matrixStats.benchmarks

---

colAlls() and rowAlls() benchmarks on subsetted computation

This report benchmark the performance of colAlls() and rowAlls() 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 5172271 276.3    7916910 422.9  7916910 422.9
Vcells 9253005  70.6   33191153 253.3 53339345 407.0
> colStats <- microbenchmark(colAlls_X_S = colAlls(X_S), `colAlls(X, rows, cols)` = colAlls(X, rows = rows, 
+     cols = cols), `colAlls(X[rows, cols])` = colAlls(X[rows, cols]), unit = "ms")
> X <- t(X)
> X_S <- t(X_S)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5160349 275.6    7916910 422.9  7916910 422.9
Vcells 9213104  70.3   33191153 253.3 53339345 407.0
> rowStats <- microbenchmark(rowAlls_X_S = rowAlls(X_S), `rowAlls(X, cols, rows)` = rowAlls(X, rows = cols, 
+     cols = rows), `rowAlls(X[cols, rows])` = rowAlls(X[cols, rows]), unit = "ms")

Table: Benchmarking of colAlls_X_S(), colAlls(X, rows, cols)() and colAlls(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	colAlls_X_S	0.002956	0.0030380	0.0060737	0.0031095	0.0032130	0.293207
2	colAlls(X, rows, cols)	0.003204	0.0034110	0.0036873	0.0034655	0.0035900	0.018075
3	colAlls(X[rows, cols])	0.003909	0.0041325	0.0043477	0.0042550	0.0044005	0.009081

	expr	min	lq	mean	median	uq	max
1	colAlls_X_S	1.000000	1.000000	1.0000000	1.000000	1.000000	1.0000000
2	colAlls(X, rows, cols)	1.083897	1.122778	0.6070975	1.114488	1.117336	0.0616459
3	colAlls(X[rows, cols])	1.322395	1.360270	0.7158150	1.368387	1.369592	0.0309713

Table: Benchmarking of rowAlls_X_S(), rowAlls(X, cols, rows)() and rowAlls(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	rowAlls_X_S	0.002998	0.0031620	0.0032910	0.0032705	0.0033315	0.005903
2	rowAlls(X, cols, rows)	0.003362	0.0035325	0.0070570	0.0036250	0.0037780	0.327844
3	rowAlls(X[cols, rows])	0.004040	0.0042070	0.0044239	0.0043525	0.0044915	0.007800

	expr	min	lq	mean	median	uq	max
1	rowAlls_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowAlls(X, cols, rows)	1.121414	1.117173	2.144329	1.108393	1.134024	55.538540
3	rowAlls(X[cols, rows])	1.347565	1.330487	1.344225	1.330836	1.348191	1.321362

Figure: Benchmarking of colAlls_X_S(), colAlls(X, rows, cols)() and colAlls(X[rows, cols])() on 10x10 data as well as rowAlls_X_S(), rowAlls(X, cols, rows)() and rowAlls(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colAlls_X_S() and rowAlls_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	colAlls_X_S	2.956	3.038	6.07372	3.1095	3.2130	293.207
2	rowAlls_X_S	2.998	3.162	3.29102	3.2705	3.3315	5.903

	expr	min	lq	mean	median	uq	max
1	colAlls_X_S	1.000000	1.000000	1.0000000	1.000000	1.000000	1.0000000
2	rowAlls_X_S	1.014208	1.040816	0.5418459	1.051777	1.036881	0.0201325

Figure: Benchmarking of colAlls_X_S() and rowAlls_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 5160069 275.6    7916910 422.9  7916910 422.9
Vcells 9177921  70.1   33191153 253.3 53339345 407.0
> colStats <- microbenchmark(colAlls_X_S = colAlls(X_S), `colAlls(X, rows, cols)` = colAlls(X, rows = rows, 
+     cols = cols), `colAlls(X[rows, cols])` = colAlls(X[rows, cols]), unit = "ms")
> X <- t(X)
> X_S <- t(X_S)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5160063 275.6    7916910 422.9  7916910 422.9
Vcells 9183004  70.1   33191153 253.3 53339345 407.0
> rowStats <- microbenchmark(rowAlls_X_S = rowAlls(X_S), `rowAlls(X, cols, rows)` = rowAlls(X, rows = cols, 
+     cols = rows), `rowAlls(X[cols, rows])` = rowAlls(X[cols, rows]), unit = "ms")

