matrixStats.benchmarks

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colProds() and rowProds() benchmarks

This report benchmark the performance of colProds() and rowProds() against alternative methods.

Alternative methods

• colProds()/rowProds() using method=”expSumLog”
• apply() + prod()
• apply() + product()

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"]]
> colStats <- microbenchmark(`colProds w/ direct` = colProds(X, method = "direct", na.rm = FALSE), 
+     `colProds w/ expSumLog` = colProds(X, method = "expSumLog", na.rm = FALSE), `apply+prod` = apply(X, 
+         MARGIN = 2L, FUN = prod, na.rm = FALSE), `apply+product` = apply(X, MARGIN = 2L, FUN = product, 
+         na.rm = FALSE), unit = "ms")
> X <- t(X)
> rowStats <- microbenchmark(`rowProds w/ direct` = rowProds(X, method = "direct", na.rm = FALSE), 
+     `rowProds w/ expSumLog` = rowProds(X, method = "expSumLog", na.rm = FALSE), `apply+prod` = apply(X, 
+         MARGIN = 1L, FUN = prod, na.rm = FALSE), `apply+product` = apply(X, MARGIN = 1L, FUN = product, 
+         na.rm = FALSE), unit = "ms")

Table: Benchmarking of colProds w/ direct(), colProds w/ expSumLog(), apply+prod() and apply+product() 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 w/ direct	0.015464	0.0170365	0.0182301	0.0177725	0.0186310	0.049166
2	colProds w/ expSumLog	0.025361	0.0266820	0.0282407	0.0281735	0.0294785	0.036924
3	apply+prod	0.033480	0.0359415	0.0375526	0.0366855	0.0375965	0.064597
4	apply+product	0.045762	0.0488585	0.0511288	0.0501465	0.0509425	0.148216

	expr	min	lq	mean	median	uq	max
1	colProds w/ direct	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	colProds w/ expSumLog	1.640003	1.566167	1.549127	1.585230	1.582229	0.7510068
3	apply+prod	2.165029	2.109676	2.059926	2.064172	2.017954	1.3138551
4	apply+product	2.959260	2.867872	2.804639	2.821578	2.734287	3.0146036

Table: Benchmarking of rowProds w/ direct(), rowProds w/ expSumLog(), apply+prod() and apply+product() 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 w/ direct	0.015246	0.0167345	0.0177963	0.0177585	0.0188475	0.023598
2	rowProds w/ expSumLog	0.024732	0.0268055	0.0286955	0.0284790	0.0295640	0.057406
3	apply+prod	0.032469	0.0348370	0.0372683	0.0364710	0.0378930	0.103660
4	apply+product	0.043533	0.0466160	0.0486299	0.0484055	0.0505520	0.065340

	expr	min	lq	mean	median	uq	max
1	rowProds w/ direct	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowProds w/ expSumLog	1.622196	1.601811	1.612443	1.603683	1.568590	2.432664
3	apply+prod	2.129673	2.081747	2.094159	2.053721	2.010505	4.392745
4	apply+product	2.855372	2.785623	2.732585	2.725765	2.682159	2.768879

Figure: Benchmarking of colProds w/ direct(), colProds w/ expSumLog(), apply+prod() and apply+product() on 10x10 data as well as rowProds w/ direct(), rowProds w/ expSumLog(), apply+prod() and apply+product() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colProds w/ direct() and rowProds 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 w/ direct	15.246	16.7345	17.79631	17.7585	18.8475	23.598
1	colProds w/ direct	15.464	17.0365	18.23007	17.7725	18.6310	49.166

	expr	min	lq	mean	median	uq	max
2	rowProds w/ direct	1.000000	1.000000	1.000000	1.000000	1.0000000	1.000000
1	colProds w/ direct	1.014299	1.018047	1.024374	1.000788	0.9885131	2.083482

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

100x100 matrix

> X <- data[["100x100"]]
> colStats <- microbenchmark(`colProds w/ direct` = colProds(X, method = "direct", na.rm = FALSE), 
+     `colProds w/ expSumLog` = colProds(X, method = "expSumLog", na.rm = FALSE), `apply+prod` = apply(X, 
+         MARGIN = 2L, FUN = prod, na.rm = FALSE), `apply+product` = apply(X, MARGIN = 2L, FUN = product, 
+         na.rm = FALSE), unit = "ms")
> X <- t(X)
> rowStats <- microbenchmark(`rowProds w/ direct` = rowProds(X, method = "direct", na.rm = FALSE), 
+     `rowProds w/ expSumLog` = rowProds(X, method = "expSumLog", na.rm = FALSE), `apply+prod` = apply(X, 
+         MARGIN = 1L, FUN = prod, na.rm = FALSE), `apply+product` = apply(X, MARGIN = 1L, FUN = product, 
+         na.rm = FALSE), unit = "ms")

