colRowAnys_subset

matrixStats: Benchmark report

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colAnys() and rowAnys() benchmarks on subsetted computation

This report benchmark the performance of colAnys() and rowAnys() 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 = "logical")

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]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3074487 164.2    5709258 305.0  5709258 305.0
Vcells 5562356  42.5   22267496 169.9 56666022 432.4
> colStats <- microbenchmark(colAnys_X_S = colAnys(X_S), `colAnys(X, rows, cols)` = colAnys(X, rows = rows, 
+     cols = cols), `colAnys(X[rows, cols])` = colAnys(X[rows, cols]), unit = "ms")
> X <- t(X)
> X_S <- t(X_S)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3073415 164.2    5709258 305.0  5709258 305.0
Vcells 5559586  42.5   22267496 169.9 56666022 432.4
> rowStats <- microbenchmark(rowAnys_X_S = rowAnys(X_S), `rowAnys(X, cols, rows)` = rowAnys(X, rows = cols, 
+     cols = rows), `rowAnys(X[cols, rows])` = rowAnys(X[cols, rows]), unit = "ms")

Table: Benchmarking of colAnys_X_S(), colAnys(X, rows, cols)() and colAnys(X[rows, cols])() 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	colAnys_X_S	0.001356	0.0013930	0.0017866	0.0014200	0.0015050	0.032118
2	colAnys(X, rows, cols)	0.001524	0.0015715	0.0016938	0.0016095	0.0017605	0.004875
3	colAnys(X[rows, cols])	0.002085	0.0022755	0.0025050	0.0023670	0.0024930	0.010202

	expr	min	lq	mean	median	uq	max
1	colAnys_X_S	1.000000	1.000000	1.0000000	1.000000	1.000000	1.0000000
2	colAnys(X, rows, cols)	1.123894	1.128141	0.9480516	1.133451	1.169767	0.1517840
3	colAnys(X[rows, cols])	1.537611	1.633525	1.4021035	1.666901	1.656478	0.3176412

Table: Benchmarking of rowAnys_X_S(), rowAnys(X, cols, rows)() and rowAnys(X[cols, rows])() 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	rowAnys_X_S	0.001364	0.0014030	0.0015140	0.001437	0.0015250	0.003704
2	rowAnys(X, cols, rows)	0.001585	0.0016445	0.0021096	0.001670	0.0018735	0.036419
3	rowAnys(X[cols, rows])	0.001948	0.0021695	0.0025403	0.002287	0.0024125	0.015965

	expr	min	lq	mean	median	uq	max
1	rowAnys_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowAnys(X, cols, rows)	1.162024	1.172131	1.393456	1.162143	1.228525	9.832343
3	rowAnys(X[cols, rows])	1.428152	1.546329	1.677913	1.591510	1.581967	4.310205

Figure: Benchmarking of colAnys_X_S(), colAnys(X, rows, cols)() and colAnys(X[rows, cols])() on 10x10 data as well as rowAnys_X_S(), rowAnys(X, cols, rows)() and rowAnys(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colAnys_X_S() and rowAnys_X_S() 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	colAnys_X_S	1.356	1.393	1.78658	1.420	1.505	32.118
2	rowAnys_X_S	1.364	1.403	1.51397	1.437	1.525	3.704

	expr	min	lq	mean	median	uq	max
1	colAnys_X_S	1.0000	1.000000	1.0000000	1.000000	1.000000	1.0000000
2	rowAnys_X_S	1.0059	1.007179	0.8474124	1.011972	1.013289	0.1153247

