matrixStats.benchmarks

---

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	0.012254	0.0131940	0.0137512	0.0136505	0.0141040	0.028068
3	colProds(X, rows, cols) w/ direct	0.013598	0.0144145	0.0150910	0.0148880	0.0153830	0.023777
5	colProds(X[rows, cols]) w/ direct	0.013447	0.0143615	0.0172449	0.0149640	0.0153810	0.244130
2	colProds_X_S w/ expSumLog	0.018376	0.0195765	0.0201663	0.0202670	0.0206985	0.025475
4	colProds(X, rows, cols) w/ expSumLog	0.019651	0.0210560	0.0218898	0.0216270	0.0220575	0.037553
6	colProds(X[rows, cols]) w/ expSumLog	0.019694	0.0206145	0.0219075	0.0216335	0.0220385	0.073779

	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	1.109679	1.092504	1.097426	1.090656	1.090683	0.8471213
5	colProds(X[rows, cols]) w/ direct	1.097356	1.088487	1.254066	1.096224	1.090542	8.6978053
2	colProds_X_S w/ expSumLog	1.499592	1.483743	1.466509	1.484708	1.467562	0.9076172
4	colProds(X, rows, cols) w/ expSumLog	1.603640	1.595877	1.591843	1.584338	1.563918	1.3379293
6	colProds(X[rows, cols]) w/ expSumLog	1.607149	1.562415	1.593128	1.584814	1.562571	2.6285806

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	0.011913	0.0129585	0.0134311	0.0134660	0.0138490	0.016012
5	rowProds(X[cols, rows]) w/ direct	0.012962	0.0139420	0.0149132	0.0145025	0.0151025	0.030446
3	rowProds(X, cols, rows) w/ direct	0.013233	0.0141825	0.0147567	0.0149220	0.0152895	0.018000
2	rowProds_X_S w/ expSumLog	0.017616	0.0191310	0.0197504	0.0198535	0.0203420	0.026931
6	rowProds(X[cols, rows]) w/ expSumLog	0.018928	0.0203500	0.0209340	0.0210600	0.0216765	0.022522
4	rowProds(X, cols, rows) w/ expSumLog	0.018844	0.0203900	0.0237887	0.0213275	0.0218280	0.261822

	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	1.088055	1.075896	1.110345	1.076972	1.090512	1.901449
3	rowProds(X, cols, rows) w/ direct	1.110803	1.094455	1.098699	1.108124	1.104015	1.124157
2	rowProds_X_S w/ expSumLog	1.478721	1.476328	1.470496	1.474343	1.468843	1.681926
6	rowProds(X[cols, rows]) w/ expSumLog	1.588853	1.570398	1.558620	1.563939	1.565203	1.406570
4	rowProds(X, cols, rows) w/ expSumLog	1.581801	1.573485	1.771162	1.583804	1.576143	16.351611

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
2	rowProds_X_S w/ direct	11.913	12.9585	13.43111	13.4660	13.849	16.012
1	colProds_X_S w/ direct	12.254	13.1940	13.75122	13.6505	14.104	28.068

	expr	min	lq	mean	median	uq	max
2	rowProds_X_S w/ direct	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	colProds_X_S w/ direct	1.028624	1.018173	1.023834	1.013701	1.018413	1.752935

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	0.075356	0.084892	0.0912111	0.0873035	0.0955310	0.170171
5	colProds(X[rows, cols]) w/ direct	0.085624	0.094628	0.1028437	0.0997975	0.1070510	0.178916
3	colProds(X, rows, cols) w/ direct	0.085629	0.094897	0.1033043	0.1000820	0.1070860	0.154112
2	colProds_X_S w/ expSumLog	0.218350	0.242647	0.2648198	0.2560175	0.2707250	0.408534
4	colProds(X, rows, cols) w/ expSumLog	0.229622	0.249282	0.2704413	0.2660840	0.2758485	0.393942
6	colProds(X[rows, cols]) w/ expSumLog	0.228696	0.249759	0.2738163	0.2682205	0.2874275	0.388222

