colRowRanges_subset

matrixStats: Benchmark report

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colRanges() and rowRanges() benchmarks on subsetted computation

This report benchmark the performance of colRanges() and rowRanges() on subsetted computation.

Data type "integer"

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 = mode)

Results

10x10 integer 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 3182203 170.0    5709258 305.0  5709258 305.0
Vcells 6337876  48.4   22343563 170.5 56666022 432.4
> colStats <- microbenchmark(colRanges_X_S = colRanges(X_S, na.rm = FALSE), `colRanges(X, rows, cols)` = colRanges(X, 
+     rows = rows, cols = cols, na.rm = FALSE), `colRanges(X[rows, cols])` = colRanges(X[rows, cols], 
+     na.rm = FALSE), unit = "ms")
> X <- t(X)
> X_S <- t(X_S)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3180566 169.9    5709258 305.0  5709258 305.0
Vcells 6333239  48.4   22343563 170.5 56666022 432.4
> rowStats <- microbenchmark(rowRanges_X_S = rowRanges(X_S, na.rm = FALSE), `rowRanges(X, cols, rows)` = rowRanges(X, 
+     rows = cols, cols = rows, na.rm = FALSE), `rowRanges(X[cols, rows])` = rowRanges(X[cols, rows], 
+     na.rm = FALSE), unit = "ms")

Table: Benchmarking of colRanges_X_S(), colRanges(X, rows, cols)() and colRanges(X[rows, cols])() on integer+10x10 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colRanges_X_S	0.001090	0.0011260	0.0021293	0.0011460	0.0012185	0.090667
2	colRanges(X, rows, cols)	0.001244	0.0012865	0.0013782	0.0013170	0.0014005	0.003072
3	colRanges(X[rows, cols])	0.001613	0.0017385	0.0020145	0.0019135	0.0020670	0.006976

	expr	min	lq	mean	median	uq	max
1	colRanges_X_S	1.000000	1.000000	1.0000000	1.000000	1.000000	1.0000000
2	colRanges(X, rows, cols)	1.141284	1.142540	0.6472483	1.149215	1.149364	0.0338822
3	colRanges(X[rows, cols])	1.479817	1.543961	0.9461221	1.669721	1.696348	0.0769409

Table: Benchmarking of rowRanges_X_S(), rowRanges(X, cols, rows)() and rowRanges(X[cols, rows])() on integer+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	rowRanges_X_S	0.001092	0.0011170	0.0012227	0.001139	0.0012520	0.003600
2	rowRanges(X, cols, rows)	0.001252	0.0012860	0.0023361	0.001307	0.0014580	0.097008
3	rowRanges(X[cols, rows])	0.001591	0.0016915	0.0019833	0.001850	0.0020055	0.005741

	expr	min	lq	mean	median	uq	max
1	rowRanges_X_S	1.00000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowRanges(X, cols, rows)	1.14652	1.151298	1.910544	1.147498	1.164537	26.946667
3	rowRanges(X[cols, rows])	1.45696	1.514324	1.622015	1.624232	1.601837	1.594722

Figure: Benchmarking of colRanges_X_S(), colRanges(X, rows, cols)() and colRanges(X[rows, cols])() on integer+10x10 data as well as rowRanges_X_S(), rowRanges(X, cols, rows)() and rowRanges(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colRanges_X_S() and rowRanges_X_S() on integer+10x10 data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
2	rowRanges_X_S	1.092	1.117	1.22272	1.139	1.2520	3.600
1	colRanges_X_S	1.090	1.126	2.12926	1.146	1.2185	90.667

	expr	min	lq	mean	median	uq	max
2	rowRanges_X_S	1.0000000	1.000000	1.000000	1.000000	1.0000000	1.00000
1	colRanges_X_S	0.9981685	1.008057	1.741413	1.006146	0.9732428	25.18528

Figure: Benchmarking of colRanges_X_S() and rowRanges_X_S() on integer+10x10 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

100x100 integer 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 3179169 169.8    5709258 305.0  5709258 305.0
Vcells 6002108  45.8   22343563 170.5 56666022 432.4
> colStats <- microbenchmark(colRanges_X_S = colRanges(X_S, na.rm = FALSE), `colRanges(X, rows, cols)` = colRanges(X, 
+     rows = rows, cols = cols, na.rm = FALSE), `colRanges(X[rows, cols])` = colRanges(X[rows, cols], 
+     na.rm = FALSE), unit = "ms")
> X <- t(X)
> X_S <- t(X_S)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3179160 169.8    5709258 305.0  5709258 305.0
Vcells 6007186  45.9   22343563 170.5 56666022 432.4
> rowStats <- microbenchmark(rowRanges_X_S = rowRanges(X_S, na.rm = FALSE), `rowRanges(X, cols, rows)` = rowRanges(X, 
+     rows = cols, cols = rows, na.rm = FALSE), `rowRanges(X[cols, rows])` = rowRanges(X[cols, rows], 
+     na.rm = FALSE), unit = "ms")

