matrixStats.benchmarks

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colProds() and rowProds() benchmarks on subsetted computation

This report benchmark the performance of colProds() and rowProds() on subsetted computation.

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 = "double")

Results

10x10 matrix

> X <- data[["10x10"]]
> rows <- sample.int(nrow(X), size = nrow(X) * 0.7)
> cols <- sample.int(ncol(X), size = ncol(X) * 0.7)
> X_S <- X[rows, cols]
> colStats <- microbenchmark(`colProds_X_S w/ direct` = colProds(X_S, method = "direct", na.rm = FALSE), 
+     `colProds_X_S w/ expSumLog` = colProds(X_S, method = "expSumLog", na.rm = FALSE), `colProds(X, rows, cols) w/ direct` = colProds(X, 
+         rows = rows, cols = cols, method = "direct", na.rm = FALSE), `colProds(X, rows, cols) w/ expSumLog` = colProds(X, 
+         rows = rows, cols = cols, method = "expSumLog", na.rm = FALSE), `colProds(X[rows, cols]) w/ direct` = colProds(X[rows, 
+         cols], method = "direct", na.rm = FALSE), `colProds(X[rows, cols]) w/ expSumLog` = colProds(X[rows, 
+         cols], method = "expSumLog", na.rm = FALSE), unit = "ms")
> X <- t(X)
> X_S <- t(X_S)
> rowStats <- microbenchmark(`rowProds_X_S w/ direct` = rowProds(X_S, method = "direct", na.rm = FALSE), 
+     `rowProds_X_S w/ expSumLog` = rowProds(X_S, method = "expSumLog", na.rm = FALSE), `rowProds(X, cols, rows) w/ direct` = rowProds(X, 
+         rows = cols, cols = rows, method = "direct", na.rm = FALSE), `rowProds(X, cols, rows) w/ expSumLog` = rowProds(X, 
+         rows = cols, cols = rows, method = "expSumLog", na.rm = FALSE), `rowProds(X[cols, rows]) w/ direct` = rowProds(X[cols, 
+         rows], method = "direct", na.rm = FALSE), `rowProds(X[cols, rows]) w/ expSumLog` = rowProds(X[cols, 
+         rows], method = "expSumLog", na.rm = FALSE), unit = "ms")

Table: Benchmarking of colProds_X_S w/ direct(), colProds_X_S w/ expSumLog(), colProds(X, rows, cols) w/ direct(), colProds(X, rows, cols) w/ expSumLog(), colProds(X[rows, cols]) w/ direct() and colProds(X[rows, cols]) w/ expSumLog() on 10x10 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colProds_X_S w/ direct	0.013104	0.0134945	0.0140341	0.0137760	0.0140640	0.030411
3	colProds(X, rows, cols) w/ direct	0.014451	0.0149205	0.0153308	0.0151895	0.0154840	0.022014
5	colProds(X[rows, cols]) w/ direct	0.014543	0.0150400	0.0179708	0.0152405	0.0155285	0.256450
2	colProds_X_S w/ expSumLog	0.020571	0.0210500	0.0216345	0.0213085	0.0217850	0.036162
4	colProds(X, rows, cols) w/ expSumLog	0.021690	0.0224525	0.0228605	0.0226650	0.0229620	0.034539
6	colProds(X[rows, cols]) w/ expSumLog	0.022037	0.0225500	0.0239190	0.0227200	0.0231395	0.077964

	expr	min	lq	mean	median	uq	max
1	colProds_X_S w/ direct	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
3	colProds(X, rows, cols) w/ direct	1.102793	1.105673	1.092397	1.102606	1.100967	0.7238828
5	colProds(X[rows, cols]) w/ direct	1.109814	1.114528	1.280507	1.106308	1.104131	8.4328039
2	colProds_X_S w/ expSumLog	1.569826	1.559895	1.541564	1.546784	1.548990	1.1891092
4	colProds(X, rows, cols) w/ expSumLog	1.655220	1.663826	1.628918	1.645253	1.632679	1.1357404
6	colProds(X[rows, cols]) w/ expSumLog	1.681700	1.671051	1.704344	1.649245	1.645300	2.5636776

Table: Benchmarking of rowProds_X_S w/ direct(), rowProds_X_S w/ expSumLog(), rowProds(X, cols, rows) w/ direct(), rowProds(X, cols, rows) w/ expSumLog(), rowProds(X[cols, rows]) w/ direct() and rowProds(X[cols, rows]) w/ expSumLog() on 10x10 data (transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	rowProds_X_S w/ direct	0.013297	0.0136985	0.0141482	0.0140395	0.0144565	0.016972
3	rowProds(X, cols, rows) w/ direct	0.014541	0.0152750	0.0159843	0.0155035	0.0160040	0.030366
5	rowProds(X[cols, rows]) w/ direct	0.014723	0.0152890	0.0157780	0.0155515	0.0158600	0.025774
2	rowProds_X_S w/ expSumLog	0.020789	0.0214310	0.0220587	0.0217115	0.0221525	0.046059
6	rowProds(X[cols, rows]) w/ expSumLog	0.022018	0.0227455	0.0234463	0.0230640	0.0235200	0.038802
4	rowProds(X, cols, rows) w/ expSumLog	0.022227	0.0227205	0.0259758	0.0230840	0.0235160	0.290159

	expr	min	lq	mean	median	uq	max
1	rowProds_X_S w/ direct	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	rowProds(X, cols, rows) w/ direct	1.093555	1.115086	1.129781	1.104277	1.107045	1.789182
5	rowProds(X[cols, rows]) w/ direct	1.107242	1.116108	1.115198	1.107696	1.097084	1.518619
2	rowProds_X_S w/ expSumLog	1.563435	1.564478	1.559125	1.546458	1.532356	2.713823
6	rowProds(X[cols, rows]) w/ expSumLog	1.655862	1.660437	1.657196	1.642794	1.626950	2.286236
4	rowProds(X, cols, rows) w/ expSumLog	1.671580	1.658612	1.835986	1.644218	1.626673	17.096335

