matrixStats.benchmarks

---

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	0.015791	0.0170935	0.0179873	0.0178220	0.0186235	0.033431
2	colProds w/ expSumLog	0.026177	0.0280410	0.0292996	0.0292300	0.0303530	0.041994
3	apply+prod	0.034605	0.0366820	0.0377462	0.0377885	0.0384030	0.053230
4	apply+product	0.046416	0.0504970	0.0523317	0.0513315	0.0522370	0.139212

	expr	min	lq	mean	median	uq	max
1	colProds w/ direct	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	colProds w/ expSumLog	1.657716	1.640448	1.628905	1.640108	1.629822	1.256139
3	apply+prod	2.191438	2.145962	2.098487	2.120329	2.062072	1.592235
4	apply+product	2.939396	2.954164	2.909363	2.880232	2.804897	4.164159

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	0.015061	0.0173315	0.0186616	0.018441	0.0192500	0.036447
2	rowProds w/ expSumLog	0.026770	0.0285790	0.0306978	0.029877	0.0311205	0.061372
3	apply+prod	0.033669	0.0363210	0.0391764	0.037578	0.0390500	0.141999
4	apply+product	0.045592	0.0490915	0.0518252	0.051090	0.0522790	0.088668

	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	1.777438	1.648963	1.644970	1.620140	1.616649	1.683870
3	apply+prod	2.235509	2.095664	2.099305	2.037742	2.028571	3.896041
4	apply+product	3.027156	2.832501	2.777103	2.770457	2.715792	2.432793

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
1	colProds w/ direct	15.791	17.0935	17.98733	17.822	18.6235	33.431
2	rowProds w/ direct	15.061	17.3315	18.66162	18.441	19.2500	36.447

	expr	min	lq	mean	median	uq	max
1	colProds w/ direct	1.0000000	1.000000	1.000000	1.000000	1.00000	1.000000
2	rowProds w/ direct	0.9537711	1.013923	1.037487	1.034732	1.03364	1.090216

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	0.113837	0.1164835	0.1350380	0.1286595	0.1430325	0.241196
3	apply+prod	0.163253	0.1703875	0.1966820	0.1866755	0.2154080	0.327832
2	colProds w/ expSumLog	0.387009	0.3906525	0.4500226	0.4218350	0.4815725	0.678125
4	apply+product	0.442763	0.4510660	0.5258223	0.4897290	0.5699340	0.897574

	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	1.434094	1.462761	1.456494	1.450927	1.506007	1.359193
2	colProds w/ expSumLog	3.399677	3.353715	3.332564	3.278693	3.366875	2.811510
4	apply+product	3.889447	3.872360	3.893885	3.806396	3.984647	3.721347

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	0.141042	0.143503	0.1743457	0.1587805	0.1932770	0.288520
3	apply+prod	0.164034	0.170025	0.1959542	0.1748620	0.2111945	0.314313
2	rowProds w/ expSumLog	0.415211	0.418217	0.4750252	0.4259075	0.5101235	0.789069
4	apply+product	0.445640	0.452045	0.5095773	0.4617895	0.5543310	0.860640

	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	1.163015	1.184818	1.123940	1.101281	1.092704	1.089398
2	rowProds w/ expSumLog	2.943882	2.914343	2.724616	2.682367	2.639339	2.734885
4	apply+product	3.159626	3.150073	2.922798	2.908352	2.868065	2.982947

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	113.837	116.4835	135.0380	128.6595	143.0325	241.196
2	rowProds w/ direct	141.042	143.5030	174.3457	158.7805	193.2770	288.520

	expr	min	lq	mean	median	uq	max
1	colProds w/ direct	1.000000	1.00000	1.000000	1.000000	1.00000	1.000000
2	rowProds w/ direct	1.238982	1.23196	1.291087	1.234114	1.35128	1.196206

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	0.049070	0.0526690	0.0630100	0.0603840	0.0714750	0.108102
3	apply+prod	0.088524	0.0916415	0.1100393	0.1024025	0.1186560	0.241756
2	colProds w/ expSumLog	0.264126	0.2686695	0.3111351	0.2914875	0.3416465	0.545174
4	apply+product	0.304561	0.3091380	0.3627528	0.3417810	0.3860410	0.786870

	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	1.804035	1.739951	1.746379	1.695855	1.660105	2.236369
2	colProds w/ expSumLog	5.382637	5.101094	4.937868	4.827231	4.779944	5.043144
4	apply+product	6.206664	5.869449	5.757066	5.660125	5.401063	7.278959

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	0.075937	0.0794735	0.0905472	0.0842065	0.0995830	0.172085
3	apply+prod	0.087590	0.0923550	0.1080249	0.0995745	0.1183560	0.231943
2	rowProds w/ expSumLog	0.293066	0.2966660	0.3540579	0.3286040	0.3873645	0.540402
4	apply+product	0.306161	0.3101580	0.3653697	0.3394815	0.3963800	0.597943

	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	1.153456	1.162086	1.193023	1.182504	1.188516	1.347840
2	rowProds w/ expSumLog	3.859331	3.732892	3.910204	3.902359	3.889866	3.140320
4	apply+product	4.031776	3.902659	4.035131	4.031536	3.980398	3.474696

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	49.070	52.6690	63.01001	60.3840	71.475	108.102
2	rowProds w/ direct	75.937	79.4735	90.54718	84.2065	99.583	172.085

	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	1.547524	1.508924	1.437029	1.394517	1.393256	1.591876