Table: Benchmarking of colRanges_X_S(), colRanges(X, rows, cols)() and colRanges(X[rows, cols])() on integer+100x100 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colRanges_X_S	0.010970	0.0113120	0.0115660	0.0115065	0.0117045	0.015767
2	colRanges(X, rows, cols)	0.013220	0.0137885	0.0141243	0.0140740	0.0143185	0.017172
3	colRanges(X[rows, cols])	0.018492	0.0189510	0.0196369	0.0191305	0.0193970	0.051053

	expr	min	lq	mean	median	uq	max
1	colRanges_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	colRanges(X, rows, cols)	1.205105	1.218927	1.221185	1.223135	1.223333	1.089110
3	colRanges(X[rows, cols])	1.685688	1.675301	1.697803	1.662582	1.657226	3.237965

Table: Benchmarking of rowRanges_X_S(), rowRanges(X, cols, rows)() and rowRanges(X[cols, rows])() on integer+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	rowRanges_X_S	0.010728	0.0110495	0.0113798	0.0112150	0.0114895	0.015325
2	rowRanges(X, cols, rows)	0.011691	0.0122685	0.0125412	0.0124550	0.0126930	0.015745
3	rowRanges(X[cols, rows])	0.018101	0.0186580	0.0195912	0.0189275	0.0192535	0.064185

	expr	min	lq	mean	median	uq	max
1	rowRanges_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowRanges(X, cols, rows)	1.089765	1.110322	1.102051	1.110566	1.104748	1.027406
3	rowRanges(X[cols, rows])	1.687267	1.688583	1.721567	1.687695	1.675747	4.188255

Figure: Benchmarking of colRanges_X_S(), colRanges(X, rows, cols)() and colRanges(X[rows, cols])() on integer+100x100 data as well as rowRanges_X_S(), rowRanges(X, cols, rows)() and rowRanges(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colRanges_X_S() and rowRanges_X_S() on integer+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
2	rowRanges_X_S	10.728	11.0495	11.37985	11.2150	11.4895	15.325
1	colRanges_X_S	10.970	11.3120	11.56605	11.5065	11.7045	15.767

	expr	min	lq	mean	median	uq	max
2	rowRanges_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	colRanges_X_S	1.022558	1.023757	1.016362	1.025992	1.018713	1.028842

Figure: Benchmarking of colRanges_X_S() and rowRanges_X_S() on integer+100x100 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

1000x10 integer 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 3179918 169.9    5709258 305.0  5709258 305.0
Vcells 6006174  45.9   22343563 170.5 56666022 432.4
> colStats <- microbenchmark(colRanges_X_S = colRanges(X_S, na.rm = FALSE), `colRanges(X, rows, cols)` = colRanges(X, 
+     rows = rows, cols = cols, na.rm = FALSE), `colRanges(X[rows, cols])` = colRanges(X[rows, cols], 
+     na.rm = FALSE), unit = "ms")
> X <- t(X)
> X_S <- t(X_S)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3179909 169.9    5709258 305.0  5709258 305.0
Vcells 6011252  45.9   22343563 170.5 56666022 432.4
> rowStats <- microbenchmark(rowRanges_X_S = rowRanges(X_S, na.rm = FALSE), `rowRanges(X, cols, rows)` = rowRanges(X, 
+     rows = cols, cols = rows, na.rm = FALSE), `rowRanges(X[cols, rows])` = rowRanges(X[cols, rows], 
+     na.rm = FALSE), unit = "ms")

Table: Benchmarking of colRanges_X_S(), colRanges(X, rows, cols)() and colRanges(X[rows, cols])() on integer+1000x10 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colRanges_X_S	0.007932	0.0080580	0.0082060	0.008130	0.0082630	0.011447
2	colRanges(X, rows, cols)	0.009957	0.0100750	0.0105154	0.010216	0.0104325	0.036373
3	colRanges(X[rows, cols])	0.015409	0.0156155	0.0160531	0.015756	0.0159915	0.025840

	expr	min	lq	mean	median	uq	max
1	colRanges_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	colRanges(X, rows, cols)	1.255295	1.250310	1.281421	1.256581	1.262556	3.177514
3	colRanges(X[rows, cols])	1.942637	1.937888	1.956255	1.938007	1.935314	2.257360

Table: Benchmarking of rowRanges_X_S(), rowRanges(X, cols, rows)() and rowRanges(X[cols, rows])() on integer+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	rowRanges_X_S	0.008930	0.009213	0.0098794	0.0092835	0.0093795	0.031482
2	rowRanges(X, cols, rows)	0.010282	0.010612	0.0118905	0.0107725	0.0109395	0.036882
3	rowRanges(X[cols, rows])	0.017307	0.017879	0.0192193	0.0180830	0.0183120	0.053687

	expr	min	lq	mean	median	uq	max
1	rowRanges_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowRanges(X, cols, rows)	1.151400	1.151851	1.203561	1.160392	1.166320	1.171527
3	rowRanges(X[cols, rows])	1.938074	1.940627	1.945382	1.947865	1.952343	1.705324

Figure: Benchmarking of colRanges_X_S(), colRanges(X, rows, cols)() and colRanges(X[rows, cols])() on integer+1000x10 data as well as rowRanges_X_S(), rowRanges(X, cols, rows)() and rowRanges(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colRanges_X_S() and rowRanges_X_S() on integer+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	colRanges_X_S	7.932	8.058	8.20602	8.1300	8.2630	11.447
2	rowRanges_X_S	8.930	9.213	9.87943	9.2835	9.3795	31.482

	expr	min	lq	mean	median	uq	max
1	colRanges_X_S	1.000000	1.000000	1.000000	1.000000	1.00000	1.00000
2	rowRanges_X_S	1.125819	1.143336	1.203925	1.141882	1.13512	2.75024