Figure: Benchmarking of colProds_X_S w/ direct(), colProds_X_S w/ expSumLog(), colProds(X, rows, cols) w/ direct(), colProds(X, rows, cols) w/ expSumLog(), colProds(X[rows, cols]) w/ direct() and colProds(X[rows, cols]) w/ expSumLog() on 10x10 data as well as rowProds_X_S w/ direct(), rowProds_X_S w/ expSumLog(), rowProds(X, cols, rows) w/ direct(), rowProds(X, cols, rows) w/ expSumLog(), rowProds(X[cols, rows]) w/ direct() and rowProds(X[cols, rows]) w/ expSumLog() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colProds_X_S w/ direct() and rowProds_X_S w/ direct() on 10x10 data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colProds_X_S w/ direct	13.104	13.4945	14.03414	13.7760	14.0640	30.411
2	rowProds_X_S w/ direct	13.297	13.6985	14.14816	14.0395	14.4565	16.972

	expr	min	lq	mean	median	uq	max
1	colProds_X_S w/ direct	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	rowProds_X_S w/ direct	1.014728	1.015117	1.008125	1.019127	1.027908	0.5580875

Figure: Benchmarking of colProds_X_S w/ direct() and rowProds_X_S w/ direct() on 10x10 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

100x100 matrix

> X <- data[["100x100"]]
> rows <- sample.int(nrow(X), size = nrow(X) * 0.7)
> cols <- sample.int(ncol(X), size = ncol(X) * 0.7)
> X_S <- X[rows, cols]
> colStats <- microbenchmark(`colProds_X_S w/ direct` = colProds(X_S, method = "direct", na.rm = FALSE), 
+     `colProds_X_S w/ expSumLog` = colProds(X_S, method = "expSumLog", na.rm = FALSE), `colProds(X, rows, cols) w/ direct` = colProds(X, 
+         rows = rows, cols = cols, method = "direct", na.rm = FALSE), `colProds(X, rows, cols) w/ expSumLog` = colProds(X, 
+         rows = rows, cols = cols, method = "expSumLog", na.rm = FALSE), `colProds(X[rows, cols]) w/ direct` = colProds(X[rows, 
+         cols], method = "direct", na.rm = FALSE), `colProds(X[rows, cols]) w/ expSumLog` = colProds(X[rows, 
+         cols], method = "expSumLog", na.rm = FALSE), unit = "ms")
> X <- t(X)
> X_S <- t(X_S)
> rowStats <- microbenchmark(`rowProds_X_S w/ direct` = rowProds(X_S, method = "direct", na.rm = FALSE), 
+     `rowProds_X_S w/ expSumLog` = rowProds(X_S, method = "expSumLog", na.rm = FALSE), `rowProds(X, cols, rows) w/ direct` = rowProds(X, 
+         rows = cols, cols = rows, method = "direct", na.rm = FALSE), `rowProds(X, cols, rows) w/ expSumLog` = rowProds(X, 
+         rows = cols, cols = rows, method = "expSumLog", na.rm = FALSE), `rowProds(X[cols, rows]) w/ direct` = rowProds(X[cols, 
+         rows], method = "direct", na.rm = FALSE), `rowProds(X[cols, rows]) w/ expSumLog` = rowProds(X[cols, 
+         rows], method = "expSumLog", na.rm = FALSE), unit = "ms")

Table: Benchmarking of colProds_X_S w/ direct(), colProds_X_S w/ expSumLog(), colProds(X, rows, cols) w/ direct(), colProds(X, rows, cols) w/ expSumLog(), colProds(X[rows, cols]) w/ direct() and colProds(X[rows, cols]) w/ expSumLog() on 100x100 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colProds_X_S w/ direct	0.073709	0.0749175	0.0905664	0.0809315	0.0977605	0.200449
3	colProds(X, rows, cols) w/ direct	0.083946	0.0864785	0.1019647	0.0934380	0.1096635	0.172019
5	colProds(X[rows, cols]) w/ direct	0.084412	0.0861960	0.1026389	0.0947585	0.1104975	0.189805
2	colProds_X_S w/ expSumLog	0.220354	0.2223560	0.2641322	0.2398320	0.2857195	0.428113
6	colProds(X[rows, cols]) w/ expSumLog	0.230429	0.2335475	0.2738112	0.2508620	0.2991160	0.402502
4	colProds(X, rows, cols) w/ expSumLog	0.231497	0.2343245	0.2724815	0.2576330	0.2907995	0.420745

	expr	min	lq	mean	median	uq	max
1	colProds_X_S w/ direct	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
3	colProds(X, rows, cols) w/ direct	1.138884	1.154316	1.125855	1.154532	1.121757	0.8581684
5	colProds(X[rows, cols]) w/ direct	1.145206	1.150546	1.133299	1.170848	1.130288	0.9468992
2	colProds_X_S w/ expSumLog	2.989513	2.968011	2.916447	2.963395	2.922648	2.1357702
6	colProds(X[rows, cols]) w/ expSumLog	3.126199	3.117396	3.023318	3.099683	3.059682	2.0080020
4	colProds(X, rows, cols) w/ expSumLog	3.140688	3.127767	3.008636	3.183346	2.974611	2.0990127