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	0.751358	0.7882705	0.9177609	0.8220155	0.8769515	6.225013
3	apply+prod	0.911552	0.9569475	1.0583675	1.0062390	1.0926425	2.096431
2	colProds w/ expSumLog	1.541540	1.5928045	1.7582215	1.6518780	1.7917575	7.133810
4	apply+product	1.758959	1.8146390	1.9636134	1.8816515	2.0484005	2.979021

	expr	min	lq	mean	median	uq	max
1	colProds w/ direct	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
3	apply+prod	1.213206	1.213984	1.153206	1.224112	1.245955	0.3367754
2	colProds w/ expSumLog	2.051672	2.020632	1.915773	2.009546	2.043166	1.1459912
4	apply+product	2.341040	2.302051	2.139570	2.289071	2.335820	0.4785566

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	0.794336	0.8372795	0.9815607	0.9137745	1.014061	2.190136
3	apply+prod	0.921813	1.0025895	1.1289391	1.0809000	1.195852	2.046786
2	rowProds w/ expSumLog	1.574138	1.6650940	1.8879979	1.8134725	2.040895	2.817356
4	apply+product	1.759603	1.9124230	2.4569115	2.1927255	2.439204	7.756790

	expr	min	lq	mean	median	uq	max
1	rowProds w/ direct	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
3	apply+prod	1.160483	1.197437	1.150147	1.182896	1.179270	0.9345474
2	rowProds w/ expSumLog	1.981703	1.988695	1.923465	1.984595	2.012596	1.2863840
4	apply+product	2.215187	2.284092	2.503066	2.399635	2.405381	3.5416933

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	751.358	788.2705	917.7609	822.0155	876.9515	6225.013
2	rowProds w/ direct	794.336	837.2795	981.5607	913.7745	1014.0610	2190.136

	expr	min	lq	mean	median	uq	max
1	colProds w/ direct	1.0000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	rowProds w/ direct	1.0572	1.062173	1.069517	1.111627	1.156348	0.3518283

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	1.106045	1.143137	1.214882	1.175420	1.219490	1.974226
3	apply+prod	1.507445	1.578699	2.110888	1.615665	1.728613	23.450982
2	colProds w/ expSumLog	3.834681	3.943871	4.387280	4.052522	4.140837	25.718991
4	apply+product	4.333414	4.435840	4.775045	4.564342	4.692711	7.107002

	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	1.362915	1.381023	1.737525	1.374544	1.417488	11.878570
2	colProds w/ expSumLog	3.467021	3.450042	3.611281	3.447724	3.395548	13.027379
4	apply+product	3.917936	3.880410	3.930460	3.883160	3.848093	3.599893

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	1.393349	1.469850	1.627374	1.529261	1.694172	2.842822
3	apply+prod	1.510528	1.637811	1.974972	1.681142	1.854321	22.809185
2	rowProds w/ expSumLog	4.141459	4.376250	4.870789	4.442355	4.680246	26.129751
4	apply+product	4.332329	4.588649	5.150038	4.675591	4.992879	26.645973

	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	1.084099	1.114271	1.213594	1.099316	1.094529	8.023431
2	rowProds w/ expSumLog	2.972306	2.977345	2.993035	2.904903	2.762557	9.191483
4	apply+product	3.109292	3.121849	3.164630	3.057418	2.947091	9.373071

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	1.106045	1.143137	1.214882	1.175420	1.219490	1.974226
2	rowProds w/ direct	1.393349	1.469850	1.627374	1.529261	1.694172	2.842822

	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	1.259758	1.285804	1.339533	1.301034	1.389246	1.439968

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	0.450941	0.4666235	0.5062665	0.4916765	0.5188500	0.769587
3	apply+prod	0.736291	0.7661985	0.9059135	0.7910270	0.8444345	8.768338
2	colProds w/ expSumLog	2.618784	2.7103005	2.8940867	2.7765815	2.8978030	4.991079
4	apply+product	2.911127	2.9746260	3.3861137	3.0809740	3.2610560	12.189658

	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	1.632788	1.642006	1.789401	1.608836	1.627512	11.393563
2	colProds w/ expSumLog	5.807376	5.808324	5.716528	5.647171	5.585050	6.485399
4	apply+product	6.455672	6.374788	6.688402	6.266262	6.285161	15.839220

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	0.730858	0.7568740	0.9185949	0.7778645	0.9321885	8.771738
3	apply+prod	0.768788	0.8009725	0.9692730	0.8206430	0.9079340	9.412551
2	rowProds w/ expSumLog	2.896170	3.0285865	3.3758957	3.0956415	3.6680915	5.058126
4	apply+product	2.936074	3.0350740	3.3611604	3.1208320	3.3335610	11.858223

	expr	min	lq	mean	median	uq	max
1	rowProds w/ direct	1.000000	1.000000	1.000000	1.000000	1.0000000	1.000000
3	apply+prod	1.051898	1.058264	1.055169	1.054995	0.9739811	1.073054
2	rowProds w/ expSumLog	3.962699	4.001441	3.675065	3.979667	3.9349246	0.576639
4	apply+product	4.017297	4.010012	3.659024	4.012051	3.5760589	1.351867

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	450.941	466.6235	506.2665	491.6765	518.8500	769.587
2	rowProds w/ direct	730.858	756.8740	918.5949	777.8645	932.1885	8771.738

	expr	min	lq	mean	median	uq	max
1	colProds w/ direct	1.00000	1.000000	1.000000	1.000000	1.000000	1.00000
2	rowProds w/ direct	1.62074	1.622023	1.814449	1.582066	1.796644	11.39798

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_0.60.0     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 18.39 secs.

Reproducibility

To reproduce this report, do:

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

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Copyright Henrik Bengtsson. Last updated on 2021-08-25 22:25:06 (+0200 UTC). Powered by RSP.