matrixStats.benchmarks

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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 5177100 276.5    8529671 455.6  8529671 455.6
Vcells 9627476  73.5   31876688 243.2 60562128 462.1
> 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 5168708 276.1    8529671 455.6  8529671 455.6
Vcells 9600142  73.3   31876688 243.2 60562128 462.1
> 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	0.003219	0.0033310	0.0036600	0.0034045	0.0035155	0.023172
2	colAnys(X, rows, cols)	0.003705	0.0037865	0.0039046	0.0038480	0.0039570	0.006410
3	colAnys(X[rows, cols])	0.004173	0.0044695	0.0046776	0.0045865	0.0047065	0.009391

	expr	min	lq	mean	median	uq	max
1	colAnys_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	colAnys(X, rows, cols)	1.150979	1.136746	1.066827	1.130269	1.125587	0.2766270
3	colAnys(X[rows, cols])	1.296365	1.341789	1.278008	1.347187	1.338785	0.4052736

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	0.003381	0.0034840	0.0036096	0.0035695	0.003663	0.005945
2	rowAnys(X, cols, rows)	0.003786	0.0038835	0.0042319	0.0039635	0.004079	0.026583
3	rowAnys(X[cols, rows])	0.004370	0.0046275	0.0048266	0.0047425	0.004898	0.007305

	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)	1.119787	1.114667	1.172418	1.110380	1.113568	4.471489
3	rowAnys(X[cols, rows])	1.292517	1.328215	1.337178	1.328618	1.337155	1.228764

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	3.219	3.331	3.66004	3.4045	3.5155	23.172
2	rowAnys_X_S	3.381	3.484	3.60955	3.5695	3.6630	5.945

	expr	min	lq	mean	median	uq	max
1	colAnys_X_S	1.000000	1.000000	1.0000000	1.000000	1.000000	1.0000000
2	rowAnys_X_S	1.050326	1.045932	0.9862051	1.048465	1.041957	0.2565596

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 5167229 276.0    8529671 455.6  8529671 455.6
Vcells 9268270  70.8   31876688 243.2 60562128 462.1
> 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 5167205 276.0    8529671 455.6  8529671 455.6
Vcells 9273323  70.8   31876688 243.2 60562128 462.1
> 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	0.004428	0.0047970	0.0050255	0.0049555	0.0051425	0.011703
2	colAnys(X, rows, cols)	0.005662	0.0059565	0.0062993	0.0062065	0.0064455	0.011864
3	colAnys(X[rows, cols])	0.020373	0.0211020	0.0218015	0.0212835	0.0216425	0.052099

	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)	1.278681	1.241714	1.253455	1.252447	1.253379	1.013757
3	colAnys(X[rows, cols])	4.600949	4.398999	4.338137	4.294925	4.208556	4.451765

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	0.008511	0.0090205	0.0092478	0.0091770	0.009338	0.014797
2	rowAnys(X, cols, rows)	0.010165	0.0108165	0.0114624	0.0111000	0.011370	0.036588
3	rowAnys(X[cols, rows])	0.025095	0.0255255	0.0259974	0.0257245	0.025930	0.040408

	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)	1.194337	1.199102	1.239463	1.209546	1.217605	2.472663
3	rowAnys(X[cols, rows])	2.948537	2.829721	2.811184	2.803149	2.776826	2.730824

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	4.428	4.7970	5.02554	4.9555	5.1425	11.703
2	rowAnys_X_S	8.511	9.0205	9.24785	9.1770	9.3380	14.797

	expr	min	lq	mean	median	uq	max
1	colAnys_X_S	1.000000	1.000000	1.00000	1.000000	1.000000	1.000000
2	rowAnys_X_S	1.922087	1.880446	1.84017	1.851882	1.815848	1.264377

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 5167970 276.0    8529671 455.6  8529671 455.6
Vcells 9272324  70.8   31876688 243.2 60562128 462.1
> 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 5167946 276.0    8529671 455.6  8529671 455.6
Vcells 9277377  70.8   31876688 243.2 60562128 462.1
> 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	0.003319	0.0034875	0.0036520	0.0035650	0.0036920	0.008389
2	colAnys(X, rows, cols)	0.006436	0.0068705	0.0074484	0.0071120	0.0074770	0.014991
3	colAnys(X[rows, cols])	0.020304	0.0206600	0.0213218	0.0207905	0.0210155	0.053564

	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)	1.939138	1.970036	2.039515	1.994951	2.025190	1.786983
3	colAnys(X[rows, cols])	6.117505	5.924014	5.838331	5.831837	5.692172	6.385028

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	0.009145	0.009619	0.0100038	0.009948	0.0101905	0.015216
2	rowAnys(X, cols, rows)	0.012208	0.012966	0.0135462	0.013311	0.0137915	0.021230
3	rowAnys(X[cols, rows])	0.026203	0.027358	0.0287920	0.027814	0.0288275	0.092350

	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)	1.334937	1.347957	1.354115	1.338058	1.353368	1.395242
3	rowAnys(X[cols, rows])	2.865282	2.844163	2.878118	2.795939	2.828860	6.069269

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	3.319	3.4875	3.65203	3.565	3.6920	8.389
2	rowAnys_X_S	9.145	9.6190	10.00376	9.948	10.1905	15.216

	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	2.755348	2.758136	2.739233	2.790463	2.760157	1.813804