Figure: Benchmarking of colAnys_X_S() and rowAnys_X_S() 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]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3071909 164.1    5709258 305.0  5709258 305.0
Vcells 5227681  39.9   22267496 169.9 56666022 432.4
> colStats <- microbenchmark(colAnys_X_S = colAnys(X_S), `colAnys(X, rows, cols)` = colAnys(X, rows = rows, 
+     cols = cols), `colAnys(X[rows, cols])` = colAnys(X[rows, cols]), unit = "ms")
> X <- t(X)
> X_S <- t(X_S)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3071903 164.1    5709258 305.0  5709258 305.0
Vcells 5232764  40.0   22267496 169.9 56666022 432.4
> rowStats <- microbenchmark(rowAnys_X_S = rowAnys(X_S), `rowAnys(X, cols, rows)` = rowAnys(X, rows = cols, 
+     cols = rows), `rowAnys(X[cols, rows])` = rowAnys(X[cols, rows]), unit = "ms")

Table: Benchmarking of colAnys_X_S(), colAnys(X, rows, cols)() and colAnys(X[rows, cols])() 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	colAnys_X_S	0.001899	0.0021305	0.0022950	0.0022515	0.0023810	0.005052
2	colAnys(X, rows, cols)	0.002252	0.0025330	0.0028043	0.0026585	0.0028205	0.010225
3	colAnys(X[rows, cols])	0.017541	0.0177730	0.0184920	0.0179225	0.0181725	0.057294

	expr	min	lq	mean	median	uq	max
1	colAnys_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	colAnys(X, rows, cols)	1.185887	1.188923	1.221883	1.180768	1.184586	2.023951
3	colAnys(X[rows, cols])	9.236967	8.342173	8.057389	7.960249	7.632297	11.340855

Table: Benchmarking of rowAnys_X_S(), rowAnys(X, cols, rows)() and rowAnys(X[cols, rows])() 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	rowAnys_X_S	0.004288	0.0045325	0.0046608	0.004629	0.0047535	0.006961
2	rowAnys(X, cols, rows)	0.004684	0.0048875	0.0050583	0.005001	0.0051535	0.007404
3	rowAnys(X[cols, rows])	0.011358	0.0116880	0.0124336	0.011819	0.0120495	0.049704

	expr	min	lq	mean	median	uq	max
1	rowAnys_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowAnys(X, cols, rows)	1.092351	1.078323	1.085297	1.080363	1.084148	1.063640
3	rowAnys(X[cols, rows])	2.648787	2.578709	2.667732	2.553251	2.534869	7.140353

Figure: Benchmarking of colAnys_X_S(), colAnys(X, rows, cols)() and colAnys(X[rows, cols])() on 100x100 data as well as rowAnys_X_S(), rowAnys(X, cols, rows)() and rowAnys(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colAnys_X_S() and rowAnys_X_S() 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	colAnys_X_S	1.899	2.1305	2.29504	2.2515	2.3810	5.052
2	rowAnys_X_S	4.288	4.5325	4.66075	4.6290	4.7535	6.961

	expr	min	lq	mean	median	uq	max
1	colAnys_X_S	1.00000	1.000000	1.000000	1.000000	1.00000	1.00000
2	rowAnys_X_S	2.25803	2.127435	2.030793	2.055963	1.99643	1.37787

Figure: Benchmarking of colAnys_X_S() and rowAnys_X_S() 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]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3072663 164.1    5709258 305.0  5709258 305.0
Vcells 5231753  40.0   22267496 169.9 56666022 432.4
> colStats <- microbenchmark(colAnys_X_S = colAnys(X_S), `colAnys(X, rows, cols)` = colAnys(X, rows = rows, 
+     cols = cols), `colAnys(X[rows, cols])` = colAnys(X[rows, cols]), unit = "ms")
> X <- t(X)
> X_S <- t(X_S)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3072654 164.1    5709258 305.0  5709258 305.0
Vcells 5236831  40.0   22267496 169.9 56666022 432.4
> rowStats <- microbenchmark(rowAnys_X_S = rowAnys(X_S), `rowAnys(X, cols, rows)` = rowAnys(X, rows = cols, 
+     cols = rows), `rowAnys(X[cols, rows])` = rowAnys(X[cols, rows]), unit = "ms")

