890	0.407414

	expr	min	lq	mean	median	uq	max
1	x_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	1.356905	1.283830	1.301974	1.317571	1.226733	1.356591
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	1.544934	1.468144	1.519218	1.503698	1.512450	1.845791

Figure: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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(x_OP_y_x_y_S = x_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `x_OP_y(x, y, OP, xrows, xcols, yidxs)` = x_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `x_OP_y(x[xrows, xcols], y[yidxs], OP)` = x_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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	x_OP_y_x_y_S	0.089230	0.1008090	0.1118742	0.1075285	0.1252975	0.147406
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	0.148351	0.1671590	0.1835342	0.1741055	0.1997620	0.271228
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	0.162216	0.1807885	0.2022645	0.1899520	0.2340245	0.325987

	expr	min	lq	mean	median	uq	max
1	x_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	1.662569	1.658175	1.640542	1.619157	1.594302	1.840007
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	1.817954	1.793377	1.807964	1.766527	1.867751	2.211491

Figure: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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(x_OP_y_x_y_S = x_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `x_OP_y(x, y, OP, xrows, xcols, yidxs)` = x_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `x_OP_y(x[xrows, xcols], y[yidxs], OP)` = x_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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	x_OP_y_x_y_S	0.004117	0.0044020	0.0048895	0.0045035	0.0046825	0.032578
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	0.004567	0.0049815	0.0053548	0.0051010	0.0052890	0.015121
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	0.005762	0.0060310	0.0064532	0.0061615	0.0063510	0.014464

	expr	min	lq	mean	median	uq	max
1	x_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	1.109303	1.131645	1.095176	1.132675	1.129525	0.4641476
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	1.399563	1.370059	1.319817	1.368158	1.356327	0.4439806

Figure: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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(x_OP_y_x_y_S = x_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `x_OP_y(x, y, OP, xrows, xcols, yidxs)` = x_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `x_OP_y(x[xrows, xcols], y[yidxs], OP)` = x_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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	x_OP_y_x_y_S	0.004014	0.0042730	0.0045204	0.0043740	0.004574	0.008945
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	0.004556	0.0048115	0.0054009	0.0049145	0.005123	0.043405
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	0.005338	0.0058055	0.0060106	0.0059460	0.006110	0.008909

	expr	min	lq	mean	median	uq	max
1	x_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	1.135027	1.126024	1.194780	1.123571	1.120026	4.8524315
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	1.329846	1.358647	1.329654	1.359396	1.335811	0.9959754

Figure: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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(x_OP_y_x_y_S = x_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `x_OP_y(x, y, OP, xrows, xcols, yidxs)` = x_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `x_OP_y(x[xrows, xcols], y[yidxs], OP)` = x_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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	x_OP_y_x_y_S	0.004105	0.0043635	0.0045512	0.0044835	0.0046385	0.005934
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	0.004659	0.0049240	0.0051769	0.0050880	0.0053230	0.009706
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	0.005487	0.0058735	0.0066254	0.0061130	0.0062960	0.032996

	expr	min	lq	mean	median	uq	max
1	x_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	1.134957	1.128452	1.137489	1.134828	1.147569	1.635659
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	1.336663	1.346053	1.455750	1.363444	1.357335	5.560499

Figure: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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(x_OP_y_x_y_S = x_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `x_OP_y(x, y, OP, xrows, xcols, yidxs)` = x_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `x_OP_y(x[xrows, xcols], y[yidxs], OP)` = x_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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	x_OP_y_x_y_S	0.004158	0.0043435	0.0048292	0.004468	0.0046785	0.030534
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	0.004583	0.0048630	0.0051324	0.005010	0.0052050	0.011506
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	0.005360	0.0058785	0.0061111	0.006034	0.0061625	0.010902

	expr	min	lq	mean	median	uq	max
1	x_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	1.102213	1.119604	1.062785	1.121307	1.112536	0.3768258
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	1.289081	1.353402	1.265447	1.350492	1.317196	0.3570446

Figure: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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(x_OP_y_x_y_S = x_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `x_OP_y(x, y, OP, xrows, xcols, yidxs)` = x_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `x_OP_y(x[xrows, xcols], y[yidxs], OP)` = x_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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	x_OP_y_x_y_S	0.010966	0.0115445	0.0121946	0.0118755	0.0122495	0.038089
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	0.019930	0.0203155	0.0212359	0.0206060	0.0213400	0.036575
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	0.028503	0.0290225	0.0299021	0.0292510	0.0302410	0.036145

	expr	min	lq	mean	median	uq	max
1	x_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	1.817436	1.759756	1.741417	1.735169	1.742112	0.9602510
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	2.599216	2.513968	2.452071	2.463138	2.468754	0.9489616