Table: Benchmarking of rowProds_X_S w/ direct(), rowProds_X_S w/ expSumLog(), rowProds(X, cols, rows) w/ direct(), rowProds(X, cols, rows) w/ expSumLog(), rowProds(X[cols, rows]) w/ direct() and rowProds(X[cols, rows]) w/ expSumLog() on 100x100 data (transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	rowProds_X_S w/ direct	0.085764	0.0880185	0.1047172	0.1042270	0.1113325	0.158051
5	rowProds(X[cols, rows]) w/ direct	0.096795	0.0980180	0.1143780	0.1058170	0.1240545	0.191773
3	rowProds(X, cols, rows) w/ direct	0.096302	0.0986680	0.1129483	0.1094275	0.1206955	0.207510
6	rowProds(X[cols, rows]) w/ expSumLog	0.242107	0.2439025	0.2871896	0.2593440	0.3062615	0.826660
2	rowProds_X_S w/ expSumLog	0.231956	0.2341890	0.2838343	0.2658600	0.3061985	0.720162
4	rowProds(X, cols, rows) w/ expSumLog	0.240587	0.2451280	0.2930772	0.2885740	0.3132735	0.456473

	expr	min	lq	mean	median	uq	max
1	rowProds_X_S w/ direct	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
5	rowProds(X[cols, rows]) w/ direct	1.128620	1.113607	1.092256	1.015255	1.114270	1.213361
3	rowProds(X, cols, rows) w/ direct	1.122872	1.120992	1.078603	1.049896	1.084099	1.312931
6	rowProds(X[cols, rows]) w/ expSumLog	2.822944	2.771037	2.742526	2.488261	2.750872	5.230337
2	rowProds_X_S w/ expSumLog	2.704585	2.660679	2.710485	2.550779	2.750307	4.556517
4	rowProds(X, cols, rows) w/ expSumLog	2.805221	2.784960	2.798750	2.768707	2.813855	2.888137

Figure: Benchmarking of colProds_X_S w/ direct(), colProds_X_S w/ expSumLog(), colProds(X, rows, cols) w/ direct(), colProds(X, rows, cols) w/ expSumLog(), colProds(X[rows, cols]) w/ direct() and colProds(X[rows, cols]) w/ expSumLog() on 100x100 data as well as rowProds_X_S w/ direct(), rowProds_X_S w/ expSumLog(), rowProds(X, cols, rows) w/ direct(), rowProds(X, cols, rows) w/ expSumLog(), rowProds(X[cols, rows]) w/ direct() and rowProds(X[cols, rows]) w/ expSumLog() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colProds_X_S w/ direct() and rowProds_X_S w/ direct() on 100x100 data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colProds_X_S w/ direct	73.709	74.9175	90.56644	80.9315	97.7605	200.449
2	rowProds_X_S w/ direct	85.764	88.0185	104.71719	104.2270	111.3325	158.051

	expr	min	lq	mean	median	uq	max
1	colProds_X_S w/ direct	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	rowProds_X_S w/ direct	1.163549	1.174872	1.156247	1.287842	1.138829	0.7884849

Figure: Benchmarking of colProds_X_S w/ direct() and rowProds_X_S w/ direct() on 100x100 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

1000x10 matrix

> X <- data[["1000x10"]]
> rows <- sample.int(nrow(X), size = nrow(X) * 0.7)
> cols <- sample.int(ncol(X), size = ncol(X) * 0.7)
> X_S <- X[rows, cols]
> colStats <- microbenchmark(`colProds_X_S w/ direct` = colProds(X_S, method = "direct", na.rm = FALSE), 
+     `colProds_X_S w/ expSumLog` = colProds(X_S, method = "expSumLog", na.rm = FALSE), `colProds(X, rows, cols) w/ direct` = colProds(X, 
+         rows = rows, cols = cols, method = "direct", na.rm = FALSE), `colProds(X, rows, cols) w/ expSumLog` = colProds(X, 
+         rows = rows, cols = cols, method = "expSumLog", na.rm = FALSE), `colProds(X[rows, cols]) w/ direct` = colProds(X[rows, 
+         cols], method = "direct", na.rm = FALSE), `colProds(X[rows, cols]) w/ expSumLog` = colProds(X[rows, 
+         cols], method = "expSumLog", na.rm = FALSE), unit = "ms")
> X <- t(X)
> X_S <- t(X_S)
> rowStats <- microbenchmark(`rowProds_X_S w/ direct` = rowProds(X_S, method = "direct", na.rm = FALSE), 
+     `rowProds_X_S w/ expSumLog` = rowProds(X_S, method = "expSumLog", na.rm = FALSE), `rowProds(X, cols, rows) w/ direct` = rowProds(X, 
+         rows = cols, cols = rows, method = "direct", na.rm = FALSE), `rowProds(X, cols, rows) w/ expSumLog` = rowProds(X, 
+         rows = cols, cols = rows, method = "expSumLog", na.rm = FALSE), `rowProds(X[cols, rows]) w/ direct` = rowProds(X[cols, 
+         rows], method = "direct", na.rm = FALSE), `rowProds(X[cols, rows]) w/ expSumLog` = rowProds(X[cols, 
+         rows], method = "expSumLog", na.rm = FALSE), unit = "ms")

Table: Benchmarking of colProds_X_S w/ direct(), colProds_X_S w/ expSumLog(), colProds(X, rows, cols) w/ direct(), colProds(X, rows, cols) w/ expSumLog(), colProds(X[rows, cols]) w/ direct() and colProds(X[rows, cols]) w/ expSumLog() on 1000x10 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colProds_X_S w/ direct	0.030052	0.0340035	0.0390363	0.0376965	0.0416195	0.059581
5	colProds(X[rows, cols]) w/ direct	0.042652	0.0467585	0.0543521	0.0518050	0.0613195	0.090626
3	colProds(X, rows, cols) w/ direct	0.042472	0.0467015	0.0546875	0.0526190	0.0611205	0.086783
2	colProds_X_S w/ expSumLog	0.148410	0.1601030	0.1790544	0.1712910	0.1900625	0.301234
6	colProds(X[rows, cols]) w/ expSumLog	0.160447	0.1735535	0.1978269	0.1889030	0.2119135	0.332199
4	colProds(X, rows, cols) w/ expSumLog	0.161044	0.1750000	0.1971329	0.1917990	0.2140610	0.286676

