matrixStats.benchmarks

x_OP_y() benchmarks

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

Alternative methods

x_OP_y_R()

as below

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

Data type “integer”

Data

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

Results

10x10 vector

All elements

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

> OP
[1] "+"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5416661 289.3    7916910 422.9  7916910 422.9
Vcells 10852572  82.8   39038428 297.9 94934136 724.3

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

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.001745	0.001982	0.1183098	0.0021305	0.0022645	11.599051
1	x_OP_y	0.004163	0.004512	0.0048525	0.0046405	0.0047880	0.020428

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.000000	1.000000	1.0000000	1.000000	1.000000	1.0000000
1	x_OP_y	2.385673	2.276488	0.0410153	2.178127	2.114374	0.0017612

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

> OP
[1] "-"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5414272 289.2    7916910 422.9  7916910 422.9
Vcells 10543711  80.5   39038428 297.9 94934136 724.3

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

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.001894	0.0021235	0.0024287	0.002221	0.0023725	0.014630
1	x_OP_y	0.004264	0.0045435	0.0049544	0.004725	0.0048815	0.023081

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.00000	1.000000	1.000000	1.00000	1.000000	1.000000
1	x_OP_y	2.25132	2.139628	2.039918	2.12742	2.057534	1.577649

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

> OP
[1] "*"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5414335 289.2    7916910 422.9  7916910 422.9
Vcells 10544265  80.5   39038428 297.9 94934136 724.3

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

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.003216	0.0037275	0.0043916	0.0039460	0.004395	0.028018
1	x_OP_y	0.006908	0.0079355	0.0087393	0.0083035	0.008720	0.036743

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.00000	1.000000	1.00000	1.000000	1.000000	1.000000
1	x_OP_y	2.14801	2.128907	1.98999	2.104283	1.984073	1.311407

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

> OP
[1] "/"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5414398 289.2    7916910 422.9  7916910 422.9
Vcells 10544307  80.5   39038428 297.9 94934136 724.3

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

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.001823	0.0020715	0.0023296	0.002185	0.0023045	0.012960
1	x_OP_y	0.004233	0.0044860	0.0048927	0.004663	0.0048520	0.021008

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.000000	1.00000	1.000000	1.000000	1.000000	1.000000
1	x_OP_y	2.321997	2.16558	2.100246	2.134096	2.105446	1.620988

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

100x100 vector

All elements

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

> OP
[1] "+"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5414461 289.2    7916910 422.9  7916910 422.9
Vcells 10544391  80.5   39038428 297.9 94934136 724.3

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

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.034646	0.0350260	0.0354890	0.035195	0.0354515	0.043266
1	x_OP_y	0.043786	0.0441645	0.0452476	0.044425	0.0449440	0.077183

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.000000	1.000000	1.000000	1.000000	1.00000	1.000000
1	x_OP_y	1.263811	1.260906	1.274974	1.262253	1.26776	1.783918

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

> OP
[1] "-"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5414524 289.2    7916910 422.9  7916910 422.9
Vcells 10544649  80.5   39038428 297.9 94934136 724.3

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

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.033759	0.0341715	0.0349730	0.0344345	0.0346845	0.053023
1	x_OP_y	0.038221	0.0387550	0.0405948	0.0391650	0.0396120	0.062740

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.000000	1.000000	1.000000	1.000000	1.000000	1.00000
1	x_OP_y	1.132172	1.134132	1.160748	1.137377	1.142066	1.18326

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

> OP
[1] "*"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5414587 289.2    7916910 422.9  7916910 422.9
Vcells 10544938  80.5   39038428 297.9 94934136 724.3

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

	expr	min	lq	mean	median	uq	max
1	x_OP_y	0.041876	0.044042	0.0451193	0.044389	0.0446395	0.077881
2	x_OP_y_R	0.050367	0.052719	0.0534964	0.053060	0.0533685	0.068363

	expr	min	lq	mean	median	uq	max
1	x_OP_y	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	x_OP_y_R	1.202765	1.197016	1.185666	1.195341	1.195544	0.8777879