	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
5	colProds(X[rows, cols]) w/ direct	1.136260	1.114687	1.127535	1.143110	1.120589	1.0513895
3	colProds(X, rows, cols) w/ direct	1.136326	1.117856	1.132586	1.146369	1.120955	0.9056302
2	colProds_X_S w/ expSumLog	2.897579	2.858302	2.903374	2.932500	2.833897	2.4007263
4	colProds(X, rows, cols) w/ expSumLog	3.047163	2.936460	2.965006	3.047804	2.887529	2.3149773
6	colProds(X[rows, cols]) w/ expSumLog	3.034874	2.942079	3.002008	3.072277	3.008735	2.2813640

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	0.088727	0.0973980	0.1051866	0.1027635	0.1091840	0.157889
3	rowProds(X, cols, rows) w/ direct	0.099244	0.1074520	0.1146477	0.1123365	0.1168475	0.197033
5	rowProds(X[cols, rows]) w/ direct	0.100435	0.1103505	0.1180430	0.1158650	0.1208215	0.172637
2	rowProds_X_S w/ expSumLog	0.233665	0.2601110	0.2832733	0.2727490	0.2879940	0.441902
4	rowProds(X, cols, rows) w/ expSumLog	0.242296	0.2695425	0.2881091	0.2842380	0.2969920	0.408022
6	rowProds(X[cols, rows]) w/ expSumLog	0.243808	0.2691680	0.2878549	0.2845320	0.2935565	0.443428

	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	1.118532	1.103226	1.089946	1.093156	1.070189	1.247921
5	rowProds(X[cols, rows]) w/ direct	1.131955	1.132985	1.122225	1.127492	1.106586	1.093407
2	rowProds_X_S w/ expSumLog	2.633528	2.670599	2.693056	2.654143	2.637694	2.798814
4	rowProds(X, cols, rows) w/ expSumLog	2.730803	2.767434	2.739030	2.765943	2.720105	2.584233
6	rowProds(X[cols, rows]) w/ expSumLog	2.747844	2.763589	2.736613	2.768804	2.688640	2.808479

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	75.356	84.892	91.21106	87.3035	95.531	170.171
2	rowProds_X_S w/ direct	88.727	97.398	105.18656	102.7635	109.184	157.889

	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	1.177438	1.147317	1.153222	1.177083	1.142917	0.9278255

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	0.028526	0.0308240	0.0360477	0.0355155	0.0382975	0.052117
5	colProds(X[rows, cols]) w/ direct	0.039119	0.0415805	0.0480739	0.0453655	0.0524895	0.075894
3	colProds(X, rows, cols) w/ direct	0.039761	0.0426775	0.0499539	0.0499970	0.0526570	0.080116
2	colProds_X_S w/ expSumLog	0.138421	0.1477160	0.1655872	0.1563490	0.1759780	0.234236
6	colProds(X[rows, cols]) w/ expSumLog	0.149783	0.1591435	0.1806083	0.1698540	0.1959150	0.302452
4	colProds(X, rows, cols) w/ expSumLog	0.150513	0.1602845	0.1831992	0.1822420	0.1964585	0.246771

	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	1.371345	1.348965	1.333621	1.277344	1.370572	1.456223
3	colProds(X, rows, cols) w/ direct	1.393851	1.384554	1.385772	1.407752	1.374946	1.537233
2	colProds_X_S w/ expSumLog	4.852450	4.792240	4.593559	4.402275	4.595026	4.494426
6	colProds(X[rows, cols]) w/ expSumLog	5.250754	5.162974	5.010261	4.782532	5.115608	5.803327
4	colProds(X, rows, cols) w/ expSumLog	5.276344	5.199990	5.082135	5.131337	5.129800	4.734942