Table: Benchmarking of colAnys_X_S(), colAnys(X, rows, cols)() and colAnys(X[rows, cols])() 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	colAnys_X_S	0.001355	0.0014415	0.0015528	0.001504	0.0016045	0.004313
2	colAnys(X, rows, cols)	0.002147	0.0022315	0.0025625	0.002302	0.0023915	0.025150
3	colAnys(X[rows, cols])	0.016088	0.0166010	0.0170227	0.016715	0.0168990	0.032261

	expr	min	lq	mean	median	uq	max
1	colAnys_X_S	1.000000	1.00000	1.000000	1.000000	1.000000	1.000000
2	colAnys(X, rows, cols)	1.584502	1.54804	1.650239	1.530585	1.490495	5.831208
3	colAnys(X[rows, cols])	11.873063	11.51648	10.962353	11.113697	10.532253	7.479944

Table: Benchmarking of rowAnys_X_S(), rowAnys(X, cols, rows)() and rowAnys(X[cols, rows])() 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	rowAnys_X_S	0.004583	0.005022	0.0053577	0.0050665	0.0052030	0.033800
2	rowAnys(X, cols, rows)	0.005105	0.005268	0.0054948	0.0053960	0.0055455	0.010779
3	rowAnys(X[cols, rows])	0.013506	0.013847	0.0145027	0.0140710	0.0142960	0.033346

	expr	min	lq	mean	median	uq	max
1	rowAnys_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	rowAnys(X, cols, rows)	1.113899	1.048984	1.025595	1.065035	1.065827	0.3189053
3	rowAnys(X[cols, rows])	2.946978	2.757268	2.706917	2.777262	2.747646	0.9865680

Figure: Benchmarking of colAnys_X_S(), colAnys(X, rows, cols)() and colAnys(X[rows, cols])() on 1000x10 data as well as rowAnys_X_S(), rowAnys(X, cols, rows)() and rowAnys(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colAnys_X_S() and rowAnys_X_S() 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	colAnys_X_S	1.355	1.4415	1.55283	1.5040	1.6045	4.313
2	rowAnys_X_S	4.583	5.0220	5.35766	5.0665	5.2030	33.800

	expr	min	lq	mean	median	uq	max
1	colAnys_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowAnys_X_S	3.382288	3.483871	3.450255	3.368683	3.242755	7.836773

Figure: Benchmarking of colAnys_X_S() and rowAnys_X_S() 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]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3072866 164.2    5709258 305.0  5709258 305.0
Vcells 5232551  40.0   22267496 169.9 56666022 432.4
> colStats <- microbenchmark(colAnys_X_S = colAnys(X_S), `colAnys(X, rows, cols)` = colAnys(X, rows = rows, 
+     cols = cols), `colAnys(X[rows, cols])` = colAnys(X[rows, cols]), unit = "ms")
> X <- t(X)
> X_S <- t(X_S)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3072860 164.2    5709258 305.0  5709258 305.0
Vcells 5237634  40.0   22267496 169.9 56666022 432.4
> rowStats <- microbenchmark(rowAnys_X_S = rowAnys(X_S), `rowAnys(X, cols, rows)` = rowAnys(X, rows = cols, 
+     cols = rows), `rowAnys(X[cols, rows])` = rowAnys(X[cols, rows]), unit = "ms")

Table: Benchmarking of colAnys_X_S(), colAnys(X, rows, cols)() and colAnys(X[rows, cols])() 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	colAnys_X_S	0.004855	0.0051615	0.0056257	0.005482	0.0058970	0.011418
2	colAnys(X, rows, cols)	0.008077	0.0094130	0.0100815	0.009698	0.0100265	0.043876
3	colAnys(X[rows, cols])	0.013571	0.0140430	0.0146889	0.014408	0.0148745	0.023496

	expr	min	lq	mean	median	uq	max
1	colAnys_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	colAnys(X, rows, cols)	1.663646	1.823695	1.792040	1.769062	1.700271	3.842704
3	colAnys(X[rows, cols])	2.795263	2.720721	2.611033	2.628238	2.522384	2.057803