	expr	min	lq	mean	median	uq	max
1	colProds_X_S w/ direct	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
5	colProds(X[rows, cols]) w/ direct	1.419273	1.375108	1.392346	1.374265	1.473336	1.521055
3	colProds(X, rows, cols) w/ direct	1.413284	1.373432	1.400938	1.395859	1.468554	1.456555
2	colProds_X_S w/ expSumLog	4.938440	4.708427	4.586869	4.543950	4.566669	5.055873
6	colProds(X[rows, cols]) w/ expSumLog	5.338979	5.103989	5.067766	5.011155	5.091688	5.575586
4	colProds(X, rows, cols) w/ expSumLog	5.358845	5.146529	5.049988	5.087979	5.143286	4.811534

Table: Benchmarking of rowProds_X_S w/ direct(), rowProds_X_S w/ expSumLog(), rowProds(X, cols, rows) w/ direct(), rowProds(X, cols, rows) w/ expSumLog(), rowProds(X[cols, rows]) w/ direct() and rowProds(X[cols, rows]) w/ expSumLog() on 1000x10 data (transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	rowProds_X_S w/ direct	0.041595	0.0454475	0.0525412	0.0525010	0.0550535	0.071829
5	rowProds(X[cols, rows]) w/ direct	0.053410	0.0611800	0.0688919	0.0690400	0.0718465	0.092042
3	rowProds(X, cols, rows) w/ direct	0.053330	0.0643215	0.0701024	0.0696200	0.0723155	0.094215
2	rowProds_X_S w/ expSumLog	0.148190	0.1758040	0.1940258	0.1891415	0.2081830	0.284685
6	rowProds(X[cols, rows]) w/ expSumLog	0.161066	0.1798505	0.2034044	0.2043790	0.2124210	0.287890
4	rowProds(X, cols, rows) w/ expSumLog	0.160327	0.1801010	0.2069060	0.2047570	0.2254720	0.269866

	expr	min	lq	mean	median	uq	max
1	rowProds_X_S w/ direct	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
5	rowProds(X[cols, rows]) w/ direct	1.284049	1.346169	1.311198	1.315023	1.305031	1.281404
3	rowProds(X, cols, rows) w/ direct	1.282125	1.415292	1.334236	1.326070	1.313549	1.311657
2	rowProds_X_S w/ expSumLog	3.562688	3.868288	3.692829	3.602627	3.781467	3.963371
6	rowProds(X[cols, rows]) w/ expSumLog	3.872244	3.957324	3.871329	3.892859	3.858447	4.007991
4	rowProds(X, cols, rows) w/ expSumLog	3.854478	3.962836	3.937975	3.900059	4.095507	3.757062

Figure: Benchmarking of colProds_X_S w/ direct(), colProds_X_S w/ expSumLog(), colProds(X, rows, cols) w/ direct(), colProds(X, rows, cols) w/ expSumLog(), colProds(X[rows, cols]) w/ direct() and colProds(X[rows, cols]) w/ expSumLog() on 1000x10 data as well as rowProds_X_S w/ direct(), rowProds_X_S w/ expSumLog(), rowProds(X, cols, rows) w/ direct(), rowProds(X, cols, rows) w/ expSumLog(), rowProds(X[cols, rows]) w/ direct() and rowProds(X[cols, rows]) w/ expSumLog() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colProds_X_S w/ direct() and rowProds_X_S w/ direct() on 1000x10 data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colProds_X_S w/ direct	30.052	34.0035	39.03631	37.6965	41.6195	59.581
2	rowProds_X_S w/ direct	41.595	45.4475	52.54123	52.5010	55.0535	71.829

	expr	min	lq	mean	median	uq	max
1	colProds_X_S w/ direct	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowProds_X_S w/ direct	1.384101	1.336554	1.345958	1.392729	1.322781	1.205569

Figure: Benchmarking of colProds_X_S w/ direct() and rowProds_X_S w/ direct() on 1000x10 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

10x1000 matrix

> X <- data[["10x1000"]]
> rows <- sample.int(nrow(X), size = nrow(X) * 0.7)
> cols <- sample.int(ncol(X), size = ncol(X) * 0.7)
> X_S <- X[rows, cols]
> colStats <- microbenchmark(`colProds_X_S w/ direct` = colProds(X_S, method = "direct", na.rm = FALSE), 
+     `colProds_X_S w/ expSumLog` = colProds(X_S, method = "expSumLog", na.rm = FALSE), `colProds(X, rows, cols) w/ direct` = colProds(X, 
+         rows = rows, cols = cols, method = "direct", na.rm = FALSE), `colProds(X, rows, cols) w/ expSumLog` = colProds(X, 
+         rows = rows, cols = cols, method = "expSumLog", na.rm = FALSE), `colProds(X[rows, cols]) w/ direct` = colProds(X[rows, 
+         cols], method = "direct", na.rm = FALSE), `colProds(X[rows, cols]) w/ expSumLog` = colProds(X[rows, 
+         cols], method = "expSumLog", na.rm = FALSE), unit = "ms")
> X <- t(X)
> X_S <- t(X_S)
> rowStats <- microbenchmark(`rowProds_X_S w/ direct` = rowProds(X_S, method = "direct", na.rm = FALSE), 
+     `rowProds_X_S w/ expSumLog` = rowProds(X_S, method = "expSumLog", na.rm = FALSE), `rowProds(X, cols, rows) w/ direct` = rowProds(X, 
+         rows = cols, cols = rows, method = "direct", na.rm = FALSE), `rowProds(X, cols, rows) w/ expSumLog` = rowProds(X, 
+         rows = cols, cols = rows, method = "expSumLog", na.rm = FALSE), `rowProds(X[cols, rows]) w/ direct` = rowProds(X[cols, 
+         rows], method = "direct", na.rm = FALSE), `rowProds(X[cols, rows]) w/ expSumLog` = rowProds(X[cols, 
+         rows], method = "expSumLog", na.rm = FALSE), unit = "ms")