Table: Benchmarking of colProds w/ direct(), colProds w/ expSumLog(), apply+prod() and apply+product() 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 w/ direct	0.117317	0.1297035	0.1389370	0.1333210	0.1395285	0.237072
3	apply+prod	0.166813	0.1828500	0.1986652	0.1910825	0.2050910	0.325056
2	colProds w/ expSumLog	0.386637	0.4198260	0.4610244	0.4508850	0.4610140	0.673868
4	apply+product	0.448368	0.5021095	0.5515868	0.5223415	0.5637455	1.064698

	expr	min	lq	mean	median	uq	max
1	colProds w/ direct	1.00000	1.000000	1.000000	1.000000	1.000000	1.000000
3	apply+prod	1.42190	1.409754	1.429894	1.433251	1.469886	1.371128
2	colProds w/ expSumLog	3.29566	3.236813	3.318225	3.381950	3.304085	2.842461
4	apply+product	3.82185	3.871210	3.970048	3.917924	4.040361	4.491032

Table: Benchmarking of rowProds w/ direct(), rowProds w/ expSumLog(), apply+prod() and apply+product() 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 w/ direct	0.143839	0.1627010	0.1789774	0.1673370	0.1870145	0.289425
3	apply+prod	0.164456	0.1826105	0.2021063	0.1936155	0.2103705	0.342941
2	rowProds w/ expSumLog	0.428025	0.4628110	0.5036717	0.4825730	0.5232340	0.791292
4	apply+product	0.449080	0.4948235	0.5339072	0.5152880	0.5423520	0.831811

	expr	min	lq	mean	median	uq	max
1	rowProds w/ direct	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	apply+prod	1.143334	1.122369	1.129228	1.157039	1.124889	1.184905
2	rowProds w/ expSumLog	2.975723	2.844549	2.814163	2.883839	2.797826	2.734014
4	apply+product	3.122102	3.041306	2.983098	3.079343	2.900053	2.874012

Figure: Benchmarking of colProds w/ direct(), colProds w/ expSumLog(), apply+prod() and apply+product() on 100x100 data as well as rowProds w/ direct(), rowProds w/ expSumLog(), apply+prod() and apply+product() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colProds w/ direct() and rowProds 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 w/ direct	117.317	129.7035	138.9370	133.321	139.5285	237.072
2	rowProds w/ direct	143.839	162.7010	178.9774	167.337	187.0145	289.425

	expr	min	lq	mean	median	uq	max
1	colProds w/ direct	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowProds w/ direct	1.226071	1.254407	1.288191	1.255144	1.340332	1.220832

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

1000x10 matrix

> X <- data[["1000x10"]]
> colStats <- microbenchmark(`colProds w/ direct` = colProds(X, method = "direct", na.rm = FALSE), 
+     `colProds w/ expSumLog` = colProds(X, method = "expSumLog", na.rm = FALSE), `apply+prod` = apply(X, 
+         MARGIN = 2L, FUN = prod, na.rm = FALSE), `apply+product` = apply(X, MARGIN = 2L, FUN = product, 
+         na.rm = FALSE), unit = "ms")
> X <- t(X)
> rowStats <- microbenchmark(`rowProds w/ direct` = rowProds(X, method = "direct", na.rm = FALSE), 
+     `rowProds w/ expSumLog` = rowProds(X, method = "expSumLog", na.rm = FALSE), `apply+prod` = apply(X, 
+         MARGIN = 1L, FUN = prod, na.rm = FALSE), `apply+product` = apply(X, MARGIN = 1L, FUN = product, 
+         na.rm = FALSE), unit = "ms")