Table: Benchmarking of colAlls_X_S(), colAlls(X, rows, cols)() and colAlls(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	colAlls_X_S	0.003750	0.0041885	0.0044965	0.0044500	0.0046240	0.009132
2	colAlls(X, rows, cols)	0.004547	0.0051200	0.0053845	0.0053185	0.0054985	0.010758
3	colAlls(X[rows, cols])	0.020162	0.0210095	0.0217871	0.0214610	0.0216935	0.047257

	expr	min	lq	mean	median	uq	max
1	colAlls_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	colAlls(X, rows, cols)	1.212533	1.222395	1.197499	1.195169	1.189122	1.178055
3	colAlls(X[rows, cols])	5.376533	5.015996	4.845379	4.822697	4.691501	5.174880

Table: Benchmarking of rowAlls_X_S(), rowAlls(X, cols, rows)() and rowAlls(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	rowAlls_X_S	0.005668	0.006093	0.0063049	0.0062395	0.0063885	0.011094
2	rowAlls(X, cols, rows)	0.006547	0.006895	0.0073086	0.0070700	0.0072275	0.027045
3	rowAlls(X[cols, rows])	0.022228	0.022485	0.0227867	0.0225935	0.0227655	0.032418

	expr	min	lq	mean	median	uq	max
1	rowAlls_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowAlls(X, cols, rows)	1.155081	1.131627	1.159194	1.133104	1.131330	2.437804
3	rowAlls(X[cols, rows])	3.921666	3.690300	3.614110	3.621043	3.563513	2.922120

Figure: Benchmarking of colAlls_X_S(), colAlls(X, rows, cols)() and colAlls(X[rows, cols])() on 100x100 data as well as rowAlls_X_S(), rowAlls(X, cols, rows)() and rowAlls(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colAlls_X_S() and rowAlls_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	colAlls_X_S	3.750	4.1885	4.49647	4.4500	4.6240	9.132
2	rowAlls_X_S	5.668	6.0930	6.30493	6.2395	6.3885	11.094

	expr	min	lq	mean	median	uq	max
1	colAlls_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowAlls_X_S	1.511467	1.454697	1.402195	1.402135	1.381596	1.214849

Figure: Benchmarking of colAlls_X_S() and rowAlls_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 5161054 275.7    7916910 422.9  7916910 422.9
Vcells 9182428  70.1   33191153 253.3 53339345 407.0
> colStats <- microbenchmark(colAlls_X_S = colAlls(X_S), `colAlls(X, rows, cols)` = colAlls(X, rows = rows, 
+     cols = cols), `colAlls(X[rows, cols])` = colAlls(X[rows, cols]), unit = "ms")
> X <- t(X)
> X_S <- t(X_S)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5161048 275.7    7916910 422.9  7916910 422.9
Vcells 9187511  70.1   33191153 253.3 53339345 407.0
> rowStats <- microbenchmark(rowAlls_X_S = rowAlls(X_S), `rowAlls(X, cols, rows)` = rowAlls(X, rows = cols, 
+     cols = rows), `rowAlls(X[cols, rows])` = rowAlls(X[cols, rows]), unit = "ms")

Table: Benchmarking of colAlls_X_S(), colAlls(X, rows, cols)() and colAlls(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	colAlls_X_S	0.003014	0.0031925	0.0034670	0.003287	0.0033905	0.005824
2	colAlls(X, rows, cols)	0.004734	0.0050375	0.0055046	0.005180	0.0053820	0.010640
3	colAlls(X[rows, cols])	0.020060	0.0205445	0.0220699	0.021066	0.0212610	0.052467

	expr	min	lq	mean	median	uq	max
1	colAlls_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	colAlls(X, rows, cols)	1.570670	1.577917	1.587707	1.575905	1.587376	1.826923
3	colAlls(X[rows, cols])	6.655607	6.435239	6.365708	6.408883	6.270757	9.008757