Figure: Benchmarking of colRanges_X_S() and rowRanges_X_S() on integer+1000x10 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

10x1000 integer 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 3180124 169.9    5709258 305.0  5709258 305.0
Vcells 6007031  45.9   22343563 170.5 56666022 432.4
> colStats <- microbenchmark(colRanges_X_S = colRanges(X_S, na.rm = FALSE), `colRanges(X, rows, cols)` = colRanges(X, 
+     rows = rows, cols = cols, na.rm = FALSE), `colRanges(X[rows, cols])` = colRanges(X[rows, cols], 
+     na.rm = FALSE), unit = "ms")
> X <- t(X)
> X_S <- t(X_S)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3180115 169.9    5709258 305.0  5709258 305.0
Vcells 6012109  45.9   22343563 170.5 56666022 432.4
> rowStats <- microbenchmark(rowRanges_X_S = rowRanges(X_S, na.rm = FALSE), `rowRanges(X, cols, rows)` = rowRanges(X, 
+     rows = cols, cols = rows, na.rm = FALSE), `rowRanges(X[cols, rows])` = rowRanges(X[cols, rows], 
+     na.rm = FALSE), unit = "ms")

Table: Benchmarking of colRanges_X_S(), colRanges(X, rows, cols)() and colRanges(X[rows, cols])() on integer+10x1000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colRanges_X_S	0.021663	0.0226605	0.0233331	0.0232045	0.0236995	0.029203
2	colRanges(X, rows, cols)	0.027995	0.0293930	0.0305225	0.0302155	0.0308265	0.067626
3	colRanges(X[rows, cols])	0.030748	0.0316230	0.0327176	0.0320380	0.0328885	0.049906

	expr	min	lq	mean	median	uq	max
1	colRanges_X_S	1.000000	1.000000	1.000000	1.00000	1.000000	1.000000
2	colRanges(X, rows, cols)	1.292296	1.297103	1.308122	1.30214	1.300724	2.315721
3	colRanges(X[rows, cols])	1.419379	1.395512	1.402196	1.38068	1.387730	1.708934

Table: Benchmarking of rowRanges_X_S(), rowRanges(X, cols, rows)() and rowRanges(X[cols, rows])() on integer+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	rowRanges_X_S	0.021073	0.0220045	0.0226350	0.0224385	0.0229385	0.038305
2	rowRanges(X, cols, rows)	0.022086	0.0238320	0.0242872	0.0243370	0.0247630	0.028424
3	rowRanges(X[cols, rows])	0.029005	0.0297885	0.0303246	0.0301120	0.0305035	0.042943

	expr	min	lq	mean	median	uq	max
1	rowRanges_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	rowRanges(X, cols, rows)	1.048071	1.083051	1.072995	1.084609	1.079539	0.7420441
3	rowRanges(X[cols, rows])	1.376406	1.353746	1.339722	1.341979	1.329795	1.1210808

Figure: Benchmarking of colRanges_X_S(), colRanges(X, rows, cols)() and colRanges(X[rows, cols])() on integer+10x1000 data as well as rowRanges_X_S(), rowRanges(X, cols, rows)() and rowRanges(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colRanges_X_S() and rowRanges_X_S() on integer+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	rowRanges_X_S	21.073	22.0045	22.63495	22.4385	22.9385	38.305
1	colRanges_X_S	21.663	22.6605	23.33309	23.2045	23.6995	29.203

	expr	min	lq	mean	median	uq	max
2	rowRanges_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
1	colRanges_X_S	1.027998	1.029812	1.030844	1.034138	1.033176	0.7623809

Figure: Benchmarking of colRanges_X_S() and rowRanges_X_S() on integer+10x1000 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

100x1000 integer 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 3180323 169.9    5709258 305.0  5709258 305.0
Vcells 6029691  46.1   22343563 170.5 56666022 432.4
> colStats <- microbenchmark(colRanges_X_S = colRanges(X_S, na.rm = FALSE), `colRanges(X, rows, cols)` = colRanges(X, 
+     rows = rows, cols = cols, na.rm = FALSE), `colRanges(X[rows, cols])` = colRanges(X[rows, cols], 
+     na.rm = FALSE), unit = "ms")
> X <- t(X)
> X_S <- t(X_S)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3180314 169.9    5709258 305.0  5709258 305.0
Vcells 6079769  46.4   22343563 170.5 56666022 432.4
> rowStats <- microbenchmark(rowRanges_X_S = rowRanges(X_S, na.rm = FALSE), `rowRanges(X, cols, rows)` = rowRanges(X, 
+     rows = cols, cols = rows, na.rm = FALSE), `rowRanges(X[cols, rows])` = rowRanges(X[cols, rows], 
+     na.rm = FALSE), unit = "ms")