Table: Benchmarking of colProds w/ direct(), colProds w/ expSumLog(), apply+prod() and apply+product() 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 w/ direct	0.051304	0.0555880	0.0648662	0.0591295	0.0745600	0.093749
3	apply+prod	0.091343	0.0985405	0.1111484	0.1021665	0.1195895	0.172639
2	colProds w/ expSumLog	0.271162	0.2915345	0.3244262	0.3017575	0.3528975	0.468217
4	apply+product	0.312593	0.3361785	0.3770063	0.3539670	0.3983920	0.635558

	expr	min	lq	mean	median	uq	max
1	colProds w/ direct	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	apply+prod	1.780426	1.772694	1.713502	1.727843	1.603936	1.841502
2	colProds w/ expSumLog	5.285397	5.244558	5.001466	5.103332	4.733067	4.994368
4	apply+product	6.092956	6.047681	5.812059	5.986301	5.343240	6.779358

Table: Benchmarking of rowProds w/ direct(), rowProds w/ expSumLog(), apply+prod() and apply+product() 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 w/ direct	0.079256	0.0846850	0.0926506	0.0876200	0.0973465	0.149839
3	apply+prod	0.092173	0.0977360	0.1104220	0.1021925	0.1203055	0.167060
2	rowProds w/ expSumLog	0.305427	0.3295275	0.3678592	0.3405060	0.3975110	0.506773
4	apply+product	0.316353	0.3345365	0.3766125	0.3523175	0.4027160	0.610738

	expr	min	lq	mean	median	uq	max
1	rowProds w/ direct	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	apply+prod	1.162978	1.154112	1.191811	1.166315	1.235848	1.114930
2	rowProds w/ expSumLog	3.853677	3.891214	3.970390	3.886167	4.083465	3.382117
4	apply+product	3.991534	3.950363	4.064867	4.020971	4.136933	4.075962

Figure: Benchmarking of colProds w/ direct(), colProds w/ expSumLog(), apply+prod() and apply+product() on 1000x10 data as well as rowProds w/ direct(), rowProds w/ expSumLog(), apply+prod() and apply+product() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colProds w/ direct() and rowProds 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 w/ direct	51.304	55.588	64.86623	59.1295	74.5600	93.749
2	rowProds w/ direct	79.256	84.685	92.65065	87.6200	97.3465	149.839

	expr	min	lq	mean	median	uq	max
1	colProds w/ direct	1.000000	1.00000	1.000000	1.000000	1.000000	1.0000
2	rowProds w/ direct	1.544831	1.52344	1.428334	1.481832	1.305613	1.5983

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

10x1000 matrix

> X <- data[["10x1000"]]
> colStats <- microbenchmark(`colProds w/ direct` = colProds(X, method = "direct", na.rm = FALSE), 
+     `colProds w/ expSumLog` = colProds(X, method = "expSumLog", na.rm = FALSE), `apply+prod` = apply(X, 
+         MARGIN = 2L, FUN = prod, na.rm = FALSE), `apply+product` = apply(X, MARGIN = 2L, FUN = product, 
+         na.rm = FALSE), unit = "ms")
> X <- t(X)
> rowStats <- microbenchmark(`rowProds w/ direct` = rowProds(X, method = "direct", na.rm = FALSE), 
+     `rowProds w/ expSumLog` = rowProds(X, method = "expSumLog", na.rm = FALSE), `apply+prod` = apply(X, 
+         MARGIN = 1L, FUN = prod, na.rm = FALSE), `apply+product` = apply(X, MARGIN = 1L, FUN = product, 
+         na.rm = FALSE), unit = "ms")

Table: Benchmarking of colProds w/ direct(), colProds w/ expSumLog(), apply+prod() and apply+product() 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 w/ direct	0.799044	0.9231395	0.958287	0.942038	0.971782	1.286089
3	apply+prod	0.960443	1.0770465	1.143599	1.124933	1.171175	1.680799
2	colProds w/ expSumLog	1.563287	1.7493370	1.905964	1.800013	1.841074	7.304836
4	apply+product	1.822864	2.0337965	2.112645	2.081757	2.131324	2.968760

	expr	min	lq	mean	median	uq	max
1	colProds w/ direct	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	apply+prod	1.201990	1.166721	1.193378	1.194148	1.205182	1.306907
2	colProds w/ expSumLog	1.956447	1.894987	1.988928	1.910765	1.894534	5.679884
4	apply+product	2.281306	2.203130	2.204606	2.209843	2.193213	2.308363