Table: Benchmarking of colProds_X_S w/ direct(), colProds_X_S w/ expSumLog(), colProds(X, rows, cols) w/ direct(), colProds(X, rows, cols) w/ expSumLog(), colProds(X[rows, cols]) w/ direct() and colProds(X[rows, cols]) w/ expSumLog() on 10x1000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colProds_X_S w/ direct	0.523648	0.5356855	0.5759407	0.5537635	0.587121	0.875800
5	colProds(X[rows, cols]) w/ direct	0.535452	0.5474045	0.5743448	0.5597895	0.585659	0.784047
3	colProds(X, rows, cols) w/ direct	0.534339	0.5455865	0.5873932	0.5609605	0.589269	0.891698
2	colProds_X_S w/ expSumLog	1.024895	1.0426410	1.1222524	1.0650560	1.158451	1.669401
6	colProds(X[rows, cols]) w/ expSumLog	1.037188	1.0502250	1.1209347	1.0700640	1.135411	1.583419
4	colProds(X, rows, cols) w/ expSumLog	1.032517	1.0540280	1.2408611	1.0860820	1.169466	6.607324

	expr	min	lq	mean	median	uq	max
1	colProds_X_S w/ direct	1.000000	1.000000	1.000000	1.000000	1.0000000	1.0000000
5	colProds(X[rows, cols]) w/ direct	1.022542	1.021877	0.997229	1.010882	0.9975099	0.8952352
3	colProds(X, rows, cols) w/ direct	1.020416	1.018483	1.019885	1.012997	1.0036585	1.0181525
2	colProds_X_S w/ expSumLog	1.957221	1.946368	1.948555	1.923305	1.9731044	1.9061441
6	colProds(X[rows, cols]) w/ expSumLog	1.980697	1.960525	1.946267	1.932348	1.9338620	1.8079687
4	colProds(X, rows, cols) w/ expSumLog	1.971777	1.967625	2.154494	1.961274	1.9918645	7.5443298

Table: Benchmarking of rowProds_X_S w/ direct(), rowProds_X_S w/ expSumLog(), rowProds(X, cols, rows) w/ direct(), rowProds(X, cols, rows) w/ expSumLog(), rowProds(X[cols, rows]) w/ direct() and rowProds(X[cols, rows]) w/ expSumLog() on 10x1000 data (transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	rowProds_X_S w/ direct	0.525206	0.537019	0.5949731	0.5571330	0.598142	1.321915
3	rowProds(X, cols, rows) w/ direct	0.538670	0.548481	0.6469377	0.5689670	0.599152	5.934930
5	rowProds(X[cols, rows]) w/ direct	0.536763	0.549954	0.6483152	0.5736465	0.600296	5.872529
6	rowProds(X[cols, rows]) w/ expSumLog	1.036684	1.057124	1.1446338	1.0820275	1.148299	2.715708
4	rowProds(X, cols, rows) w/ expSumLog	1.033060	1.060096	1.1304942	1.0821795	1.141911	1.701270
2	rowProds_X_S w/ expSumLog	1.024732	1.051346	1.1299487	1.0837890	1.145460	1.937171

	expr	min	lq	mean	median	uq	max
1	rowProds_X_S w/ direct	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	rowProds(X, cols, rows) w/ direct	1.025636	1.021344	1.087339	1.021241	1.001689	4.489646
5	rowProds(X[cols, rows]) w/ direct	1.022005	1.024087	1.089655	1.029640	1.003601	4.442441
6	rowProds(X[cols, rows]) w/ expSumLog	1.973862	1.968504	1.923841	1.942135	1.919777	2.054374
4	rowProds(X, cols, rows) w/ expSumLog	1.966962	1.974037	1.900076	1.942408	1.909096	1.286974
2	rowProds_X_S w/ expSumLog	1.951105	1.957744	1.899159	1.945297	1.915031	1.465428

Figure: Benchmarking of colProds_X_S w/ direct(), colProds_X_S w/ expSumLog(), colProds(X, rows, cols) w/ direct(), colProds(X, rows, cols) w/ expSumLog(), colProds(X[rows, cols]) w/ direct() and colProds(X[rows, cols]) w/ expSumLog() on 10x1000 data as well as rowProds_X_S w/ direct(), rowProds_X_S w/ expSumLog(), rowProds(X, cols, rows) w/ direct(), rowProds(X, cols, rows) w/ expSumLog(), rowProds(X[cols, rows]) w/ direct() and rowProds(X[cols, rows]) w/ expSumLog() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colProds_X_S w/ direct() and rowProds_X_S w/ direct() on 10x1000 data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colProds_X_S w/ direct	523.648	535.6855	575.9407	553.7635	587.121	875.800
2	rowProds_X_S w/ direct	525.206	537.0190	594.9731	557.1330	598.142	1321.915

	expr	min	lq	mean	median	uq	max
1	colProds_X_S w/ direct	1.000000	1.000000	1.000000	1.000000	1.000000	1.00000
2	rowProds_X_S w/ direct	1.002975	1.002489	1.033046	1.006085	1.018771	1.50938

Figure: Benchmarking of colProds_X_S w/ direct() and rowProds_X_S w/ direct() on 10x1000 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