Table: Benchmarking of rowAlls_X_S(), rowAlls(X, cols, rows)() and rowAlls(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	rowAlls_X_S	0.007138	0.0072560	0.0076136	0.0073890	0.0075935	0.022234
2	rowAlls(X, cols, rows)	0.009022	0.0091825	0.0095088	0.0093540	0.0095750	0.012307
3	rowAlls(X[cols, rows])	0.025563	0.0258000	0.0267082	0.0259975	0.0268810	0.062221

	expr	min	lq	mean	median	uq	max
1	rowAlls_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	rowAlls(X, cols, rows)	1.263939	1.265504	1.248916	1.265936	1.260947	0.5535216
3	rowAlls(X[cols, rows])	3.581255	3.555678	3.507962	3.518406	3.540001	2.7984618

Figure: Benchmarking of colAlls_X_S(), colAlls(X, rows, cols)() and colAlls(X[rows, cols])() on 1000x10 data as well as rowAlls_X_S(), rowAlls(X, cols, rows)() and rowAlls(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colAlls_X_S() and rowAlls_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	colAlls_X_S	3.014	3.1925	3.4670	3.287	3.3905	5.824
2	rowAlls_X_S	7.138	7.2560	7.6136	7.389	7.5935	22.234

	expr	min	lq	mean	median	uq	max
1	colAlls_X_S	1.000000	1.000000	1.00000	1.000000	1.00000	1.000000
2	rowAlls_X_S	2.368281	2.272827	2.19602	2.247946	2.23964	3.817651

Figure: Benchmarking of colAlls_X_S() and rowAlls_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 5161291 275.7    7916910 422.9  7916910 422.9
Vcells 9183261  70.1   33191153 253.3 53339345 407.0
> colStats <- microbenchmark(colAlls_X_S = colAlls(X_S), `colAlls(X, rows, cols)` = colAlls(X, rows = rows, 
+     cols = cols), `colAlls(X[rows, cols])` = colAlls(X[rows, cols]), unit = "ms")
> X <- t(X)
> X_S <- t(X_S)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5161285 275.7    7916910 422.9  7916910 422.9
Vcells 9188344  70.2   33191153 253.3 53339345 407.0
> rowStats <- microbenchmark(rowAlls_X_S = rowAlls(X_S), `rowAlls(X, cols, rows)` = rowAlls(X, rows = cols, 
+     cols = rows), `rowAlls(X[cols, rows])` = rowAlls(X[cols, rows]), unit = "ms")

Table: Benchmarking of colAlls_X_S(), colAlls(X, rows, cols)() and colAlls(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	colAlls_X_S	0.008170	0.008902	0.0159932	0.013339	0.0177990	0.132226
2	colAlls(X, rows, cols)	0.015708	0.016890	0.0246443	0.018383	0.0327495	0.062799
3	colAlls(X[rows, cols])	0.026395	0.027087	0.0389926	0.030673	0.0514955	0.076142

	expr	min	lq	mean	median	uq	max
1	colAlls_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	colAlls(X, rows, cols)	1.922644	1.897326	1.540927	1.378139	1.839963	0.4749369
3	colAlls(X[rows, cols])	3.230722	3.042799	2.438077	2.299498	2.893168	0.5758474