Table: Benchmarking of rowAnys_X_S(), rowAnys(X, cols, rows)() and rowAnys(X[cols, rows])() 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	rowAnys_X_S	0.009044	0.0098765	0.0149362	0.0102615	0.0108710	0.200069
2	rowAnys(X, cols, rows)	0.009850	0.0114350	0.0141290	0.0120690	0.0128595	0.041426
3	rowAnys(X[cols, rows])	0.016586	0.0173130	0.0233150	0.0178590	0.0183900	0.164487

	expr	min	lq	mean	median	uq	max
1	rowAnys_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	rowAnys(X, cols, rows)	1.089120	1.157799	0.945956	1.176144	1.182918	0.2070586
3	rowAnys(X[cols, rows])	1.833923	1.752949	1.560976	1.740389	1.691657	0.8221514

Figure: Benchmarking of colAnys_X_S(), colAnys(X, rows, cols)() and colAnys(X[rows, cols])() on 10x1000 data as well as rowAnys_X_S(), rowAnys(X, cols, rows)() and rowAnys(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colAnys_X_S() and rowAnys_X_S() 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	colAnys_X_S	4.855	5.1615	5.62569	5.4820	5.897	11.418
2	rowAnys_X_S	9.044	9.8765	14.93616	10.2615	10.871	200.069

	expr	min	lq	mean	median	uq	max
1	colAnys_X_S	1.000000	1.000000	1.000000	1.000000	1.00000	1.00000
2	rowAnys_X_S	1.862822	1.913494	2.654992	1.871853	1.84348	17.52225

Figure: Benchmarking of colAnys_X_S() and rowAnys_X_S() 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]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3073065 164.2    5709258 305.0  5709258 305.0
Vcells 5255196  40.1   22267496 169.9 56666022 432.4
> colStats <- microbenchmark(colAnys_X_S = colAnys(X_S), `colAnys(X, rows, cols)` = colAnys(X, rows = rows, 
+     cols = cols), `colAnys(X[rows, cols])` = colAnys(X[rows, cols]), unit = "ms")
> X <- t(X)
> X_S <- t(X_S)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3073059 164.2    5709258 305.0  5709258 305.0
Vcells 5305279  40.5   22267496 169.9 56666022 432.4
> rowStats <- microbenchmark(rowAnys_X_S = rowAnys(X_S), `rowAnys(X, cols, rows)` = rowAnys(X, rows = cols, 
+     cols = rows), `rowAnys(X[cols, rows])` = rowAnys(X[cols, rows]), unit = "ms")

Table: Benchmarking of colAnys_X_S(), colAnys(X, rows, cols)() and colAnys(X[rows, cols])() 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	colAnys_X_S	0.005351	0.0061920	0.0077397	0.0068515	0.007864	0.025802
2	colAnys(X, rows, cols)	0.009326	0.0120895	0.0143827	0.0131150	0.013843	0.068032
3	colAnys(X[rows, cols])	0.152655	0.1552720	0.1678322	0.1606905	0.166528	0.354265

	expr	min	lq	mean	median	uq	max
1	colAnys_X_S	1.000000	1.000000	1.000000	1.000000	1.00000	1.000000
2	colAnys(X, rows, cols)	1.742852	1.952439	1.858294	1.914179	1.76030	2.636695
3	colAnys(X[rows, cols])	28.528312	25.076227	21.684563	23.453331	21.17599	13.730137