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	0.041530	0.0440780	0.0501652	0.0464435	0.0537505	0.070249
5	rowProds(X[cols, rows]) w/ direct	0.053872	0.0578740	0.0650592	0.0627805	0.0709960	0.090010
3	rowProds(X, cols, rows) w/ direct	0.053912	0.0589770	0.0660756	0.0647760	0.0695765	0.094357
2	rowProds_X_S w/ expSumLog	0.153034	0.1656715	0.1876841	0.1822315	0.1993470	0.290609
6	rowProds(X[cols, rows]) w/ expSumLog	0.164987	0.1755245	0.1970204	0.1838890	0.2130140	0.296501
4	rowProds(X, cols, rows) w/ expSumLog	0.165902	0.1765130	0.2007746	0.1866730	0.2179445	0.275991

	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	1.297183	1.312991	1.296898	1.351761	1.320844	1.281299
3	rowProds(X, cols, rows) w/ direct	1.298146	1.338014	1.317158	1.394727	1.294434	1.343179
2	rowProds_X_S w/ expSumLog	3.684903	3.758598	3.741317	3.923725	3.708747	4.136842
6	rowProds(X[cols, rows]) w/ expSumLog	3.972719	3.982134	3.927428	3.959413	3.963014	4.220715
4	rowProds(X, cols, rows) w/ expSumLog	3.994751	4.004560	4.002266	4.019357	4.054744	3.928753

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	28.526	30.824	36.04769	35.5155	38.2975	52.117
2	rowProds_X_S w/ direct	41.530	44.078	50.16525	46.4435	53.7505	70.249

	expr	min	lq	mean	median	uq	max
1	colProds_X_S w/ direct	1.000000	1.00000	1.000000	1.000000	1.000000	1.00000
2	rowProds_X_S w/ direct	1.455865	1.42999	1.391636	1.307697	1.403499	1.34791

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	0.525756	0.6323360	0.6559065	0.6511925	0.6745010	0.896585
5	colProds(X[rows, cols]) w/ direct	0.557986	0.6459375	0.7152804	0.6643885	0.6815345	5.973052
3	colProds(X, rows, cols) w/ direct	0.556517	0.6539165	0.6759455	0.6697385	0.6930740	0.898400
2	colProds_X_S w/ expSumLog	1.026312	1.1686805	1.2133229	1.1991765	1.2420445	1.600904
6	colProds(X[rows, cols]) w/ expSumLog	1.044535	1.1885215	1.2693525	1.2119165	1.2469120	6.639532
4	colProds(X, rows, cols) w/ expSumLog	0.974945	1.1750930	1.2145190	1.2161365	1.2562875	1.622386

	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	1.061302	1.021510	1.090522	1.020264	1.010428	6.662003
3	colProds(X, rows, cols) w/ direct	1.058508	1.034128	1.030552	1.028480	1.027536	1.002024
2	colProds_X_S w/ expSumLog	1.952069	1.848195	1.849841	1.841508	1.841427	1.785557
6	colProds(X[rows, cols]) w/ expSumLog	1.986730	1.879573	1.935264	1.861073	1.848644	7.405357
4	colProds(X, rows, cols) w/ expSumLog	1.854368	1.858336	1.851665	1.867553	1.862544	1.809517

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	0.557322	0.6473255	0.7267380	0.6648705	0.6913685	6.163101
5	rowProds(X[cols, rows]) w/ direct	0.555565	0.6551515	0.6781582	0.6702035	0.6895050	0.873777
3	rowProds(X, cols, rows) w/ direct	0.541358	0.6549870	0.7365054	0.6732120	0.6952990	6.095395
2	rowProds_X_S w/ expSumLog	1.008335	1.1730865	1.2041849	1.1965245	1.2347695	1.582484
4	rowProds(X, cols, rows) w/ expSumLog	1.039314	1.1907545	1.2438429	1.2149530	1.2547920	2.188572
6	rowProds(X[cols, rows]) w/ expSumLog	1.018895	1.2001285	1.2292273	1.2224570	1.2574410	1.558981