Table: Benchmarking of rowAlls_X_S(), rowAlls(X, cols, rows)() and rowAlls(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	rowAlls_X_S	0.009084	0.0104245	0.0114272	0.0111400	0.0118735	0.027885
2	rowAlls(X, cols, rows)	0.015078	0.0177890	0.0194490	0.0189195	0.0204300	0.049381
3	rowAlls(X[cols, rows])	0.026531	0.0274885	0.0286498	0.0280920	0.0291910	0.042547

	expr	min	lq	mean	median	uq	max
1	rowAlls_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowAlls(X, cols, rows)	1.659841	1.706461	1.701987	1.698339	1.720638	1.770880
3	rowAlls(X[cols, rows])	2.920630	2.636913	2.507158	2.521723	2.458500	1.525802

Figure: Benchmarking of colAlls_X_S(), colAlls(X, rows, cols)() and colAlls(X[rows, cols])() on 10x1000 data as well as rowAlls_X_S(), rowAlls(X, cols, rows)() and rowAlls(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colAlls_X_S() and rowAlls_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
2	rowAlls_X_S	9.084	10.4245	11.42721	11.140	11.8735	27.885
1	colAlls_X_S	8.170	8.9020	15.99319	13.339	17.7990	132.226

	expr	min	lq	mean	median	uq	max
2	rowAlls_X_S	1.0000000	1.0000000	1.000000	1.000000	1.000000	1.000000
1	colAlls_X_S	0.8993835	0.8539498	1.399571	1.197397	1.499052	4.741833

Figure: Benchmarking of colAlls_X_S() and rowAlls_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 5161513 275.7    7916910 422.9  7916910 422.9
Vcells 9205928  70.3   33191153 253.3 53339345 407.0
> colStats <- microbenchmark(colAlls_X_S = colAlls(X_S), `colAlls(X, rows, cols)` = colAlls(X, rows = rows, 
+     cols = cols), `colAlls(X[rows, cols])` = colAlls(X[rows, cols]), unit = "ms")
> X <- t(X)
> X_S <- t(X_S)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5161507 275.7    7916910 422.9  7916910 422.9
Vcells 9256011  70.7   33191153 253.3 53339345 407.0
> rowStats <- microbenchmark(rowAlls_X_S = rowAlls(X_S), `rowAlls(X, cols, rows)` = rowAlls(X, rows = cols, 
+     cols = rows), `rowAlls(X[cols, rows])` = rowAlls(X[cols, rows]), unit = "ms")

Table: Benchmarking of colAlls_X_S(), colAlls(X, rows, cols)() and colAlls(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	colAlls_X_S	0.007357	0.0085510	0.0104021	0.0097815	0.0106425	0.055342
2	colAlls(X, rows, cols)	0.017228	0.0181445	0.0204397	0.0196645	0.0207660	0.056603
3	colAlls(X[rows, cols])	0.130260	0.1408425	0.1490012	0.1481745	0.1588430	0.191139

	expr	min	lq	mean	median	uq	max
1	colAlls_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	colAlls(X, rows, cols)	2.341715	2.121916	1.964965	2.010377	1.951233	1.022786
3	colAlls(X[rows, cols])	17.705586	16.470881	14.324202	15.148444	14.925346	3.453778

Table: Benchmarking of rowAlls_X_S(), rowAlls(X, cols, rows)() and rowAlls(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	rowAlls_X_S	0.024087	0.026607	0.0289235	0.0279065	0.0300425	0.060435
2	rowAlls(X, cols, rows)	0.030601	0.032826	0.0360491	0.0347675	0.0382620	0.056998
3	rowAlls(X[cols, rows])	0.143706	0.146069	0.1604577	0.1546995	0.1733410	0.230073

	expr	min	lq	mean	median	uq	max
1	rowAlls_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowAlls(X, cols, rows)	1.270436	1.233736	1.246359	1.245857	1.273596	0.943129
3	rowAlls(X[cols, rows])	5.966123	5.489871	5.547660	5.543494	5.769859	3.806950