Table: Benchmarking of colRanges_X_S(), colRanges(X, rows, cols)() and colRanges(X[rows, cols])() on integer+100x1000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colRanges_X_S	0.106644	0.1073510	0.1080100	0.107762	0.1081135	0.115837
2	colRanges(X, rows, cols)	0.133348	0.1340055	0.1356224	0.134500	0.1351675	0.220734
3	colRanges(X[rows, cols])	0.178587	0.1796025	0.1813233	0.180086	0.1811260	0.202905

	expr	min	lq	mean	median	uq	max
1	colRanges_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	colRanges(X, rows, cols)	1.250403	1.248293	1.255646	1.248121	1.250237	1.905557
3	colRanges(X[rows, cols])	1.674609	1.673040	1.678763	1.671146	1.675332	1.751642

Table: Benchmarking of rowRanges_X_S(), rowRanges(X, cols, rows)() and rowRanges(X[cols, rows])() on integer+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	rowRanges_X_S	0.104170	0.1051490	0.1060804	0.1055540	0.1059540	0.127620
2	rowRanges(X, cols, rows)	0.116271	0.1175575	0.1183745	0.1180615	0.1184810	0.134772
3	rowRanges(X[cols, rows])	0.167494	0.1690150	0.1712729	0.1696345	0.1706875	0.227266

	expr	min	lq	mean	median	uq	max
1	rowRanges_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowRanges(X, cols, rows)	1.116166	1.118009	1.115894	1.118494	1.118231	1.056041
3	rowRanges(X[cols, rows])	1.607891	1.607386	1.614558	1.607087	1.610958	1.780802

Figure: Benchmarking of colRanges_X_S(), colRanges(X, rows, cols)() and colRanges(X[rows, cols])() on integer+100x1000 data as well as rowRanges_X_S(), rowRanges(X, cols, rows)() and rowRanges(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colRanges_X_S() and rowRanges_X_S() on integer+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
2	rowRanges_X_S	104.170	105.149	106.0804	105.554	105.9540	127.620
1	colRanges_X_S	106.644	107.351	108.0100	107.762	108.1135	115.837

	expr	min	lq	mean	median	uq	max
2	rowRanges_X_S	1.00000	1.000000	1.00000	1.000000	1.000000	1.0000000
1	colRanges_X_S	1.02375	1.020942	1.01819	1.020918	1.020382	0.9076712

Figure: Benchmarking of colRanges_X_S() and rowRanges_X_S() on integer+100x1000 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

1000x100 integer 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 3180539 169.9    5709258 305.0  5709258 305.0
Vcells 6030482  46.1   22343563 170.5 56666022 432.4
> colStats <- microbenchmark(colRanges_X_S = colRanges(X_S, na.rm = FALSE), `colRanges(X, rows, cols)` = colRanges(X, 
+     rows = rows, cols = cols, na.rm = FALSE), `colRanges(X[rows, cols])` = colRanges(X[rows, cols], 
+     na.rm = FALSE), unit = "ms")
> X <- t(X)
> X_S <- t(X_S)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3180530 169.9    5709258 305.0  5709258 305.0
Vcells 6080560  46.4   22343563 170.5 56666022 432.4
> rowStats <- microbenchmark(rowRanges_X_S = rowRanges(X_S, na.rm = FALSE), `rowRanges(X, cols, rows)` = rowRanges(X, 
+     rows = cols, cols = rows, na.rm = FALSE), `rowRanges(X[cols, rows])` = rowRanges(X[cols, rows], 
+     na.rm = FALSE), unit = "ms")

Table: Benchmarking of colRanges_X_S(), colRanges(X, rows, cols)() and colRanges(X[rows, cols])() on integer+1000x100 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colRanges_X_S	0.070667	0.0712605	0.0719798	0.0714400	0.0716405	0.096485
2	colRanges(X, rows, cols)	0.085166	0.0858685	0.0868319	0.0861460	0.0868310	0.108069
3	colRanges(X[rows, cols])	0.134336	0.1348045	0.1371886	0.1351895	0.1365980	0.212129

	expr	min	lq	mean	median	uq	max
1	colRanges_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.00000
2	colRanges(X, rows, cols)	1.205174	1.204994	1.206338	1.205851	1.212038	1.12006
3	colRanges(X[rows, cols])	1.900972	1.891714	1.905933	1.892350	1.906715	2.19857

Table: Benchmarking of rowRanges_X_S(), rowRanges(X, cols, rows)() and rowRanges(X[cols, rows])() on integer+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	rowRanges_X_S	0.076880	0.0773960	0.0778467	0.0777270	0.0779475	0.083768
2	rowRanges(X, cols, rows)	0.090642	0.0913865	0.0918813	0.0915955	0.0918210	0.099784
3	rowRanges(X[cols, rows])	0.148250	0.1490800	0.1511589	0.1494645	0.1503420	0.229534

	expr	min	lq	mean	median	uq	max
1	rowRanges_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowRanges(X, cols, rows)	1.179006	1.180765	1.180285	1.178426	1.177985	1.191195
3	rowRanges(X[cols, rows])	1.928330	1.926198	1.941751	1.922942	1.928760	2.740116

Figure: Benchmarking of colRanges_X_S(), colRanges(X, rows, cols)() and colRanges(X[rows, cols])() on integer+1000x100 data as well as rowRanges_X_S(), rowRanges(X, cols, rows)() and rowRanges(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colRanges_X_S() and rowRanges_X_S() on integer+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	colRanges_X_S	70.667	71.2605	71.97976	71.440	71.6405	96.485
2	rowRanges_X_S	76.880	77.3960	77.84667	77.727	77.9475	83.768

	expr	min	lq	mean	median	uq	max
1	colRanges_X_S	1.000000	1.0000	1.000000	1.000000	1.000000	1.0000000
2	rowRanges_X_S	1.087919	1.0861	1.081508	1.088004	1.088037	0.8681971