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

> OP
[1] "/"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5414650 289.2    7916910 422.9  7916910 422.9
Vcells 10544980  80.5   39038428 297.9 94934136 724.3

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

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.022659	0.0234705	0.0254748	0.024179	0.0246615	0.053463
1	x_OP_y	0.030212	0.0309920	0.0331072	0.031794	0.0326425	0.063958

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	x_OP_y	1.333333	1.320466	1.299605	1.314943	1.323622	1.196304

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

1000x10 vector

All elements

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

> OP
[1] "+"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5414713 289.2    7916910 422.9  7916910 422.9
Vcells 10545772  80.5   39038428 297.9 94934136 724.3

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

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.033730	0.034406	0.036349	0.0359005	0.0365170	0.067853
1	x_OP_y	0.041315	0.043006	0.043675	0.0433825	0.0437765	0.061877

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
1	x_OP_y	1.224874	1.249956	1.201545	1.208409	1.198798	0.9119273

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

> OP
[1] "-"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5414776 289.2    7916910 422.9  7916910 422.9
Vcells 10545814  80.5   39038428 297.9 94934136 724.3

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

	expr	min	lq	mean	median	uq	max
1	x_OP_y	0.037287	0.0379045	0.0387342	0.0381430	0.0384760	0.055843
2	x_OP_y_R	0.038794	0.0392505	0.0402957	0.0395215	0.0400815	0.071242

	expr	min	lq	mean	median	uq	max
1	x_OP_y	1.000000	1.00000	1.000000	1.00000	1.000000	1.000000
2	x_OP_y_R	1.040416	1.03551	1.040315	1.03614	1.041727	1.275755

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

> OP
[1] "*"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5414839 289.2    7916910 422.9  7916910 422.9
Vcells 10546241  80.5   39038428 297.9 94934136 724.3

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

	expr	min	lq	mean	median	uq	max
1	x_OP_y	0.041016	0.0428595	0.0437888	0.0433315	0.043798	0.067717
2	x_OP_y_R	0.050557	0.0527605	0.0540048	0.0530630	0.053369	0.096236

	expr	min	lq	mean	median	uq	max
1	x_OP_y	1.000000	1.000000	1.0000	1.000000	1.000000	1.00000
2	x_OP_y_R	1.232617	1.231011	1.2333	1.224583	1.218526	1.42115

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

> OP
[1] "/"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5414902 289.2    7916910 422.9  7916910 422.9
Vcells 10546283  80.5   39038428 297.9 94934136 724.3

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

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.023405	0.0239570	0.0250790	0.0242255	0.0246195	0.055720
1	x_OP_y	0.030221	0.0307675	0.0315555	0.0310515	0.0314290	0.049751

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.00000	1.00000	1.000000	1.000000	1.00000	1.0000000
1	x_OP_y	1.29122	1.28428	1.258245	1.281769	1.27659	0.8928751

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

10x1000 vector

All elements

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

> OP
[1] "+"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5414965 289.2    7916910 422.9  7916910 422.9
Vcells 10546278  80.5   39038428 297.9 94934136 724.3

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

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.034933	0.0351745	0.0367221	0.0354380	0.0371110	0.070618
1	x_OP_y	0.043174	0.0436910	0.0454902	0.0440975	0.0458815	0.074519

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.000000	1.000000	1.00000	1.000000	1.000000	1.000000
1	x_OP_y	1.235909	1.242121	1.23877	1.244356	1.236331	1.055241

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

> OP
[1] "-"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5415028 289.2    7916910 422.9  7916910 422.9
Vcells 10546320  80.5   39038428 297.9 94934136 724.3

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

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.033951	0.0344830	0.0370768	0.0359955	0.036847	0.071624
1	x_OP_y	0.037570	0.0383995	0.0414510	0.0398795	0.041676	0.097974

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	x_OP_y	1.106595	1.113578	1.117976	1.107902	1.131055	1.367893