	expr	min	lq	mean	median	uq	max
1	rowProds_X_S w/ direct	1.0000000	1.000000	1.0000000	1.000000	1.0000000	1.0000000
5	rowProds(X[cols, rows]) w/ direct	0.9968474	1.012090	0.9331536	1.008021	0.9973046	0.1417755
3	rowProds(X, cols, rows) w/ direct	0.9713559	1.011836	1.0134400	1.012546	1.0056851	0.9890143
2	rowProds_X_S w/ expSumLog	1.8092503	1.812205	1.6569725	1.799635	1.7859788	0.2567675
4	rowProds(X, cols, rows) w/ expSumLog	1.8648358	1.839499	1.7115424	1.827353	1.8149395	0.3551089
6	rowProds(X[cols, rows]) w/ expSumLog	1.8281981	1.853980	1.6914311	1.838639	1.8187710	0.2529540

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	525.756	632.3360	655.9065	651.1925	674.5010	896.585
2	rowProds_X_S w/ direct	557.322	647.3255	726.7380	664.8705	691.3685	6163.101

	expr	min	lq	mean	median	uq	max
1	colProds_X_S w/ direct	1.000000	1.000000	1.00000	1.000000	1.000000	1.000000
2	rowProds_X_S w/ direct	1.060039	1.023705	1.10799	1.021005	1.025007	6.873973

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	0.774959	0.844722	0.8739614	0.8539230	0.8751675	1.623594
3	colProds(X, rows, cols) w/ direct	0.813472	0.964379	0.9896130	0.9757085	0.9969175	1.469689
5	colProds(X[rows, cols]) w/ direct	0.834291	0.967196	0.9962637	0.9782095	1.0019710	1.786837
2	colProds_X_S w/ expSumLog	2.246616	2.570511	2.6092187	2.5963635	2.6609010	3.233377
6	colProds(X[rows, cols]) w/ expSumLog	2.285602	2.697354	2.9305741	2.7201430	2.7646495	24.938368
4	colProds(X, rows, cols) w/ expSumLog	2.438107	2.709574	3.1867407	2.7205825	2.7718770	24.639916

	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	1.049697	1.141652	1.132330	1.142619	1.139116	0.9052072
5	colProds(X[rows, cols]) w/ direct	1.076561	1.144987	1.139940	1.145548	1.144891	1.1005442
2	colProds_X_S w/ expSumLog	2.899013	3.043026	2.985508	3.040512	3.040448	1.9914936
6	colProds(X[rows, cols]) w/ expSumLog	2.949320	3.193185	3.353208	3.185466	3.158995	15.3599779
4	colProds(X, rows, cols) w/ expSumLog	3.146111	3.207652	3.646317	3.185981	3.167253	15.1761561

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	0.913361	1.016366	1.059492	1.042142	1.086821	1.563300
5	rowProds(X[cols, rows]) w/ direct	1.042259	1.194009	1.227772	1.218082	1.237332	1.593861
3	rowProds(X, cols, rows) w/ direct	1.048832	1.193943	1.661884	1.219029	1.239256	23.048014
2	rowProds_X_S w/ expSumLog	2.401771	2.740555	2.997389	2.781973	2.824940	23.381652
4	rowProds(X, cols, rows) w/ expSumLog	2.609489	2.890916	2.954931	2.960818	2.985603	4.448444
6	rowProds(X[cols, rows]) w/ expSumLog	2.579001	2.893291	2.973974	2.972091	3.000861	4.290152

	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	1.141125	1.174784	1.158831	1.168825	1.138488	1.019549
3	rowProds(X, cols, rows) w/ direct	1.148321	1.174718	1.568566	1.169733	1.140258	14.743180
2	rowProds_X_S w/ expSumLog	2.629597	2.696426	2.829081	2.669475	2.599269	14.956599
4	rowProds(X, cols, rows) w/ expSumLog	2.857018	2.844367	2.789007	2.841087	2.747097	2.845547
6	rowProds(X[cols, rows]) w/ expSumLog	2.823638	2.846703	2.806980	2.851905	2.761136	2.744292