100x1000 matrix

> X <- data[["100x1000"]]
> rows <- sample.int(nrow(X), size = nrow(X) * 0.7)
> cols <- sample.int(ncol(X), size = ncol(X) * 0.7)
> X_S <- X[rows, cols]
> colStats <- microbenchmark(`colProds_X_S w/ direct` = colProds(X_S, method = "direct", na.rm = FALSE), 
+     `colProds_X_S w/ expSumLog` = colProds(X_S, method = "expSumLog", na.rm = FALSE), `colProds(X, rows, cols) w/ direct` = colProds(X, 
+         rows = rows, cols = cols, method = "direct", na.rm = FALSE), `colProds(X, rows, cols) w/ expSumLog` = colProds(X, 
+         rows = rows, cols = cols, method = "expSumLog", na.rm = FALSE), `colProds(X[rows, cols]) w/ direct` = colProds(X[rows, 
+         cols], method = "direct", na.rm = FALSE), `colProds(X[rows, cols]) w/ expSumLog` = colProds(X[rows, 
+         cols], method = "expSumLog", na.rm = FALSE), unit = "ms")
> X <- t(X)
> X_S <- t(X_S)
> rowStats <- microbenchmark(`rowProds_X_S w/ direct` = rowProds(X_S, method = "direct", na.rm = FALSE), 
+     `rowProds_X_S w/ expSumLog` = rowProds(X_S, method = "expSumLog", na.rm = FALSE), `rowProds(X, cols, rows) w/ direct` = rowProds(X, 
+         rows = cols, cols = rows, method = "direct", na.rm = FALSE), `rowProds(X, cols, rows) w/ expSumLog` = rowProds(X, 
+         rows = cols, cols = rows, method = "expSumLog", na.rm = FALSE), `rowProds(X[cols, rows]) w/ direct` = rowProds(X[cols, 
+         rows], method = "direct", na.rm = FALSE), `rowProds(X[cols, rows]) w/ expSumLog` = rowProds(X[cols, 
+         rows], method = "expSumLog", na.rm = FALSE), unit = "ms")

Table: Benchmarking of colProds_X_S w/ direct(), colProds_X_S w/ expSumLog(), colProds(X, rows, cols) w/ direct(), colProds(X, rows, cols) w/ expSumLog(), colProds(X[rows, cols]) w/ direct() and colProds(X[rows, cols]) w/ expSumLog() on 100x1000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colProds_X_S w/ direct	0.693250	0.7210880	1.0752459	0.7540725	0.861600	24.251464
5	colProds(X[rows, cols]) w/ direct	0.786981	0.8192815	0.9336942	0.8533490	0.962303	2.250769
3	colProds(X, rows, cols) w/ direct	0.796398	0.8288000	0.9344435	0.8703645	0.991692	1.634443
2	colProds_X_S w/ expSumLog	2.137575	2.2014775	2.4854195	2.2775650	2.559591	5.828753
6	colProds(X[rows, cols]) w/ expSumLog	2.258601	2.2933745	2.5132972	2.3563435	2.601824	5.138715
4	colProds(X, rows, cols) w/ expSumLog	2.254370	2.3022690	2.7948370	2.3669620	2.582371	25.151733

	expr	min	lq	mean	median	uq	max
1	colProds_X_S w/ direct	1.000000	1.000000	1.0000000	1.000000	1.000000	1.0000000
5	colProds(X[rows, cols]) w/ direct	1.135205	1.136174	0.8683541	1.131654	1.116879	0.0928096
3	colProds(X, rows, cols) w/ direct	1.148789	1.149374	0.8690509	1.154219	1.150989	0.0673956
2	colProds_X_S w/ expSumLog	3.083411	3.052994	2.3114894	3.020353	2.970742	0.2403464
6	colProds(X[rows, cols]) w/ expSumLog	3.257989	3.180436	2.3374163	3.124824	3.019758	0.2118930
4	colProds(X, rows, cols) w/ expSumLog	3.251886	3.192771	2.5992538	3.138905	2.997180	1.0371223

Table: Benchmarking of rowProds_X_S w/ direct(), rowProds_X_S w/ expSumLog(), rowProds(X, cols, rows) w/ direct(), rowProds(X, cols, rows) w/ expSumLog(), rowProds(X[cols, rows]) w/ direct() and rowProds(X[cols, rows]) w/ expSumLog() on 100x1000 data (transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	rowProds_X_S w/ direct	0.826818	0.8845375	1.008451	0.9732775	1.076523	1.749637
5	rowProds(X[cols, rows]) w/ direct	0.980743	1.0651300	1.194578	1.1332255	1.265120	2.544259
3	rowProds(X, cols, rows) w/ direct	0.996019	1.0702235	1.184752	1.1399655	1.265089	1.923425
2	rowProds_X_S w/ expSumLog	2.294785	2.4054455	2.691871	2.5605495	2.800074	4.140237
4	rowProds(X, cols, rows) w/ expSumLog	2.469234	2.5475565	2.855325	2.7065260	3.093694	4.504706
6	rowProds(X[cols, rows]) w/ expSumLog	2.457406	2.5713975	3.324232	2.7079870	2.940023	27.454912

	expr	min	lq	mean	median	uq	max
1	rowProds_X_S w/ direct	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
5	rowProds(X[cols, rows]) w/ direct	1.186165	1.204166	1.184567	1.164340	1.175191	1.454164
3	rowProds(X, cols, rows) w/ direct	1.204641	1.209924	1.174823	1.171265	1.175163	1.099328
2	rowProds_X_S w/ expSumLog	2.775441	2.719439	2.669312	2.630853	2.601035	2.366341
4	rowProds(X, cols, rows) w/ expSumLog	2.986430	2.880100	2.831397	2.780837	2.873784	2.574652
6	rowProds(X[cols, rows]) w/ expSumLog	2.972125	2.907053	3.296374	2.782338	2.731036	15.691776