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

> OP
[1] "*"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5415091 289.2    7916910 422.9  7916910 422.9
Vcells 10546362  80.5   39038428 297.9 94934136 724.3

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

	expr	min	lq	mean	median	uq	max
1	x_OP_y	0.034669	0.0364845	0.0387897	0.03765	0.0391755	0.070359
2	x_OP_y_R	0.042301	0.0440145	0.0465300	0.04560	0.0473990	0.063301

	expr	min	lq	mean	median	uq	max
1	x_OP_y	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	x_OP_y_R	1.220139	1.206389	1.199544	1.211155	1.209914	0.8996859

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

> OP
[1] "/"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5415154 289.3    7916910 422.9  7916910 422.9
Vcells 10546963  80.5   39038428 297.9 94934136 724.3

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

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.019543	0.020177	0.0210901	0.0208365	0.0211265	0.034680
1	x_OP_y	0.026204	0.026877	0.0285741	0.0274720	0.0283100	0.060968

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	x_OP_y	1.340838	1.332061	1.354858	1.318456	1.340023	1.758016

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

100x1000 vector

All elements

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

> OP
[1] "+"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5415217 289.3    7916910 422.9  7916910 422.9
Vcells 10547047  80.5   39038428 297.9 94934136 724.3

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

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.206595	0.222928	0.2581896	0.252993	0.2810800	0.395533
1	x_OP_y	0.256061	0.287432	0.3177830	0.308237	0.3420815	0.414349

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	x_OP_y	1.239435	1.289349	1.230813	1.218362	1.217025	1.047571

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

> OP
[1] "-"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5415280 289.3    7916910 422.9  7916910 422.9
Vcells 10547089  80.5   39038428 297.9 94934136 724.3

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

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.207133	0.226874	0.2563974	0.253889	0.269979	0.399060
1	x_OP_y	0.214268	0.236916	0.2648584	0.263563	0.280436	0.379632

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
1	x_OP_y	1.034447	1.044263	1.032999	1.038103	1.038733	0.9513156

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

> OP
[1] "*"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5415343 289.3    7916910 422.9  7916910 422.9
Vcells 10547766  80.5   39038428 297.9 94934136 724.3

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

	expr	min	lq	mean	median	uq	max
1	x_OP_y	0.263325	0.2876820	0.3101268	0.3162695	0.3285000	0.384502
2	x_OP_y_R	0.336725	0.3789075	0.4059591	0.4085335	0.4303695	0.493282

	expr	min	lq	mean	median	uq	max
1	x_OP_y	1.000000	1.000000	1.00000	1.000000	1.000000	1.000000
2	x_OP_y_R	1.278743	1.317105	1.30901	1.291726	1.310105	1.282911

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

> OP
[1] "/"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5415406 289.3    7916910 422.9  7916910 422.9
Vcells 10547808  80.5   39038428 297.9 94934136 724.3

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

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.173549	0.1982010	0.3415812	0.405323	0.4422375	0.520576
1	x_OP_y	0.215580	0.2433185	0.3719319	0.424221	0.4789150	0.528031

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	x_OP_y	1.242185	1.227635	1.088853	1.046625	1.082936	1.014321

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

1000x100 vector

All elements

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

> OP
[1] "+"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5415469 289.3    7916910 422.9  7916910 422.9
Vcells 10548300  80.5   39038428 297.9 94934136 724.3

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

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.218424	0.2357295	0.2557380	0.2600620	0.2721125	0.312178
1	x_OP_y	0.248858	0.2639385	0.2916061	0.2966775	0.3095995	0.371030

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	x_OP_y	1.139334	1.119667	1.140253	1.140795	1.137763	1.188521

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

> OP
[1] "-"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5415532 289.3    7916910 422.9  7916910 422.9
Vcells 10548342  80.5   39038428 297.9 94934136 724.3