Table: Benchmarking of rowAnys_X_S(), rowAnys(X, cols, rows)() and rowAnys(X[cols, rows])() 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	rowAnys_X_S	0.042043	0.0438890	0.0454123	0.0446005	0.045449	0.084432
2	rowAnys(X, cols, rows)	0.042650	0.0442855	0.0455195	0.0453670	0.046423	0.056537
3	rowAnys(X[cols, rows])	0.123326	0.1266025	0.1302286	0.1282850	0.129231	0.195170

	expr	min	lq	mean	median	uq	max
1	rowAnys_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	rowAnys(X, cols, rows)	1.014438	1.009034	1.002360	1.017186	1.021431	0.6696158
3	rowAnys(X[cols, rows])	2.933330	2.884607	2.867693	2.876313	2.843429	2.3115643

Figure: Benchmarking of colAnys_X_S(), colAnys(X, rows, cols)() and colAnys(X[rows, cols])() on 100x1000 data as well as rowAnys_X_S(), rowAnys(X, cols, rows)() and rowAnys(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colAnys_X_S() and rowAnys_X_S() 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	colAnys_X_S	5.351	6.192	7.73971	6.8515	7.864	25.802
2	rowAnys_X_S	42.043	43.889	45.41231	44.6005	45.449	84.432

	expr	min	lq	mean	median	uq	max
1	colAnys_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowAnys_X_S	7.857036	7.088017	5.867443	6.509596	5.779374	3.272305

Figure: Benchmarking of colAnys_X_S() and rowAnys_X_S() 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]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3073281 164.2    5709258 305.0  5709258 305.0
Vcells 5255863  40.1   22267496 169.9 56666022 432.4
> colStats <- microbenchmark(colAnys_X_S = colAnys(X_S), `colAnys(X, rows, cols)` = colAnys(X, rows = rows, 
+     cols = cols), `colAnys(X[rows, cols])` = colAnys(X[rows, cols]), unit = "ms")
> X <- t(X)
> X_S <- t(X_S)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3073275 164.2    5709258 305.0  5709258 305.0
Vcells 5305946  40.5   22267496 169.9 56666022 432.4
> rowStats <- microbenchmark(rowAnys_X_S = rowAnys(X_S), `rowAnys(X, cols, rows)` = rowAnys(X, rows = cols, 
+     cols = rows), `rowAnys(X[cols, rows])` = rowAnys(X[cols, rows]), unit = "ms")

Table: Benchmarking of colAnys_X_S(), colAnys(X, rows, cols)() and colAnys(X[rows, cols])() 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	colAnys_X_S	0.002617	0.0028565	0.0031919	0.0030350	0.0032470	0.010359
2	colAnys(X, rows, cols)	0.003639	0.0039855	0.0044078	0.0042635	0.0044615	0.011315
3	colAnys(X[rows, cols])	0.081950	0.0824815	0.0853578	0.0828950	0.0845325	0.184085

	expr	min	lq	mean	median	uq	max
1	colAnys_X_S	1.000000	1.000000	1.00000	1.000000	1.000000	1.000000
2	colAnys(X, rows, cols)	1.390524	1.395239	1.38093	1.404778	1.374038	1.092287
3	colAnys(X[rows, cols])	31.314482	28.875022	26.74199	27.313015	26.034031	17.770538

Table: Benchmarking of rowAnys_X_S(), rowAnys(X, cols, rows)() and rowAnys(X[cols, rows])() 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	rowAnys_X_S	0.028460	0.0287755	0.0294075	0.0289865	0.0293925	0.043539
2	rowAnys(X, cols, rows)	0.028699	0.0290525	0.0294747	0.0293630	0.0297725	0.031693
3	rowAnys(X[cols, rows])	0.093992	0.0946480	0.0976003	0.0953205	0.0974690	0.195697

	expr	min	lq	mean	median	uq	max
1	rowAnys_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	rowAnys(X, cols, rows)	1.008398	1.009626	1.002285	1.012989	1.012928	0.7279221
3	rowAnys(X[cols, rows])	3.302600	3.289187	3.318894	3.288445	3.316118	4.4947518