Table: Benchmarking of rowProds w/ direct(), rowProds w/ expSumLog(), apply+prod() and apply+product() 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 w/ direct	0.823888	0.937789	0.9609848	0.9533515	0.9761085	1.274040
3	apply+prod	0.936360	1.083555	1.1131097	1.1021725	1.1470555	1.402351
2	rowProds w/ expSumLog	1.518385	1.768428	1.8766842	1.8216165	1.8756260	7.359193
4	apply+product	1.792159	2.034142	2.1317012	2.0607120	2.1115305	7.548568

	expr	min	lq	mean	median	uq	max
1	rowProds w/ direct	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	apply+prod	1.136514	1.155436	1.158301	1.156103	1.175131	1.100712
2	rowProds w/ expSumLog	1.842951	1.885742	1.952876	1.910750	1.921534	5.776265
4	apply+product	2.175246	2.169083	2.218246	2.161545	2.163213	5.924907

Figure: Benchmarking of colProds w/ direct(), colProds w/ expSumLog(), apply+prod() and apply+product() on 10x1000 data as well as rowProds w/ direct(), rowProds w/ expSumLog(), apply+prod() and apply+product() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colProds w/ direct() and rowProds 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 w/ direct	799.044	923.1395	958.2870	942.0380	971.7820	1286.089
2	rowProds w/ direct	823.888	937.7890	960.9848	953.3515	976.1085	1274.040

	expr	min	lq	mean	median	uq	max
1	colProds w/ direct	1.000000	1.000000	1.000000	1.00000	1.000000	1.0000000
2	rowProds w/ direct	1.031092	1.015869	1.002815	1.01201	1.004452	0.9906313

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

100x1000 matrix

> X <- data[["100x1000"]]
> colStats <- microbenchmark(`colProds w/ direct` = colProds(X, method = "direct", na.rm = FALSE), 
+     `colProds w/ expSumLog` = colProds(X, method = "expSumLog", na.rm = FALSE), `apply+prod` = apply(X, 
+         MARGIN = 2L, FUN = prod, na.rm = FALSE), `apply+product` = apply(X, MARGIN = 2L, FUN = product, 
+         na.rm = FALSE), unit = "ms")
> X <- t(X)
> rowStats <- microbenchmark(`rowProds w/ direct` = rowProds(X, method = "direct", na.rm = FALSE), 
+     `rowProds w/ expSumLog` = rowProds(X, method = "expSumLog", na.rm = FALSE), `apply+prod` = apply(X, 
+         MARGIN = 1L, FUN = prod, na.rm = FALSE), `apply+product` = apply(X, MARGIN = 1L, FUN = product, 
+         na.rm = FALSE), unit = "ms")

Table: Benchmarking of colProds w/ direct(), colProds w/ expSumLog(), apply+prod() and apply+product() 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 w/ direct	1.179389	1.314968	1.345066	1.349840	1.366229	1.683755
3	apply+prod	1.575255	1.775967	1.847444	1.816569	1.860508	2.686729
2	colProds w/ expSumLog	4.051115	4.557006	5.084922	4.656977	4.700619	25.746866
4	apply+product	4.427333	5.124107	5.434336	5.213097	5.292663	26.213598

	expr	min	lq	mean	median	uq	max
1	colProds w/ direct	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	apply+prod	1.335654	1.350577	1.373497	1.345767	1.361784	1.595677
2	colProds w/ expSumLog	3.434927	3.465487	3.780426	3.450023	3.440580	15.291337
4	apply+product	3.753921	3.896753	4.040201	3.862013	3.873922	15.568535

Table: Benchmarking of rowProds w/ direct(), rowProds w/ expSumLog(), apply+prod() and apply+product() 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 w/ direct	1.500721	1.640316	1.706389	1.682621	1.714270	2.488731
3	apply+prod	1.582496	1.776851	2.437600	1.833390	1.871156	22.490657
2	rowProds w/ expSumLog	4.158133	4.859330	4.947674	4.949581	5.036792	7.099323
4	apply+product	4.655465	5.131910	5.281496	5.262080	5.310206	6.602964

	expr	min	lq	mean	median	uq	max
1	rowProds w/ direct	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	apply+prod	1.054490	1.083237	1.428514	1.089603	1.091518	9.036998
2	rowProds w/ expSumLog	2.770757	2.962434	2.899500	2.941590	2.938156	2.852587
4	apply+product	3.102152	3.128610	3.095130	3.127311	3.097649	2.653145