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

	expr	min	lq	mean	median	uq	max
1	x_OP_y	0.204926	0.240084	0.2728040	0.2592890	0.312930	0.392250
2	x_OP_y_R	0.234384	0.271386	0.3122882	0.3026965	0.339439	0.498607

	expr	min	lq	mean	median	uq	max
1	x_OP_y	1.000000	1.000000	1.000000	1.00000	1.000000	1.000000
2	x_OP_y_R	1.143749	1.130379	1.144734	1.16741	1.084712	1.271146

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

> OP
[1] "*"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5415595 289.3    7916910 422.9  7916910 422.9
Vcells 10549168  80.5   39038428 297.9 94934136 724.3

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

	expr	min	lq	mean	median	uq	max
1	x_OP_y	0.243579	0.272916	0.3246984	0.3000865	0.3279790	0.733109
2	x_OP_y_R	0.318497	0.356502	0.4213620	0.4039185	0.4390145	0.974944

	expr	min	lq	mean	median	uq	max
1	x_OP_y	1.000000	1.00000	1.000000	1.000000	1.000000	1.000000
2	x_OP_y_R	1.307572	1.30627	1.297703	1.346007	1.338545	1.329876

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

> OP
[1] "/"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5415658 289.3    7916910 422.9  7916910 422.9
Vcells 10549210  80.5   39038428 297.9 94934136 724.3

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

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.179733	0.216987	0.3375957	0.392457	0.4359585	0.500883
1	x_OP_y	0.214625	0.250102	0.3697244	0.418205	0.4710770	0.520053

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	x_OP_y	1.194132	1.152613	1.095169	1.065607	1.080555	1.038272

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

Data type “double”

Data

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

Results

10x10 vector

All elements

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

> OP
[1] "+"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5415721 289.3    7916910 422.9  7916910 422.9
Vcells 10663818  81.4   39038428 297.9 94934136 724.3

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

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.001636	0.0018535	0.0021567	0.0019715	0.0021135	0.015436
1	x_OP_y	0.004026	0.0043050	0.0046818	0.0044380	0.0046495	0.021790

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.00000	1.000000	1.000000	1.000000	1.000000	1.000000
1	x_OP_y	2.46088	2.322633	2.170772	2.251078	2.199905	1.411635

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

> OP
[1] "-"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5415784 289.3    7916910 422.9  7916910 422.9
Vcells 10664780  81.4   39038428 297.9 94934136 724.3

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

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.001827	0.0020375	0.0023971	0.002163	0.002267	0.022385
1	x_OP_y	0.004158	0.0044960	0.0049764	0.004698	0.004911	0.026761

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	x_OP_y	2.275862	2.206626	2.075986	2.171983	2.166299	1.195488

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

> OP
[1] "*"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5415847 289.3    7916910 422.9  7916910 422.9
Vcells 10664822  81.4   39038428 297.9 94934136 724.3

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

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.001600	0.0019155	0.0021650	0.0020500	0.002230	0.008718
1	x_OP_y	0.004074	0.0043510	0.0048329	0.0045505	0.004805	0.028044

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.00000	1.00000	1.000000	1.000000	1.000000	1.000000
1	x_OP_y	2.54625	2.27147	2.232264	2.219756	2.154708	3.216793

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

> OP
[1] "/"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5415910 289.3    7916910 422.9  7916910 422.9
Vcells 10664864  81.4   39038428 297.9 94934136 724.3

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

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.001672	0.0020015	0.0024178	0.002125	0.002333	0.019550
1	x_OP_y	0.004130	0.0043345	0.0049909	0.004536	0.004753	0.024665

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	x_OP_y	2.470096	2.165626	2.064233	2.134588	2.037291	1.261637

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

100x100 vector

All elements

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

> OP
[1] "+"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5415973 289.3    7916910 422.9  7916910 422.9
Vcells 10664990  81.4   39038428 297.9 94934136 724.3

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

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.015937	0.0169795	0.0179701	0.0173535	0.0177465	0.050092
1	x_OP_y	0.017434	0.0187035	0.0198595	0.0191415	0.0197625	0.051765