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 5168175 276.1    8529671 455.6  8529671 455.6
Vcells 9273124  70.8   31876688 243.2 60562128 462.1
> 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 5168151 276.1    8529671 455.6  8529671 455.6
Vcells 9278177  70.8   31876688 243.2 60562128 462.1
> 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	0.016067	0.0168255	0.0185005	0.0173310	0.0180270	0.047947
2	colAnys(X, rows, cols)	0.016226	0.0195075	0.0230198	0.0210520	0.0231620	0.038127
3	colAnys(X[rows, cols])	0.034174	0.0352295	0.0399646	0.0359865	0.0373125	0.064604

	expr	min	lq	mean	median	uq	max
1	colAnys_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	colAnys(X, rows, cols)	1.009896	1.159401	1.244275	1.214702	1.284850	0.7951905
3	colAnys(X[rows, cols])	2.126968	2.093816	2.160184	2.076424	2.069812	1.3474044

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	0.023456	0.0255020	0.0269853	0.0267640	0.028216	0.039947
2	rowAnys(X, cols, rows)	0.022930	0.0310540	0.0339282	0.0330200	0.034288	0.100766
3	rowAnys(X[cols, rows])	0.039365	0.0425765	0.0448292	0.0441295	0.045917	0.066982

	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)	0.977575	1.217708	1.257284	1.233747	1.215197	2.522492
3	rowAnys(X[cols, rows])	1.678249	1.669536	1.661248	1.648838	1.627339	1.676772

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	16.067	16.8255	18.50054	17.331	18.027	47.947
2	rowAnys_X_S	23.456	25.5020	26.98528	26.764	28.216	39.947

	expr	min	lq	mean	median	uq	max
1	colAnys_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	rowAnys_X_S	1.459887	1.515676	1.458621	1.544285	1.565208	0.8331491

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 5168386 276.1    8529671 455.6  8529671 455.6
Vcells 9295783  71.0   31876688 243.2 60562128 462.1
> 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 5168362 276.1    8529671 455.6  8529671 455.6
Vcells 9345836  71.4   31876688 243.2 60562128 462.1
> 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	0.014802	0.016241	0.0177867	0.0171440	0.0178130	0.062863
2	colAnys(X, rows, cols)	0.017272	0.021048	0.0224166	0.0220205	0.0232735	0.053511
3	colAnys(X[rows, cols])	0.130239	0.137066	0.1459472	0.1433940	0.1508165	0.191160

	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)	1.166869	1.295979	1.260298	1.284444	1.306546	0.851232
3	colAnys(X[rows, cols])	8.798743	8.439505	8.205406	8.364092	8.466654	3.040898

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	0.050192	0.0537165	0.0584704	0.0572910	0.0621585	0.093070
2	rowAnys(X, cols, rows)	0.052976	0.0560360	0.0610338	0.0584985	0.0645260	0.087732
3	rowAnys(X[cols, rows])	0.160618	0.1710855	0.1862103	0.1789950	0.1988935	0.267386

	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)	1.055467	1.043180	1.043842	1.021077	1.038088	0.9426453
3	rowAnys(X[cols, rows])	3.200072	3.184971	3.184696	3.124313	3.199780	2.8729558

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	14.802	16.2410	17.78671	17.144	17.8130	62.863
2	rowAnys_X_S	50.192	53.7165	58.47037	57.291	62.1585	93.070

	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	3.390893	3.307463	3.287307	3.341752	3.489502	1.480521

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 5168596 276.1    8529671 455.6  8529671 455.6
Vcells 9296443  71.0   31876688 243.2 60562128 462.1
> 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 5168572 276.1    8529671 455.6  8529671 455.6
Vcells 9346496  71.4   31876688 243.2 60562128 462.1
> 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	0.003695	0.004417	0.0052580	0.0047455	0.005138	0.034422
2	colAnys(X, rows, cols)	0.007045	0.007841	0.0085871	0.0082510	0.009057	0.021070
3	colAnys(X[rows, cols])	0.126519	0.136779	0.1463808	0.1469260	0.153856	0.204313

	expr	min	lq	mean	median	uq	max
1	colAnys_X_S	1.000000	1.000000	1.000000	1.00000	1.000000	1.0000000
2	colAnys(X, rows, cols)	1.906631	1.775187	1.633162	1.73870	1.762748	0.6121085
3	colAnys(X[rows, cols])	34.240595	30.966493	27.839850	30.96112	29.944726	5.9355354

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	0.037217	0.0386990	0.0421941	0.041443	0.0436280	0.061977
2	rowAnys(X, cols, rows)	0.043439	0.0459090	0.0499184	0.048884	0.0513085	0.085342
3	rowAnys(X[cols, rows])	0.152316	0.1581785	0.1705069	0.164383	0.1809795	0.261740

	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)	1.167182	1.186310	1.183067	1.179548	1.176045	1.376995
3	rowAnys(X[cols, rows])	4.092646	4.087405	4.041015	3.966484	4.148242	4.223180

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	3.695	4.417	5.25796	4.7455	5.138	34.422
2	rowAnys_X_S	37.217	38.699	42.19407	41.4430	43.628	61.977

	expr	min	lq	mean	median	uq	max
1	colAnys_X_S	1.00000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowAnys_X_S	10.07226	8.761377	8.024799	8.733116	8.491242	1.800506

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 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 11.11 secs.

Reproducibility

To reproduce this report, do:

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

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Copyright Dongcan Jiang. Last updated on 2021-08-25 18:50:50 (+0200 UTC). Powered by RSP.