Figure: Benchmarking of colRanges_X_S() and rowRanges_X_S() on integer+1000x100 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

Data type "double"

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 = mode)

Results

10x10 double 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 3180757 169.9    5709258 305.0  5709258 305.0
Vcells 6121590  46.8   22343563 170.5 56666022 432.4
> colStats <- microbenchmark(colRanges_X_S = colRanges(X_S, na.rm = FALSE), `colRanges(X, rows, cols)` = colRanges(X, 
+     rows = rows, cols = cols, na.rm = FALSE), `colRanges(X[rows, cols])` = colRanges(X[rows, cols], 
+     na.rm = FALSE), unit = "ms")
> X <- t(X)
> X_S <- t(X_S)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3180739 169.9    5709258 305.0  5709258 305.0
Vcells 6121753  46.8   22343563 170.5 56666022 432.4
> rowStats <- microbenchmark(rowRanges_X_S = rowRanges(X_S, na.rm = FALSE), `rowRanges(X, cols, rows)` = rowRanges(X, 
+     rows = cols, cols = rows, na.rm = FALSE), `rowRanges(X[cols, rows])` = rowRanges(X[cols, rows], 
+     na.rm = FALSE), unit = "ms")

Table: Benchmarking of colRanges_X_S(), colRanges(X, rows, cols)() and colRanges(X[rows, cols])() on double+10x10 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colRanges_X_S	0.001065	0.0010980	0.0012882	0.0011225	0.0011985	0.013261
2	colRanges(X, rows, cols)	0.001233	0.0013100	0.0013996	0.0013340	0.0014160	0.004094
3	colRanges(X[rows, cols])	0.001685	0.0018745	0.0020288	0.0019555	0.0020425	0.006468

	expr	min	lq	mean	median	uq	max
1	colRanges_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	colRanges(X, rows, cols)	1.157747	1.193078	1.086496	1.188419	1.181477	0.3087248
3	colRanges(X[rows, cols])	1.582160	1.707195	1.574964	1.742093	1.704214	0.4877460

Table: Benchmarking of rowRanges_X_S(), rowRanges(X, cols, rows)() and rowRanges(X[cols, rows])() on double+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	rowRanges_X_S	0.001091	0.0011390	0.0012359	0.0011695	0.0012730	0.002975
2	rowRanges(X, cols, rows)	0.001228	0.0012930	0.0015560	0.0013200	0.0014225	0.020242
3	rowRanges(X[cols, rows])	0.001614	0.0017785	0.0019172	0.0018580	0.0019470	0.004203

	expr	min	lq	mean	median	uq	max
1	rowRanges_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowRanges(X, cols, rows)	1.125573	1.135206	1.258993	1.128688	1.117439	6.804034
3	rowRanges(X[cols, rows])	1.479377	1.561457	1.551240	1.588713	1.529458	1.412773

Figure: Benchmarking of colRanges_X_S(), colRanges(X, rows, cols)() and colRanges(X[rows, cols])() on double+10x10 data as well as rowRanges_X_S(), rowRanges(X, cols, rows)() and rowRanges(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colRanges_X_S() and rowRanges_X_S() on double+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	colRanges_X_S	1.065	1.098	1.28815	1.1225	1.1985	13.261
2	rowRanges_X_S	1.091	1.139	1.23594	1.1695	1.2730	2.975

	expr	min	lq	mean	median	uq	max
1	colRanges_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	rowRanges_X_S	1.024413	1.037341	0.959469	1.041871	1.062161	0.2243421

Figure: Benchmarking of colRanges_X_S() and rowRanges_X_S() on double+10x10 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

100x100 double 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 3180958 169.9    5709258 305.0  5709258 305.0
Vcells 6127545  46.8   22343563 170.5 56666022 432.4
> colStats <- microbenchmark(colRanges_X_S = colRanges(X_S, na.rm = FALSE), `colRanges(X, rows, cols)` = colRanges(X, 
+     rows = rows, cols = cols, na.rm = FALSE), `colRanges(X[rows, cols])` = colRanges(X[rows, cols], 
+     na.rm = FALSE), unit = "ms")
> X <- t(X)
> X_S <- t(X_S)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3180949 169.9    5709258 305.0  5709258 305.0
Vcells 6137623  46.9   22343563 170.5 56666022 432.4
> rowStats <- microbenchmark(rowRanges_X_S = rowRanges(X_S, na.rm = FALSE), `rowRanges(X, cols, rows)` = rowRanges(X, 
+     rows = cols, cols = rows, na.rm = FALSE), `rowRanges(X[cols, rows])` = rowRanges(X[cols, rows], 
+     na.rm = FALSE), unit = "ms")

Table: Benchmarking of colRanges_X_S(), colRanges(X, rows, cols)() and colRanges(X[rows, cols])() on double+100x100 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colRanges_X_S	0.008210	0.0085490	0.0087703	0.0087240	0.0088610	0.013393
2	colRanges(X, rows, cols)	0.011324	0.0117125	0.0122276	0.0118640	0.0122115	0.029017
3	colRanges(X[rows, cols])	0.023852	0.0243985	0.0249778	0.0245675	0.0247210	0.058056

	expr	min	lq	mean	median	uq	max
1	colRanges_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	colRanges(X, rows, cols)	1.379293	1.370043	1.394198	1.359927	1.378118	2.166580
3	colRanges(X[rows, cols])	2.905238	2.853960	2.847980	2.816082	2.789866	4.334802