Figure: Benchmarking of colProds_X_S w/ direct(), colProds_X_S w/ expSumLog(), colProds(X, rows, cols) w/ direct(), colProds(X, rows, cols) w/ expSumLog(), colProds(X[rows, cols]) w/ direct() and colProds(X[rows, cols]) w/ expSumLog() on 100x1000 data as well as rowProds_X_S w/ direct(), rowProds_X_S w/ expSumLog(), rowProds(X, cols, rows) w/ direct(), rowProds(X, cols, rows) w/ expSumLog(), rowProds(X[cols, rows]) w/ direct() and rowProds(X[cols, rows]) w/ expSumLog() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colProds_X_S w/ direct() and rowProds_X_S w/ direct() on 100x1000 data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colProds_X_S w/ direct	693.250	721.0880	1075.246	754.0725	861.600	24251.464
2	rowProds_X_S w/ direct	826.818	884.5375	1008.451	973.2775	1076.523	1749.637

	expr	min	lq	mean	median	uq	max
1	colProds_X_S w/ direct	1.000000	1.000000	1.0000000	1.000000	1.000000	1.0000000
2	rowProds_X_S w/ direct	1.192669	1.226671	0.9378796	1.290695	1.249446	0.0721456

Figure: Benchmarking of colProds_X_S w/ direct() and rowProds_X_S w/ direct() on 100x1000 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

1000x100 matrix

> X <- data[["1000x100"]]
> rows <- sample.int(nrow(X), size = nrow(X) * 0.7)
> cols <- sample.int(ncol(X), size = ncol(X) * 0.7)
> X_S <- X[rows, cols]
> colStats <- microbenchmark(`colProds_X_S w/ direct` = colProds(X_S, method = "direct", na.rm = FALSE), 
+     `colProds_X_S w/ expSumLog` = colProds(X_S, method = "expSumLog", na.rm = FALSE), `colProds(X, rows, cols) w/ direct` = colProds(X, 
+         rows = rows, cols = cols, method = "direct", na.rm = FALSE), `colProds(X, rows, cols) w/ expSumLog` = colProds(X, 
+         rows = rows, cols = cols, method = "expSumLog", na.rm = FALSE), `colProds(X[rows, cols]) w/ direct` = colProds(X[rows, 
+         cols], method = "direct", na.rm = FALSE), `colProds(X[rows, cols]) w/ expSumLog` = colProds(X[rows, 
+         cols], method = "expSumLog", na.rm = FALSE), unit = "ms")
> X <- t(X)
> X_S <- t(X_S)
> rowStats <- microbenchmark(`rowProds_X_S w/ direct` = rowProds(X_S, method = "direct", na.rm = FALSE), 
+     `rowProds_X_S w/ expSumLog` = rowProds(X_S, method = "expSumLog", na.rm = FALSE), `rowProds(X, cols, rows) w/ direct` = rowProds(X, 
+         rows = cols, cols = rows, method = "direct", na.rm = FALSE), `rowProds(X, cols, rows) w/ expSumLog` = rowProds(X, 
+         rows = cols, cols = rows, method = "expSumLog", na.rm = FALSE), `rowProds(X[cols, rows]) w/ direct` = rowProds(X[cols, 
+         rows], method = "direct", na.rm = FALSE), `rowProds(X[cols, rows]) w/ expSumLog` = rowProds(X[cols, 
+         rows], method = "expSumLog", na.rm = FALSE), unit = "ms")

Table: Benchmarking of colProds_X_S w/ direct(), colProds_X_S w/ expSumLog(), colProds(X, rows, cols) w/ direct(), colProds(X, rows, cols) w/ expSumLog(), colProds(X[rows, cols]) w/ direct() and colProds(X[rows, cols]) w/ expSumLog() on 1000x100 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colProds_X_S w/ direct	0.238667	0.2472480	0.2776331	0.2557235	0.2985885	0.482010
5	colProds(X[rows, cols]) w/ direct	0.335498	0.3433565	0.4005840	0.3552270	0.4297075	0.723056
3	colProds(X, rows, cols) w/ direct	0.335107	0.3439390	0.3864759	0.3559160	0.3932580	0.688277
2	colProds_X_S w/ expSumLog	1.307053	1.3253225	1.4385322	1.3494585	1.4848140	2.302503
6	colProds(X[rows, cols]) w/ expSumLog	1.408062	1.4204860	1.7333478	1.4364855	1.6093775	11.177601
4	colProds(X, rows, cols) w/ expSumLog	1.403216	1.4319280	1.6079715	1.4876970	1.7257095	2.530356

	expr	min	lq	mean	median	uq	max
1	colProds_X_S w/ direct	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
5	colProds(X[rows, cols]) w/ direct	1.405716	1.388713	1.442854	1.389106	1.439129	1.500085
3	colProds(X, rows, cols) w/ direct	1.404078	1.391069	1.392038	1.391800	1.317057	1.427931
2	colProds_X_S w/ expSumLog	5.476471	5.360296	5.181414	5.277022	4.972777	4.776878
6	colProds(X[rows, cols]) w/ expSumLog	5.899693	5.745187	6.243303	5.617339	5.389951	23.189563
4	colProds(X, rows, cols) w/ expSumLog	5.879388	5.791464	5.791713	5.817600	5.779558	5.249592