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.000000	1.000000	1.000000	1.000000	1.0000	1.000000
1	x_OP_y	1.093932	1.101534	1.105138	1.103034	1.1136	1.033399

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

> OP
[1] "-"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5416036 289.3    7916910 422.9  7916910 422.9
Vcells 10665032  81.4   39038428 297.9 94934136 724.3

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

	expr	min	lq	mean	median	uq	max
1	x_OP_y	0.018938	0.0194895	0.0206191	0.0197555	0.0202410	0.05393
2	x_OP_y_R	0.019021	0.0195865	0.0204662	0.0198735	0.0201755	0.05138

	expr	min	lq	mean	median	uq	max
1	x_OP_y	1.000000	1.000000	1.0000000	1.000000	1.000000	1.0000000
2	x_OP_y_R	1.004383	1.004977	0.9925855	1.005973	0.996764	0.9527165

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

> OP
[1] "*"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5416099 289.3    7916910 422.9  7916910 422.9
Vcells 10665074  81.4   39038428 297.9 94934136 724.3

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

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.016980	0.0177195	0.0188028	0.0180590	0.0184470	0.037352
1	x_OP_y	0.018799	0.0195505	0.0212312	0.0199105	0.0203675	0.051589

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	x_OP_y	1.107126	1.103333	1.129153	1.102525	1.104109	1.381158

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

> OP
[1] "/"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5416162 289.3    7916910 422.9  7916910 422.9
Vcells 10666221  81.4   39038428 297.9 94934136 724.3

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

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.019233	0.0197785	0.0207181	0.0199875	0.020269	0.052269
1	x_OP_y	0.021696	0.0223820	0.0230580	0.0227015	0.023075	0.040945

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
1	x_OP_y	1.128061	1.131633	1.112938	1.135785	1.138438	0.7833515

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

1000x10 vector

All elements

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

> OP
[1] "+"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5416225 289.3    7916910 422.9  7916910 422.9
Vcells 10667163  81.4   39038428 297.9 94934136 724.3

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

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.015873	0.016901	0.0185272	0.017479	0.0183390	0.048790
1	x_OP_y	0.017227	0.018503	0.0193969	0.019042	0.0196345	0.037878

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
1	x_OP_y	1.085302	1.094787	1.046944	1.089422	1.070642	0.7763476

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

> OP
[1] "-"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5416288 289.3    7916910 422.9  7916910 422.9
Vcells 10667205  81.4   39038428 297.9 94934136 724.3

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

	expr	min	lq	mean	median	uq	max
1	x_OP_y	0.017902	0.018679	0.0196484	0.0191175	0.0200615	0.036702
2	x_OP_y_R	0.018442	0.018923	0.0203766	0.0193960	0.0202425	0.050510

	expr	min	lq	mean	median	uq	max
1	x_OP_y	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	x_OP_y_R	1.030164	1.013063	1.037062	1.014568	1.009022	1.376219

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

> OP
[1] "*"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5416351 289.3    7916910 422.9  7916910 422.9
Vcells 10667247  81.4   39038428 297.9 94934136 724.3

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

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.016905	0.0175890	0.0180825	0.0179005	0.018162	0.025523
1	x_OP_y	0.018776	0.0193595	0.0208565	0.0197540	0.020330	0.069348

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.000000	1.000000	1.000000	1.000000	1.00000	1.000000
1	x_OP_y	1.110677	1.100659	1.153409	1.103545	1.11937	2.717079

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

> OP
[1] "/"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5416414 289.3    7916910 422.9  7916910 422.9
Vcells 10667289  81.4   39038428 297.9 94934136 724.3

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

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.019284	0.0197845	0.0203245	0.0199935	0.0203230	0.037019
1	x_OP_y	0.021935	0.0224130	0.0235238	0.0228125	0.0231405	0.055052

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	x_OP_y	1.137471	1.132856	1.157411	1.140996	1.138636	1.487128

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

10x1000 vector

All elements

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

> OP
[1] "+"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5416477 289.3    7916910 422.9  7916910 422.9
Vcells 10666347  81.4   39038428 297.9 94934136 724.3