Table: Benchmarking of rowRanges_X_S(), rowRanges(X, cols, rows)() and rowRanges(X[cols, rows])() on double+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	rowRanges_X_S	0.011820	0.0122955	0.0125662	0.012508	0.0126795	0.016145
2	rowRanges(X, cols, rows)	0.011972	0.0125130	0.0129063	0.012770	0.0129870	0.017451
3	rowRanges(X[cols, rows])	0.019413	0.0199120	0.0208002	0.020126	0.0203820	0.080707

	expr	min	lq	mean	median	uq	max
1	rowRanges_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowRanges(X, cols, rows)	1.012860	1.017689	1.027063	1.020947	1.024252	1.080892
3	rowRanges(X[cols, rows])	1.642386	1.619454	1.655252	1.609050	1.607477	4.998885

Figure: Benchmarking of colRanges_X_S(), colRanges(X, rows, cols)() and colRanges(X[rows, cols])() on double+100x100 data as well as rowRanges_X_S(), rowRanges(X, cols, rows)() and rowRanges(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colRanges_X_S() and rowRanges_X_S() on double+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	colRanges_X_S	8.21	8.5490	8.77034	8.724	8.8610	13.393
2	rowRanges_X_S	11.82	12.2955	12.56618	12.508	12.6795	16.145

	expr	min	lq	mean	median	uq	max
1	colRanges_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.00000
2	rowRanges_X_S	1.439708	1.438238	1.432804	1.433746	1.430933	1.20548

Figure: Benchmarking of colRanges_X_S() and rowRanges_X_S() on double+100x100 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

1000x10 double 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 3181168 169.9    5709258 305.0  5709258 305.0
Vcells 6128962  46.8   22343563 170.5 56666022 432.4
> colStats <- microbenchmark(colRanges_X_S = colRanges(X_S, na.rm = FALSE), `colRanges(X, rows, cols)` = colRanges(X, 
+     rows = rows, cols = cols, na.rm = FALSE), `colRanges(X[rows, cols])` = colRanges(X[rows, cols], 
+     na.rm = FALSE), unit = "ms")
> X <- t(X)
> X_S <- t(X_S)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3181159 169.9    5709258 305.0  5709258 305.0
Vcells 6139040  46.9   22343563 170.5 56666022 432.4
> rowStats <- microbenchmark(rowRanges_X_S = rowRanges(X_S, na.rm = FALSE), `rowRanges(X, cols, rows)` = rowRanges(X, 
+     rows = cols, cols = rows, na.rm = FALSE), `rowRanges(X[cols, rows])` = rowRanges(X[cols, rows], 
+     na.rm = FALSE), unit = "ms")

Table: Benchmarking of colRanges_X_S(), colRanges(X, rows, cols)() and colRanges(X[rows, cols])() on double+1000x10 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colRanges_X_S	0.006826	0.006998	0.0071366	0.0070695	0.0071950	0.010718
2	colRanges(X, rows, cols)	0.009641	0.009910	0.0102917	0.0100005	0.0101285	0.035653
3	colRanges(X[rows, cols])	0.022677	0.022804	0.0229688	0.0229135	0.0229975	0.027748

	expr	min	lq	mean	median	uq	max
1	colRanges_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	colRanges(X, rows, cols)	1.412394	1.416119	1.442101	1.414598	1.407714	3.326460
3	colRanges(X[rows, cols])	3.322151	3.258645	3.218454	3.241177	3.196317	2.588916

Table: Benchmarking of rowRanges_X_S(), rowRanges(X, cols, rows)() and rowRanges(X[cols, rows])() on double+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	rowRanges_X_S	0.010227	0.0103790	0.0107991	0.0104625	0.010551	0.040578
2	rowRanges(X, cols, rows)	0.011592	0.0119025	0.0124504	0.0122210	0.012439	0.038032
3	rowRanges(X[cols, rows])	0.019229	0.0197995	0.0200289	0.0199785	0.020164	0.026432

	expr	min	lq	mean	median	uq	max
1	rowRanges_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	rowRanges(X, cols, rows)	1.133470	1.146787	1.152904	1.168076	1.178940	0.9372566
3	rowRanges(X[cols, rows])	1.880219	1.907650	1.854678	1.909534	1.911099	0.6513875

Figure: Benchmarking of colRanges_X_S(), colRanges(X, rows, cols)() and colRanges(X[rows, cols])() on double+1000x10 data as well as rowRanges_X_S(), rowRanges(X, cols, rows)() and rowRanges(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colRanges_X_S() and rowRanges_X_S() on double+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	colRanges_X_S	6.826	6.998	7.13660	7.0695	7.195	10.718
2	rowRanges_X_S	10.227	10.379	10.79914	10.4625	10.551	40.578

	expr	min	lq	mean	median	uq	max
1	colRanges_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowRanges_X_S	1.498242	1.483138	1.513205	1.479949	1.466435	3.785967

Figure: Benchmarking of colRanges_X_S() and rowRanges_X_S() on double+1000x10 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