Table: Benchmarking of rowProds_X_S w/ direct(), rowProds_X_S w/ expSumLog(), rowProds(X, cols, rows) w/ direct(), rowProds(X, cols, rows) w/ expSumLog(), rowProds(X[cols, rows]) w/ direct() and rowProds(X[cols, rows]) w/ expSumLog() on 1000x100 data (transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	rowProds_X_S w/ direct	0.374254	0.3786410	0.4088243	0.3835165	0.4063745	0.701556
5	rowProds(X[cols, rows]) w/ direct	0.481832	0.4918155	0.5228188	0.4981410	0.5193815	0.813087
3	rowProds(X, cols, rows) w/ direct	0.484074	0.4920385	0.6275272	0.5000475	0.5806940	8.926097
2	rowProds_X_S w/ expSumLog	1.463210	1.4706190	1.6064194	1.4935535	1.6759630	2.421766
6	rowProds(X[cols, rows]) w/ expSumLog	1.570197	1.5803785	1.7876741	1.5888510	1.7326515	10.818760
4	rowProds(X, cols, rows) w/ expSumLog	1.567760	1.5828360	1.6866927	1.6057930	1.7217105	2.216337

	expr	min	lq	mean	median	uq	max
1	rowProds_X_S w/ direct	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
5	rowProds(X[cols, rows]) w/ direct	1.287446	1.298897	1.278835	1.298878	1.278086	1.158977
3	rowProds(X, cols, rows) w/ direct	1.293437	1.299486	1.534956	1.303849	1.428963	12.723285
2	rowProds_X_S w/ expSumLog	3.909671	3.883940	3.929364	3.894366	4.124184	3.451992
6	rowProds(X[cols, rows]) w/ expSumLog	4.195538	4.173818	4.372719	4.142849	4.263682	15.421093
4	rowProds(X, cols, rows) w/ expSumLog	4.189027	4.180308	4.125715	4.187024	4.236758	3.159173

Figure: Benchmarking of colProds_X_S w/ direct(), colProds_X_S w/ expSumLog(), colProds(X, rows, cols) w/ direct(), colProds(X, rows, cols) w/ expSumLog(), colProds(X[rows, cols]) w/ direct() and colProds(X[rows, cols]) w/ expSumLog() on 1000x100 data as well as rowProds_X_S w/ direct(), rowProds_X_S w/ expSumLog(), rowProds(X, cols, rows) w/ direct(), rowProds(X, cols, rows) w/ expSumLog(), rowProds(X[cols, rows]) w/ direct() and rowProds(X[cols, rows]) w/ expSumLog() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colProds_X_S w/ direct() and rowProds_X_S w/ direct() on 1000x100 data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colProds_X_S w/ direct	238.667	247.248	277.6331	255.7235	298.5885	482.010
2	rowProds_X_S w/ direct	374.254	378.641	408.8243	383.5165	406.3745	701.556

	expr	min	lq	mean	median	uq	max
1	colProds_X_S w/ direct	1.000000	1.000000	1.000000	1.000000	1.000000	1.00000
2	rowProds_X_S w/ direct	1.568101	1.531422	1.472534	1.499731	1.360985	1.45548

Figure: Benchmarking of colProds_X_S w/ direct() and rowProds_X_S w/ direct() on 1000x100 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

Appendix

Session information

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

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

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

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

other attached packages:
[1] microbenchmark_1.4-7   matrixStats_0.60.0     ggplot2_3.3.5         
[4] knitr_1.33             R.devices_2.17.0       R.utils_2.10.1        
[7] R.oo_1.24.0            R.methodsS3_1.8.1-9001 history_0.0.1-9000    

loaded via a namespace (and not attached):
 [1] Biobase_2.52.0          httr_1.4.2              splines_4.1.1          
 [4] bit64_4.0.5             network_1.17.1          assertthat_0.2.1       
 [7] highr_0.9               stats4_4.1.1            blob_1.2.2             
[10] GenomeInfoDbData_1.2.6  robustbase_0.93-8       pillar_1.6.2           
[13] RSQLite_2.2.8           lattice_0.20-44         glue_1.4.2             
[16] digest_0.6.27           XVector_0.32.0          colorspace_2.0-2       
[19] Matrix_1.3-4            XML_3.99-0.7            pkgconfig_2.0.3        
[22] zlibbioc_1.38.0         genefilter_1.74.0       purrr_0.3.4            
[25] ergm_4.1.2              xtable_1.8-4            scales_1.1.1           
[28] tibble_3.1.4            annotate_1.70.0         KEGGREST_1.32.0        
[31] farver_2.1.0            generics_0.1.0          IRanges_2.26.0         
[34] ellipsis_0.3.2          cachem_1.0.6            withr_2.4.2            
[37] BiocGenerics_0.38.0     mime_0.11               survival_3.2-13        
[40] magrittr_2.0.1          crayon_1.4.1            statnet.common_4.5.0   
[43] memoise_2.0.0           laeken_0.5.1            fansi_0.5.0            
[46] R.cache_0.15.0          MASS_7.3-54             R.rsp_0.44.0           
[49] progressr_0.8.0         tools_4.1.1             lifecycle_1.0.0        
[52] S4Vectors_0.30.0        trust_0.1-8             munsell_0.5.0          
[55] tabby_0.0.1-9001        AnnotationDbi_1.54.1    Biostrings_2.60.2      
[58] compiler_4.1.1          GenomeInfoDb_1.28.1     rlang_0.4.11           
[61] grid_4.1.1              RCurl_1.98-1.4          cwhmisc_6.6            
[64] rstudioapi_0.13         rappdirs_0.3.3          startup_0.15.0         
[67] labeling_0.4.2          bitops_1.0-7            base64enc_0.1-3        
[70] boot_1.3-28             gtable_0.3.0            DBI_1.1.1              
[73] markdown_1.1            R6_2.5.1                lpSolveAPI_5.5.2.0-17.7
[76] rle_0.9.2               dplyr_1.0.7             fastmap_1.1.0          
[79] bit_4.0.4               utf8_1.2.2              parallel_4.1.1         
[82] Rcpp_1.0.7              vctrs_0.3.8             png_0.1-7              
[85] DEoptimR_1.0-9          tidyselect_1.1.1        xfun_0.25              
[88] coda_0.19-4            

Total processing time was 17.82 secs.

Reproducibility

To reproduce this report, do:

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

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Copyright Dongcan Jiang. Last updated on 2021-08-25 22:24:46 (+0200 UTC). Powered by RSP.