Figure: Benchmarking of colAnys_X_S(), colAnys(X, rows, cols)() and colAnys(X[rows, cols])() on 1000x100 data as well as rowAnys_X_S(), rowAnys(X, cols, rows)() and rowAnys(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colAnys_X_S() and rowAnys_X_S() 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	colAnys_X_S	2.617	2.8565	3.19190	3.0350	3.2470	10.359
2	rowAnys_X_S	28.460	28.7755	29.40748	28.9865	29.3925	43.539

	expr	min	lq	mean	median	uq	max
1	colAnys_X_S	1.00000	1.00000	1.000000	1.000000	1.000000	1.000000
2	rowAnys_X_S	10.87505	10.07369	9.213158	9.550741	9.052202	4.203012

Figure: Benchmarking of colAnys_X_S() and rowAnys_X_S() on 1000x100 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

Appendix

Session information

R version 3.6.1 Patched (2019-08-27 r77078)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.3 LTS

Matrix products: default
BLAS:   /home/hb/software/R-devel/R-3-6-branch/lib/R/lib/libRblas.so
LAPACK: /home/hb/software/R-devel/R-3-6-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_1.4-6    matrixStats_0.55.0-9000 ggplot2_3.2.1          
[4] knitr_1.24              R.devices_2.16.0        R.utils_2.9.0          
[7] R.oo_1.22.0             R.methodsS3_1.7.1       history_0.0.0-9002     

loaded via a namespace (and not attached):
 [1] Biobase_2.45.0       bit64_0.9-7          splines_3.6.1       
 [4] network_1.15         assertthat_0.2.1     highr_0.8           
 [7] stats4_3.6.1         blob_1.2.0           robustbase_0.93-5   
[10] pillar_1.4.2         RSQLite_2.1.2        backports_1.1.4     
[13] lattice_0.20-38      glue_1.3.1           digest_0.6.20       
[16] colorspace_1.4-1     sandwich_2.5-1       Matrix_1.2-17       
[19] XML_3.98-1.20        lpSolve_5.6.13.3     pkgconfig_2.0.2     
[22] genefilter_1.66.0    purrr_0.3.2          ergm_3.10.4         
[25] xtable_1.8-4         mvtnorm_1.0-11       scales_1.0.0        
[28] tibble_2.1.3         annotate_1.62.0      IRanges_2.18.2      
[31] TH.data_1.0-10       withr_2.1.2          BiocGenerics_0.30.0 
[34] lazyeval_0.2.2       mime_0.7             survival_2.44-1.1   
[37] magrittr_1.5         crayon_1.3.4         statnet.common_4.3.0
[40] memoise_1.1.0        laeken_0.5.0         R.cache_0.13.0      
[43] MASS_7.3-51.4        R.rsp_0.43.1         tools_3.6.1         
[46] multcomp_1.4-10      S4Vectors_0.22.1     trust_0.1-7         
[49] munsell_0.5.0        AnnotationDbi_1.46.1 compiler_3.6.1      
[52] rlang_0.4.0          grid_3.6.1           RCurl_1.95-4.12     
[55] cwhmisc_6.6          rappdirs_0.3.1       labeling_0.3        
[58] bitops_1.0-6         base64enc_0.1-3      boot_1.3-23         
[61] gtable_0.3.0         codetools_0.2-16     DBI_1.0.0           
[64] markdown_1.1         R6_2.4.0             zoo_1.8-6           
[67] dplyr_0.8.3          bit_1.1-14           zeallot_0.1.0       
[70] parallel_3.6.1       Rcpp_1.0.2           vctrs_0.2.0         
[73] DEoptimR_1.0-8       tidyselect_0.2.5     xfun_0.9            
[76] coda_0.19-3         

Total processing time was 11.83 secs.

Reproducibility

To reproduce this report, do:

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

---

Copyright Dongcan Jiang. Last updated on 2019-09-10 20:35:59 (-0700 UTC). Powered by RSP.

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