10x1000 double 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 3181365 170.0    5709258 305.0  5709258 305.0
Vcells 6129084  46.8   22343563 170.5 56666022 432.4
> colStats <- microbenchmark(colRanges_X_S = colRanges(X_S, na.rm = FALSE), `colRanges(X, rows, cols)` = colRanges(X, 
+     rows = rows, cols = cols, na.rm = FALSE), `colRanges(X[rows, cols])` = colRanges(X[rows, cols], 
+     na.rm = FALSE), unit = "ms")
> X <- t(X)
> X_S <- t(X_S)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3181365 170.0    5709258 305.0  5709258 305.0
Vcells 6139177  46.9   22343563 170.5 56666022 432.4
> rowStats <- microbenchmark(rowRanges_X_S = rowRanges(X_S, na.rm = FALSE), `rowRanges(X, cols, rows)` = rowRanges(X, 
+     rows = cols, cols = rows, na.rm = FALSE), `rowRanges(X[cols, rows])` = rowRanges(X[cols, rows], 
+     na.rm = FALSE), unit = "ms")

Table: Benchmarking of colRanges_X_S(), colRanges(X, rows, cols)() and colRanges(X[rows, cols])() on double+10x1000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colRanges_X_S	0.018690	0.020484	0.0215774	0.0216015	0.0221880	0.031057
2	colRanges(X, rows, cols)	0.025960	0.028222	0.0295472	0.0291165	0.0299860	0.065011
3	colRanges(X[rows, cols])	0.028935	0.030216	0.0311852	0.0312600	0.0319745	0.037561

	expr	min	lq	mean	median	uq	max
1	colRanges_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	colRanges(X, rows, cols)	1.388978	1.377758	1.369363	1.347892	1.351451	2.093280
3	colRanges(X[rows, cols])	1.548154	1.475102	1.445275	1.447122	1.441072	1.209421

Table: Benchmarking of rowRanges_X_S(), rowRanges(X, cols, rows)() and rowRanges(X[cols, rows])() on double+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	rowRanges_X_S	0.021389	0.0226125	0.0231756	0.0230595	0.0232190	0.042653
2	rowRanges(X, cols, rows)	0.022330	0.0242145	0.0249002	0.0248250	0.0254350	0.030853
3	rowRanges(X[cols, rows])	0.030004	0.0308745	0.0313960	0.0311435	0.0314465	0.045963

	expr	min	lq	mean	median	uq	max
1	rowRanges_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	rowRanges(X, cols, rows)	1.043995	1.070846	1.074416	1.076563	1.095439	0.7233489
3	rowRanges(X[cols, rows])	1.402777	1.365373	1.354704	1.350571	1.354343	1.0776030

Figure: Benchmarking of colRanges_X_S(), colRanges(X, rows, cols)() and colRanges(X[rows, cols])() on double+10x1000 data as well as rowRanges_X_S(), rowRanges(X, cols, rows)() and rowRanges(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colRanges_X_S() and rowRanges_X_S() on double+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	colRanges_X_S	18.690	20.4840	21.57735	21.6015	22.188	31.057
2	rowRanges_X_S	21.389	22.6125	23.17556	23.0595	23.219	42.653

	expr	min	lq	mean	median	uq	max
1	colRanges_X_S	1.000000	1.00000	1.000000	1.000000	1.000000	1.000000
2	rowRanges_X_S	1.144409	1.10391	1.074069	1.067495	1.046467	1.373378

Figure: Benchmarking of colRanges_X_S() and rowRanges_X_S() on double+10x1000 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

100x1000 double 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 3181573 170.0    5709258 305.0  5709258 305.0
Vcells 6174555  47.2   22343563 170.5 56666022 432.4
> colStats <- microbenchmark(colRanges_X_S = colRanges(X_S, na.rm = FALSE), `colRanges(X, rows, cols)` = colRanges(X, 
+     rows = rows, cols = cols, na.rm = FALSE), `colRanges(X[rows, cols])` = colRanges(X[rows, cols], 
+     na.rm = FALSE), unit = "ms")
> X <- t(X)
> X_S <- t(X_S)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3181564 170.0    5709258 305.0  5709258 305.0
Vcells 6274633  47.9   22343563 170.5 56666022 432.4
> rowStats <- microbenchmark(rowRanges_X_S = rowRanges(X_S, na.rm = FALSE), `rowRanges(X, cols, rows)` = rowRanges(X, 
+     rows = cols, cols = rows, na.rm = FALSE), `rowRanges(X[cols, rows])` = rowRanges(X[cols, rows], 
+     na.rm = FALSE), unit = "ms")

Table: Benchmarking of colRanges_X_S(), colRanges(X, rows, cols)() and colRanges(X[rows, cols])() on double+100x1000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colRanges_X_S	0.100562	0.1012370	0.1019546	0.101508	0.1019375	0.115033
2	colRanges(X, rows, cols)	0.134218	0.1350955	0.1364043	0.135314	0.1356110	0.233219
3	colRanges(X[rows, cols])	0.248367	0.2496655	0.2543097	0.250348	0.2521670	0.365943

	expr	min	lq	mean	median	uq	max
1	colRanges_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.00000
2	colRanges(X, rows, cols)	1.334679	1.334448	1.337892	1.333038	1.330335	2.02741
3	colRanges(X[rows, cols])	2.469790	2.466149	2.494343	2.466288	2.473741	3.18120