Figure: Benchmarking of colAlls_X_S(), colAlls(X, rows, cols)() and colAlls(X[rows, cols])() on 100x1000 data as well as rowAlls_X_S(), rowAlls(X, cols, rows)() and rowAlls(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colAlls_X_S() and rowAlls_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	colAlls_X_S	7.357	8.551	10.40206	9.7815	10.6425	55.342
2	rowAlls_X_S	24.087	26.607	28.92350	27.9065	30.0425	60.435

	expr	min	lq	mean	median	uq	max
1	colAlls_X_S	1.000000	1.000000	1.000000	1.000000	1.00000	1.000000
2	rowAlls_X_S	3.274025	3.111566	2.780555	2.852988	2.82288	1.092028

Figure: Benchmarking of colAlls_X_S() and rowAlls_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 5161729 275.7    7916910 422.9  7916910 422.9
Vcells 9206596  70.3   33191153 253.3 53339345 407.0
> colStats <- microbenchmark(colAlls_X_S = colAlls(X_S), `colAlls(X, rows, cols)` = colAlls(X, rows = rows, 
+     cols = cols), `colAlls(X[rows, cols])` = colAlls(X[rows, cols]), unit = "ms")
> X <- t(X)
> X_S <- t(X_S)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5161723 275.7    7916910 422.9  7916910 422.9
Vcells 9256679  70.7   33191153 253.3 53339345 407.0
> rowStats <- microbenchmark(rowAlls_X_S = rowAlls(X_S), `rowAlls(X, cols, rows)` = rowAlls(X, rows = cols, 
+     cols = rows), `rowAlls(X[cols, rows])` = rowAlls(X[cols, rows]), unit = "ms")

Table: Benchmarking of colAlls_X_S(), colAlls(X, rows, cols)() and colAlls(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	colAlls_X_S	0.003326	0.0039155	0.0045337	0.0042120	0.0045445	0.024890
2	colAlls(X, rows, cols)	0.005185	0.0059210	0.0065720	0.0062450	0.0067195	0.021305
3	colAlls(X[rows, cols])	0.125968	0.1355105	0.1432898	0.1412175	0.1520975	0.205071

	expr	min	lq	mean	median	uq	max
1	colAlls_X_S	1.00000	1.000000	1.000000	1.000000	1.00000	1.0000000
2	colAlls(X, rows, cols)	1.55893	1.512195	1.449581	1.482669	1.47860	0.8559663
3	colAlls(X[rows, cols])	37.87372	34.608734	31.605268	33.527422	33.46848	8.2390920

Table: Benchmarking of rowAlls_X_S(), rowAlls(X, cols, rows)() and rowAlls(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	rowAlls_X_S	0.021149	0.0216690	0.0236387	0.0231610	0.0248480	0.034273
2	rowAlls(X, cols, rows)	0.023540	0.0246015	0.0267711	0.0259440	0.0278365	0.049886
3	rowAlls(X[cols, rows])	0.140070	0.1408635	0.1542066	0.1504155	0.1622935	0.238308

	expr	min	lq	mean	median	uq	max
1	rowAlls_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowAlls(X, cols, rows)	1.113055	1.135332	1.132512	1.120159	1.120271	1.455548
3	rowAlls(X[cols, rows])	6.623008	6.500692	6.523493	6.494344	6.531451	6.953228

Figure: Benchmarking of colAlls_X_S(), colAlls(X, rows, cols)() and colAlls(X[rows, cols])() on 1000x100 data as well as rowAlls_X_S(), rowAlls(X, cols, rows)() and rowAlls(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colAlls_X_S() and rowAlls_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	colAlls_X_S	3.326	3.9155	4.53373	4.212	4.5445	24.890
2	rowAlls_X_S	21.149	21.6690	23.63865	23.161	24.8480	34.273

	expr	min	lq	mean	median	uq	max
1	colAlls_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowAlls_X_S	6.358689	5.534159	5.213952	5.498813	5.467708	1.376979

Figure: Benchmarking of colAlls_X_S() and rowAlls_X_S() 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 11.36 secs.

Reproducibility

To reproduce this report, do:

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

---

Copyright Dongcan Jiang. Last updated on 2021-08-25 22:09:58 (+0200 UTC). Powered by RSP.