Figure: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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(x_OP_y_x_y_S = x_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `x_OP_y(x, y, OP, xrows, xcols, yidxs)` = x_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `x_OP_y(x[xrows, xcols], y[yidxs], OP)` = x_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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	x_OP_y_x_y_S	0.010975	0.0118495	0.0125865	0.0121540	0.012469	0.026985
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	0.018073	0.0192630	0.0204049	0.0195145	0.019939	0.035861
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	0.028458	0.0297960	0.0318925	0.0301060	0.030822	0.108843

	expr	min	lq	mean	median	uq	max
1	x_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	1.646743	1.625638	1.621170	1.605603	1.599086	1.328923
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	2.592984	2.514537	2.533865	2.477045	2.471890	4.033463

Figure: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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(x_OP_y_x_y_S = x_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `x_OP_y(x, y, OP, xrows, xcols, yidxs)` = x_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `x_OP_y(x[xrows, xcols], y[yidxs], OP)` = x_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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	x_OP_y_x_y_S	0.011007	0.0115485	0.0122927	0.0118310	0.0121520	0.042284
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	0.020579	0.0211495	0.0217612	0.0213415	0.0216935	0.037354
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	0.028287	0.0289405	0.0298139	0.0292290	0.0297435	0.045548

	expr	min	lq	mean	median	uq	max
1	x_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	1.869628	1.831363	1.770258	1.803863	1.785179	0.8834074
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	2.569910	2.505996	2.425342	2.470543	2.447622	1.0771923

Figure: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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(x_OP_y_x_y_S = x_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `x_OP_y(x, y, OP, xrows, xcols, yidxs)` = x_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `x_OP_y(x[xrows, xcols], y[yidxs], OP)` = x_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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	x_OP_y_x_y_S	0.012631	0.0131360	0.0137054	0.0134700	0.013889	0.018492
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	0.015309	0.0159700	0.0170936	0.0164645	0.017057	0.050154
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	0.030089	0.0305275	0.0316096	0.0309655	0.032210	0.037894

	expr	min	lq	mean	median	uq	max
1	x_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	1.212018	1.215743	1.247217	1.222309	1.228094	2.712200
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	2.382155	2.323957	2.306361	2.298849	2.319101	2.049211

Figure: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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(x_OP_y_x_y_S = x_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `x_OP_y(x, y, OP, xrows, xcols, yidxs)` = x_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `x_OP_y(x[xrows, xcols], y[yidxs], OP)` = x_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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	x_OP_y_x_y_S	0.010913	0.011579	0.0128022	0.0119460	0.012509	0.042454
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	0.022468	0.022977	0.0240274	0.0232025	0.024120	0.044847
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	0.030992	0.031591	0.0341731	0.0322705	0.033170	0.071380

	expr	min	lq	mean	median	uq	max
1	x_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	2.058829	1.984368	1.876818	1.942282	1.928212	1.056367
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	2.839916	2.728301	2.669310	2.701364	2.651691	1.681349

Figure: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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(x_OP_y_x_y_S = x_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `x_OP_y(x, y, OP, xrows, xcols, yidxs)` = x_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `x_OP_y(x[xrows, xcols], y[yidxs], OP)` = x_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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	x_OP_y_x_y_S	0.011070	0.0117110	0.0123339	0.0120875	0.0124800	0.027094
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	0.020557	0.0211955	0.0217935	0.0216220	0.0221995	0.026667
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	0.030974	0.0316270	0.0331714	0.0325835	0.0335670	0.072395

	expr	min	lq	mean	median	uq	max
1	x_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	1.857001	1.809880	1.766962	1.788790	1.778806	0.9842401
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	2.798013	2.700623	2.689454	2.695636	2.689663	2.6719938