Table: Benchmarking of rowRanges_X_S(), rowRanges(X, cols, rows)() and rowRanges(X[cols, rows])() on double+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	rowRanges_X_S	0.116119	0.116875	0.1177092	0.1171680	0.117585	0.138375
2	rowRanges(X, cols, rows)	0.135232	0.136289	0.1374557	0.1365975	0.137253	0.179292
3	rowRanges(X[cols, rows])	0.186056	0.187095	0.1905034	0.1879500	0.189161	0.304286

	expr	min	lq	mean	median	uq	max
1	rowRanges_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowRanges(X, cols, rows)	1.164598	1.166109	1.167757	1.165826	1.167266	1.295697
3	rowRanges(X[cols, rows])	1.602287	1.600813	1.618424	1.604107	1.608717	2.198996

Figure: Benchmarking of colRanges_X_S(), colRanges(X, rows, cols)() and colRanges(X[rows, cols])() on double+100x1000 data as well as rowRanges_X_S(), rowRanges(X, cols, rows)() and rowRanges(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colRanges_X_S() and rowRanges_X_S() on double+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	colRanges_X_S	100.562	101.237	101.9546	101.508	101.9375	115.033
2	rowRanges_X_S	116.119	116.875	117.7092	117.168	117.5850	138.375

	expr	min	lq	mean	median	uq	max
1	colRanges_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowRanges_X_S	1.154701	1.154469	1.154526	1.154274	1.153501	1.202916

Figure: Benchmarking of colRanges_X_S() and rowRanges_X_S() on double+100x1000 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

1000x100 double 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 3181789 170.0    5709258 305.0  5709258 305.0
Vcells 6174702  47.2   22343563 170.5 56666022 432.4
> colStats <- microbenchmark(colRanges_X_S = colRanges(X_S, na.rm = FALSE), `colRanges(X, rows, cols)` = colRanges(X, 
+     rows = rows, cols = cols, na.rm = FALSE), `colRanges(X[rows, cols])` = colRanges(X[rows, cols], 
+     na.rm = FALSE), unit = "ms")
> X <- t(X)
> X_S <- t(X_S)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3181780 170.0    5709258 305.0  5709258 305.0
Vcells 6274780  47.9   22343563 170.5 56666022 432.4
> rowStats <- microbenchmark(rowRanges_X_S = rowRanges(X_S, na.rm = FALSE), `rowRanges(X, cols, rows)` = rowRanges(X, 
+     rows = cols, cols = rows, na.rm = FALSE), `rowRanges(X[cols, rows])` = rowRanges(X[cols, rows], 
+     na.rm = FALSE), unit = "ms")

Table: Benchmarking of colRanges_X_S(), colRanges(X, rows, cols)() and colRanges(X[rows, cols])() on double+1000x100 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colRanges_X_S	0.060877	0.0611645	0.0616153	0.061334	0.0616005	0.070619
2	colRanges(X, rows, cols)	0.085854	0.0863455	0.0866055	0.086484	0.0866800	0.090331
3	colRanges(X[rows, cols])	0.130192	0.1312110	0.1345383	0.131964	0.1327190	0.265479

	expr	min	lq	mean	median	uq	max
1	colRanges_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	colRanges(X, rows, cols)	1.410286	1.411693	1.405585	1.410050	1.407131	1.279132
3	colRanges(X[rows, cols])	2.138607	2.145215	2.183523	2.151564	2.154512	3.759314

Table: Benchmarking of rowRanges_X_S(), rowRanges(X, cols, rows)() and rowRanges(X[cols, rows])() on double+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	rowRanges_X_S	0.089140	0.0896180	0.0900077	0.0898030	0.0900340	0.095609
2	rowRanges(X, cols, rows)	0.106737	0.1072765	0.1078168	0.1076055	0.1078565	0.118149
3	rowRanges(X[cols, rows])	0.163829	0.1649710	0.1681752	0.1655475	0.1667735	0.306344

	expr	min	lq	mean	median	uq	max
1	rowRanges_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowRanges(X, cols, rows)	1.197409	1.197042	1.197862	1.198239	1.197953	1.235752
3	rowRanges(X[cols, rows])	1.837884	1.840824	1.868453	1.843452	1.852339	3.204134

Figure: Benchmarking of colRanges_X_S(), colRanges(X, rows, cols)() and colRanges(X[rows, cols])() on double+1000x100 data as well as rowRanges_X_S(), rowRanges(X, cols, rows)() and rowRanges(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colRanges_X_S() and rowRanges_X_S() on double+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	colRanges_X_S	60.877	61.1645	61.61526	61.334	61.6005	70.619
2	rowRanges_X_S	89.140	89.6180	90.00770	89.803	90.0340	95.609

	expr	min	lq	mean	median	uq	max
1	colRanges_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowRanges_X_S	1.464264	1.465196	1.460802	1.464163	1.461579	1.353871

Figure: Benchmarking of colRanges_X_S() and rowRanges_X_S() on double+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 23.23 secs.

Reproducibility

To reproduce this report, do:

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

---

Copyright Dongcan Jiang. Last updated on 2019-09-10 20:51:03 (-0700 UTC). Powered by RSP.

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