Figure: Benchmarking of colProds w/ direct(), colProds w/ expSumLog(), apply+prod() and apply+product() on 100x1000 data as well as rowProds w/ direct(), rowProds w/ expSumLog(), apply+prod() and apply+product() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colProds w/ direct() and rowProds 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 w/ direct	1.179389	1.314968	1.345066	1.349840	1.366229	1.683755
2	rowProds w/ direct	1.500721	1.640316	1.706389	1.682621	1.714270	2.488731

	expr	min	lq	mean	median	uq	max
1	colProds w/ direct	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowProds w/ direct	1.272456	1.247419	1.268629	1.246534	1.254746	1.478084

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

1000x100 matrix

> X <- data[["1000x100"]]
> colStats <- microbenchmark(`colProds w/ direct` = colProds(X, method = "direct", na.rm = FALSE), 
+     `colProds w/ expSumLog` = colProds(X, method = "expSumLog", na.rm = FALSE), `apply+prod` = apply(X, 
+         MARGIN = 2L, FUN = prod, na.rm = FALSE), `apply+product` = apply(X, MARGIN = 2L, FUN = product, 
+         na.rm = FALSE), unit = "ms")
> X <- t(X)
> rowStats <- microbenchmark(`rowProds w/ direct` = rowProds(X, method = "direct", na.rm = FALSE), 
+     `rowProds w/ expSumLog` = rowProds(X, method = "expSumLog", na.rm = FALSE), `apply+prod` = apply(X, 
+         MARGIN = 1L, FUN = prod, na.rm = FALSE), `apply+product` = apply(X, MARGIN = 1L, FUN = product, 
+         na.rm = FALSE), unit = "ms")

Table: Benchmarking of colProds w/ direct(), colProds w/ expSumLog(), apply+prod() and apply+product() 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 w/ direct	0.492126	0.5421640	0.5585672	0.5555440	0.567384	0.681578
3	apply+prod	0.811366	0.8763845	1.1486733	0.8955615	0.922404	9.229041
2	colProds w/ expSumLog	2.734650	3.1977200	3.2522467	3.2312370	3.302136	4.137175
4	apply+product	3.094241	3.5440850	3.6029714	3.5776690	3.663947	4.990393

	expr	min	lq	mean	median	uq	max
1	colProds w/ direct	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	apply+prod	1.648696	1.616456	2.056464	1.612044	1.625714	13.540697
2	colProds w/ expSumLog	5.556809	5.898068	5.822481	5.816348	5.819932	6.069995
4	apply+product	6.287497	6.536924	6.450382	6.439938	6.457615	7.321822

Table: Benchmarking of rowProds w/ direct(), rowProds w/ expSumLog(), apply+prod() and apply+product() 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 w/ direct	0.766797	0.8575020	0.9809245	0.8771355	0.8963170	9.267478
3	apply+prod	0.813991	0.9070525	0.9439808	0.9191840	0.9451315	1.385319
2	rowProds w/ expSumLog	3.031859	3.5412015	3.7012978	3.5755145	3.6667280	11.453874
4	apply+product	3.082218	3.5543650	7.2111981	3.6093075	3.7005655	360.611770

	expr	min	lq	mean	median	uq	max
1	rowProds w/ direct	1.000000	1.000000	1.0000000	1.000000	1.000000	1.0000000
3	apply+prod	1.061547	1.057785	0.9623379	1.047938	1.054461	0.1494818
2	rowProds w/ expSumLog	3.953927	4.129671	3.7732749	4.076354	4.090883	1.2359214
4	apply+product	4.019601	4.145022	7.3514303	4.114880	4.128635	38.9115324

Figure: Benchmarking of colProds w/ direct(), colProds w/ expSumLog(), apply+prod() and apply+product() on 1000x100 data as well as rowProds w/ direct(), rowProds w/ expSumLog(), apply+prod() and apply+product() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colProds w/ direct() and rowProds 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 w/ direct	492.126	542.164	558.5671	555.5440	567.384	681.578
2	rowProds w/ direct	766.797	857.502	980.9245	877.1355	896.317	9267.478

	expr	min	lq	mean	median	uq	max
1	colProds w/ direct	1.000000	1.000000	1.000000	1.000000	1.000000	1.00000
2	rowProds w/ direct	1.558132	1.581628	1.756144	1.578877	1.579736	13.59709

Figure: Benchmarking of colProds w/ direct() and rowProds 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 18.47 secs.

Reproducibility

To reproduce this report, do:

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

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Copyright Henrik Bengtsson. Last updated on 2021-08-25 19:04:39 (+0200 UTC). Powered by RSP.