Figure: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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(x_OP_y_x_y_S = x_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `x_OP_y(x, y, OP, xrows, xcols, yidxs)` = x_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `x_OP_y(x[xrows, xcols], y[yidxs], OP)` = x_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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	x_OP_y_x_y_S	0.011257	0.0116755	0.0121951	0.0118530	0.012433	0.018590
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	0.022535	0.0228365	0.0238168	0.0234295	0.024361	0.031938
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	0.030955	0.0315490	0.0333036	0.0328195	0.033313	0.070362

	expr	min	lq	mean	median	uq	max
1	x_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	2.001866	1.955933	1.952988	1.976673	1.959382	1.718020
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	2.749845	2.702154	2.730911	2.768877	2.679402	3.784938

Figure: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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(x_OP_y_x_y_S = x_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `x_OP_y(x, y, OP, xrows, xcols, yidxs)` = x_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `x_OP_y(x[xrows, xcols], y[yidxs], OP)` = x_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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	x_OP_y_x_y_S	0.012573	0.0131415	0.0138816	0.0133320	0.0136505	0.039601
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	0.018018	0.0186735	0.0196317	0.0190540	0.0193985	0.052687
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	0.032484	0.0328995	0.0334378	0.0331485	0.0336730	0.039234

	expr	min	lq	mean	median	uq	max
1	x_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	1.433071	1.420956	1.414232	1.429193	1.421083	1.3304462
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	2.583632	2.503481	2.408797	2.486386	2.466796	0.9907326

Figure: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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(x_OP_y_x_y_S = x_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `x_OP_y(x, y, OP, xrows, xcols, yidxs)` = x_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `x_OP_y(x[xrows, xcols], y[yidxs], OP)` = x_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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	x_OP_y_x_y_S	0.010907	0.0116200	0.0120543	0.0118960	0.0122495	0.016913
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	0.021959	0.0224110	0.0229363	0.0226145	0.0231595	0.027249
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	0.031635	0.0319495	0.0345411	0.0321820	0.0334520	0.095095

	expr	min	lq	mean	median	uq	max
1	x_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	2.013294	1.928657	1.902756	1.901017	1.890649	1.611128
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	2.900431	2.749527	2.865472	2.705279	2.730887	5.622598

Figure: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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(x_OP_y_x_y_S = x_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `x_OP_y(x, y, OP, xrows, xcols, yidxs)` = x_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `x_OP_y(x[xrows, xcols], y[yidxs], OP)` = x_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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	x_OP_y_x_y_S	0.010874	0.0116985	0.0129855	0.012187	0.012713	0.055745
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	0.020564	0.0212935	0.0226076	0.022143	0.022783	0.040455
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	0.031474	0.0319280	0.0334021	0.032456	0.034013	0.043388

	expr	min	lq	mean	median	uq	max
1	x_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	1.891116	1.820191	1.740996	1.816936	1.792103	0.7257153
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	2.894427	2.729239	2.572273	2.663166	2.675450	0.7783299

Figure: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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(x_OP_y_x_y_S = x_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `x_OP_y(x, y, OP, xrows, xcols, yidxs)` = x_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `x_OP_y(x[xrows, xcols], y[yidxs], OP)` = x_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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	x_OP_y_x_y_S	0.010960	0.0117200	0.0125178	0.0120970	0.0125875	0.040274
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	0.022119	0.0228125	0.0237193	0.0232670	0.0240790	0.038324
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	0.031653	0.0322400	0.0337555	0.0326095	0.0339560	0.061728

	expr	min	lq	mean	median	uq	max
1	x_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	2.018157	1.946459	1.894846	1.923369	1.912929	0.9515817
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	2.888047	2.750853	2.696601	2.695668	2.697597	1.5327010

Figure: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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(x_OP_y_x_y_S = x_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `x_OP_y(x, y, OP, xrows, xcols, yidxs)` = x_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `x_OP_y(x[xrows, xcols], y[yidxs], OP)` = x_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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	x_OP_y_x_y_S	0.013097	0.0136580	0.0142580	0.013939	0.0143065	0.026048
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	0.019334	0.0200435	0.0208960	0.020516	0.0208875	0.052337
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	0.034490	0.0350255	0.0361646	0.035313	0.0357010	0.072794

	expr	min	lq	mean	median	uq	max
1	x_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	1.476216	1.467528	1.465566	1.471842	1.460001	2.009252
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	2.633427	2.564468	2.536443	2.533396	2.495439	2.794610