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

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.016938	0.0177040	0.0182197	0.017965	0.0183240	0.025058
1	x_OP_y	0.018568	0.0191845	0.0202301	0.019488	0.0199165	0.051692

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	x_OP_y	1.096233	1.083625	1.110345	1.084776	1.086908	2.062894

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

> OP
[1] "-"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5416540 289.3    7916910 422.9  7916910 422.9
Vcells 10667717  81.4   39038428 297.9 94934136 724.3

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

	expr	min	lq	mean	median	uq	max
1	x_OP_y	0.017948	0.0191505	0.0202394	0.0195675	0.0199100	0.049754
2	x_OP_y_R	0.018297	0.0190745	0.0200302	0.0198000	0.0202385	0.033064

	expr	min	lq	mean	median	uq	max
1	x_OP_y	1.000000	1.0000000	1.0000000	1.000000	1.000000	1.0000000
2	x_OP_y_R	1.019445	0.9960314	0.9896627	1.011882	1.016499	0.6645496

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

> OP
[1] "*"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5416603 289.3    7916910 422.9  7916910 422.9
Vcells 10667759  81.4   39038428 297.9 94934136 724.3

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

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.015709	0.0169650	0.0178209	0.0172535	0.0176375	0.048522
1	x_OP_y	0.017060	0.0184585	0.0196658	0.0189410	0.0195765	0.049231

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	x_OP_y	1.086002	1.088034	1.103527	1.097806	1.109936	1.014612

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

> OP
[1] "/"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5416666 289.3    7916910 422.9  7916910 422.9
Vcells 10667801  81.4   39038428 297.9 94934136 724.3

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

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.018263	0.0193205	0.0200639	0.0198605	0.0203415	0.029922
1	x_OP_y	0.021068	0.0219500	0.0234009	0.0224820	0.0229995	0.054567

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	x_OP_y	1.153589	1.136099	1.166319	1.131996	1.130669	1.823641

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

100x1000 vector

All elements

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

> OP
[1] "+"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5416729 289.3    7916910 422.9  7916910 422.9
Vcells 10667927  81.4   39038428 297.9 94934136 724.3

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

	expr	min	lq	mean	median	uq	max
1	x_OP_y	0.126234	0.1426845	0.2809578	0.3434185	0.3911035	0.445408
2	x_OP_y_R	0.134252	0.1541160	0.2975768	0.3517350	0.3876935	0.452414

	expr	min	lq	mean	median	uq	max
1	x_OP_y	1.000000	1.000000	1.000000	1.000000	1.0000000	1.000000
2	x_OP_y_R	1.063517	1.080117	1.059151	1.024217	0.9912811	1.015729

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

> OP
[1] "-"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5416792 289.3    7916910 422.9  7916910 422.9
Vcells 10667969  81.4   39038428 297.9 94934136 724.3

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

	expr	min	lq	mean	median	uq	max
1	x_OP_y	0.119238	0.1326565	0.2733983	0.341503	0.3836775	0.413903
2	x_OP_y_R	0.147918	0.1673165	0.3080147	0.361057	0.4063995	0.448076

	expr	min	lq	mean	median	uq	max
1	x_OP_y	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	x_OP_y_R	1.240527	1.261276	1.126615	1.057259	1.059222	1.082563

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

> OP
[1] "*"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5416855 289.3    7916910 422.9  7916910 422.9
Vcells 10668011  81.4   39038428 297.9 94934136 724.3

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

	expr	min	lq	mean	median	uq	max
1	x_OP_y	0.122301	0.1324765	0.2802148	0.344373	0.3888135	0.552690
2	x_OP_y_R	0.129927	0.1464740	0.2970998	0.354247	0.4025355	0.521488

	expr	min	lq	mean	median	uq	max
1	x_OP_y	1.000000	1.00000	1.000000	1.000000	1.000000	1.0000000
2	x_OP_y_R	1.062354	1.10566	1.060257	1.028672	1.035292	0.9435452