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	774.959	844.722	873.9614	853.923	875.1675	1623.594
2	rowProds_X_S w/ direct	913.361	1016.365	1059.4923	1042.142	1086.8210	1563.300

	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	1.178593	1.203195	1.212287	1.220417	1.241843	0.9628639

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	0.247535	0.2864045	0.3006736	0.2983275	0.3055005	0.429981
3	colProds(X, rows, cols) w/ direct	0.342335	0.4017270	0.5006887	0.4139185	0.4224000	8.813239
5	colProds(X[rows, cols]) w/ direct	0.342518	0.4034985	0.4177472	0.4147195	0.4212925	0.533589
2	colProds_X_S w/ expSumLog	1.396613	1.6018535	1.6379117	1.6374335	1.6580245	2.268643
6	colProds(X[rows, cols]) w/ expSumLog	1.448869	1.7157955	1.7562088	1.7664835	1.7812070	2.511134
4	colProds(X, rows, cols) w/ expSumLog	1.476296	1.7162295	1.8439057	1.7686895	1.7886480	10.177295

	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
3	colProds(X, rows, cols) w/ direct	1.382976	1.402656	1.665224	1.387463	1.382649	20.496810
5	colProds(X[rows, cols]) w/ direct	1.383715	1.408841	1.389371	1.390148	1.379024	1.240959
2	colProds_X_S w/ expSumLog	5.642083	5.592976	5.447475	5.488711	5.427240	5.276147
6	colProds(X[rows, cols]) w/ expSumLog	5.853188	5.990812	5.840915	5.921290	5.830455	5.840105
4	colProds(X, rows, cols) w/ expSumLog	5.963989	5.992327	6.132583	5.928684	5.854812	23.669174

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	0.390746	0.444243	0.4581356	0.4504080	0.4613310	0.694193
5	rowProds(X[cols, rows]) w/ direct	0.518946	0.567984	0.5842130	0.5797555	0.5949395	0.768634
3	rowProds(X, cols, rows) w/ direct	0.511310	0.574356	0.5860074	0.5828950	0.5970945	0.780831
2	rowProds_X_S w/ expSumLog	1.520620	1.774465	1.8033133	1.7854080	1.8323560	2.430712
6	rowProds(X[cols, rows]) w/ expSumLog	1.670471	1.900571	2.0210632	1.9154820	1.9619530	10.756083
4	rowProds(X, cols, rows) w/ expSumLog	1.616773	1.897766	2.0081928	1.9326720	1.9656200	10.708158

	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	1.328090	1.278544	1.275197	1.287179	1.289615	1.107234
3	rowProds(X, cols, rows) w/ direct	1.308548	1.292887	1.279114	1.294149	1.294286	1.124804
2	rowProds_X_S w/ expSumLog	3.891582	3.994357	3.936200	3.963979	3.971890	3.501493
6	rowProds(X[cols, rows]) w/ expSumLog	4.275081	4.278224	4.411496	4.252771	4.252810	15.494370
4	rowProds(X, cols, rows) w/ expSumLog	4.137657	4.271910	4.383403	4.290936	4.260758	15.425333

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	247.535	286.4045	300.6736	298.3275	305.5005	429.981
2	rowProds_X_S w/ direct	390.746	444.2430	458.1356	450.4080	461.3310	694.193

	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	1.578548	1.551103	1.523698	1.509777	1.510083	1.614474

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_0.60.1     ggplot2_3.3.5         
[4] knitr_1.33             R.devices_2.17.0       R.utils_2.10.1        
[7] R.oo_1.24.0            R.methodsS3_1.8.1-9001 history_0.0.1-9000    

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

Total processing time was 17.37 secs.

Reproducibility

To reproduce this report, do:

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

---

Copyright Dongcan Jiang. Last updated on 2021-08-25 19:04:19 (+0200 UTC). Powered by RSP.