Figure: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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(x_OP_y_x_y_S = x_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `x_OP_y(x, y, OP, xrows, xcols, yidxs)` = x_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `x_OP_y(x[xrows, xcols], y[yidxs], OP)` = x_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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	x_OP_y_x_y_S	0.056473	0.0600895	0.1025607	0.0676575	0.1892350	0.241864
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	0.118717	0.1233465	0.1644007	0.1328120	0.1557785	0.370247
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	0.176631	0.1845910	0.2791689	0.2115015	0.4500480	0.651747

	expr	min	lq	mean	median	uq	max
1	x_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.0000000	1.000000
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	2.102190	2.052713	1.602960	1.963005	0.8232013	1.530807
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	3.127707	3.071934	2.721988	3.126061	2.3782493	2.694684

Figure: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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(x_OP_y_x_y_S = x_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `x_OP_y(x, y, OP, xrows, xcols, yidxs)` = x_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `x_OP_y(x[xrows, xcols], y[yidxs], OP)` = x_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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	x_OP_y_x_y_S	0.055203	0.0581440	0.0657654	0.0622770	0.0719805	0.128674
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	0.104425	0.1092505	0.1227363	0.1191995	0.1322750	0.172032
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	0.176822	0.1865470	0.2086018	0.2040505	0.2281355	0.369138

	expr	min	lq	mean	median	uq	max
1	x_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	1.891654	1.878964	1.866274	1.914021	1.837651	1.336960
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	3.203123	3.208362	3.171906	3.276499	3.169407	2.868785

Figure: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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(x_OP_y_x_y_S = x_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `x_OP_y(x, y, OP, xrows, xcols, yidxs)` = x_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `x_OP_y(x[xrows, xcols], y[yidxs], OP)` = x_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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	x_OP_y_x_y_S	0.056008	0.0583700	0.0677104	0.0645360	0.0732285	0.124893
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	0.124125	0.1293685	0.1482757	0.1449955	0.1616070	0.261661
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	0.177125	0.1879810	0.2134462	0.2121690	0.2292580	0.316786

	expr	min	lq	mean	median	uq	max
1	x_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	2.216201	2.216353	2.189851	2.246738	2.206887	2.095081
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	3.162495	3.220507	3.152340	3.287607	3.130721	2.536459

Figure: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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(x_OP_y_x_y_S = x_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `x_OP_y(x, y, OP, xrows, xcols, yidxs)` = x_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `x_OP_y(x[xrows, xcols], y[yidxs], OP)` = x_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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	x_OP_y_x_y_S	0.068304	0.070684	0.0791743	0.0759585	0.088376	0.113329
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	0.094012	0.097499	0.1123378	0.1084820	0.123048	0.258036
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	0.188164	0.195570	0.2163035	0.2092970	0.241508	0.315300

	expr	min	lq	mean	median	uq	max
1	x_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	1.376376	1.379365	1.418866	1.428175	1.392324	2.276875
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	2.754802	2.766821	2.731990	2.755412	2.732733	2.782165

Figure: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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(x_OP_y_x_y_S = x_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `x_OP_y(x, y, OP, xrows, xcols, yidxs)` = x_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `x_OP_y(x[xrows, xcols], y[yidxs], OP)` = x_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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	x_OP_y_x_y_S	0.054132	0.0574220	0.0665703	0.0602450	0.0710540	0.167341
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	0.115262	0.1197645	0.1376343	0.1242860	0.1424685	0.252069
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	0.171625	0.1781245	0.2092158	0.1849855	0.2169400	0.392022

	expr	min	lq	mean	median	uq	max
1	x_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	2.129277	2.085690	2.067504	2.063009	2.005074	1.506319
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	3.170491	3.102025	3.142779	3.070554	3.053171	2.342654

Figure: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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(x_OP_y_x_y_S = x_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `x_OP_y(x, y, OP, xrows, xcols, yidxs)` = x_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `x_OP_y(x[xrows, xcols], y[yidxs], OP)` = x_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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	x_OP_y_x_y_S	0.052994	0.0573660	0.0637077	0.0605715	0.0687355	0.113115
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	0.099036	0.1055705	0.1195192	0.1134550	0.1310980	0.245193
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	0.167543	0.1784280	0.1986300	0.1902075	0.2120115	0.298828

	expr	min	lq	mean	median	uq	max
1	x_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	1.868815	1.840297	1.876055	1.873076	1.907282	2.167644
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	3.161547	3.110344	3.117832	3.140214	3.084454	2.641807