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

> OP
[1] "/"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5416918 289.3    7916910 422.9  7916910 422.9
Vcells 10668053  81.4   39038428 297.9 94934136 724.3

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

	expr	min	lq	mean	median	uq	max
1	x_OP_y	0.144130	0.1598995	0.2913056	0.3468255	0.4011815	0.441631
2	x_OP_y_R	0.142693	0.1662600	0.3057355	0.3545605	0.4060015	0.455083

	expr	min	lq	mean	median	uq	max
1	x_OP_y	1.0000000	1.000000	1.000000	1.000000	1.000000	1.00000
2	x_OP_y_R	0.9900298	1.039778	1.049535	1.022302	1.012015	1.03046

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

1000x100 vector

All elements

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

> OP
[1] "+"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5416981 289.3    7916910 422.9  7916910 422.9
Vcells 10668995  81.4   39038428 297.9 94934136 724.3

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

	expr	min	lq	mean	median	uq	max
1	x_OP_y	0.130862	0.1433530	0.2965059	0.3681460	0.395757	0.487060
2	x_OP_y_R	0.139765	0.1530895	0.3111418	0.3809985	0.406488	0.480899

	expr	min	lq	mean	median	uq	max
1	x_OP_y	1.000000	1.00000	1.000000	1.000000	1.000000	1.0000000
2	x_OP_y_R	1.068034	1.06792	1.049362	1.034911	1.027115	0.9873506

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

> OP
[1] "-"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5417044 289.4    7916910 422.9  7916910 422.9
Vcells 10670627  81.5   39038428 297.9 94934136 724.3

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

	expr	min	lq	mean	median	uq	max
1	x_OP_y	0.126619	0.1427050	0.2913081	0.3531805	0.3961785	0.441303
2	x_OP_y_R	0.152317	0.1669595	0.3057430	0.3648710	0.4167585	0.453241

	expr	min	lq	mean	median	uq	max
1	x_OP_y	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	x_OP_y_R	1.202955	1.169963	1.049552	1.033101	1.051946	1.027052

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

> OP
[1] "*"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5417107 289.4    7916910 422.9  7916910 422.9
Vcells 10670669  81.5   39038428 297.9 94934136 724.3

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

	expr	min	lq	mean	median	uq	max
1	x_OP_y	0.130735	0.142609	0.2874260	0.350753	0.3958225	0.447575
2	x_OP_y_R	0.139397	0.152527	0.2977497	0.357040	0.4032735	0.449363

	expr	min	lq	mean	median	uq	max
1	x_OP_y	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	x_OP_y_R	1.066256	1.069547	1.035918	1.017924	1.018824	1.003995

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

> OP
[1] "/"
> stats <- microbenchmark(x_OP_y = x_OP_y(x, y, OP = OP, na.rm = FALSE), x_OP_y_R = x_OP_y_R(x, y, 
+     OP = OP, na.rm = FALSE), unit = "ms")
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5417170 289.4    7916910 422.9  7916910 422.9
Vcells 10670711  81.5   39038428 297.9 94934136 724.3

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

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	0.152285	0.1729235	0.3116299	0.3647405	0.4177895	0.470703
1	x_OP_y	0.155192	0.1752165	0.3124604	0.3659615	0.4216820	0.461336

	expr	min	lq	mean	median	uq	max
2	x_OP_y_R	1.000000	1.00000	1.000000	1.000000	1.000000	1.0000
1	x_OP_y	1.019089	1.01326	1.002665	1.003348	1.009317	0.9801

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

Appendix

Session information

R version 4.1.1 Patched (2021-08-10 r80727)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.5 LTS

Matrix products: default
BLAS:   /home/hb/software/R-devel/R-4-1-branch/lib/R/lib/libRblas.so
LAPACK: /home/hb/software/R-devel/R-4-1-branch/lib/R/lib/libRlapack.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] microbenchmark_1.4-7   matrixStats_0.60.0     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 1.18 mins.

Reproducibility

To reproduce this report, do:

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