Figure: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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(x_OP_y_x_y_S = x_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `x_OP_y(x, y, OP, xrows, xcols, yidxs)` = x_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `x_OP_y(x[xrows, xcols], y[yidxs], OP)` = x_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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	x_OP_y_x_y_S	0.055286	0.058758	0.0652120	0.0634820	0.0718830	0.097903
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	0.115694	0.119929	0.1322296	0.1268385	0.1426455	0.168063
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	0.171338	0.177544	0.2001334	0.1894635	0.2186630	0.360192

	expr	min	lq	mean	median	uq	max
1	x_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	2.092646	2.041067	2.027688	1.998023	1.984412	1.716628
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	3.099121	3.021614	3.068966	2.984523	3.041929	3.679070

Figure: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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(x_OP_y_x_y_S = x_OP_y(x_S, y_S, OP = OP, na.rm = FALSE), `x_OP_y(x, y, OP, xrows, xcols, yidxs)` = x_OP_y(x, 
+     y, OP = OP, xrows = xrows, xcols = xcols, yidxs = yidxs, na.rm = FALSE), `x_OP_y(x[xrows, xcols], y[yidxs], OP)` = x_OP_y(x[xrows, 
+     xcols], y[yidxs], OP = OP, na.rm = FALSE), unit = "ms")

Table: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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	x_OP_y_x_y_S	0.068097	0.0711525	0.0803506	0.0788600	0.0886030	0.110095
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	0.089441	0.0932645	0.1055002	0.1035815	0.1171120	0.194089
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	0.187759	0.1950735	0.2183420	0.2092075	0.2432055	0.388538

	expr	min	lq	mean	median	uq	max
1	x_OP_y_x_y_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	x_OP_y(x, y, OP, xrows, xcols, yidxs)	1.313435	1.310769	1.312997	1.313486	1.321761	1.762923
3	x_OP_y(x[xrows, xcols], y[yidxs], OP)	2.757229	2.741625	2.717365	2.652897	2.744890	3.529116

Figure: Benchmarking of x_OP_y_x_y_S(), x_OP_y(x, y, OP, xrows, xcols, yidxs)() and x_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         limma_3.48.3           
[16] glue_1.4.2              digest_0.6.27           XVector_0.32.0         
[19] colorspace_2.0-2        Matrix_1.3-4            XML_3.99-0.7           
[22] pkgconfig_2.0.3         zlibbioc_1.38.0         genefilter_1.74.0      
[25] purrr_0.3.4             ergm_4.1.2              xtable_1.8-4           
[28] scales_1.1.1            tibble_3.1.4            annotate_1.70.0        
[31] KEGGREST_1.32.0         farver_2.1.0            generics_0.1.0         
[34] IRanges_2.26.0          ellipsis_0.3.2          cachem_1.0.6           
[37] withr_2.4.2             BiocGenerics_0.38.0     mime_0.11              
[40] survival_3.2-13         magrittr_2.0.1          crayon_1.4.1           
[43] statnet.common_4.5.0    memoise_2.0.0           laeken_0.5.1           
[46] fansi_0.5.0             R.cache_0.15.0          MASS_7.3-54            
[49] R.rsp_0.44.0            progressr_0.8.0         tools_4.1.1            
[52] lifecycle_1.0.0         S4Vectors_0.30.0        trust_0.1-8            
[55] munsell_0.5.0           tabby_0.0.1-9001        AnnotationDbi_1.54.1   
[58] Biostrings_2.60.2       compiler_4.1.1          GenomeInfoDb_1.28.1    
[61] rlang_0.4.11            grid_4.1.1              RCurl_1.98-1.4         
[64] cwhmisc_6.6             rstudioapi_0.13         rappdirs_0.3.3         
[67] startup_0.15.0-9000     labeling_0.4.2          bitops_1.0-7           
[70] base64enc_0.1-3         boot_1.3-28             gtable_0.3.0           
[73] DBI_1.1.1               markdown_1.1            R6_2.5.1               
[76] lpSolveAPI_5.5.2.0-17.7 rle_0.9.2               dplyr_1.0.7            
[79] fastmap_1.1.0           bit_4.0.4               utf8_1.2.2             
[82] parallel_4.1.1          Rcpp_1.0.7              vctrs_0.3.8            
[85] png_0.1-7               DEoptimR_1.0-9          tidyselect_1.1.1       
[88] xfun_0.25               coda_0.19-4            

Total processing time was 48.75 secs.

Reproducibility

To reproduce this report, do:

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