matrixStats.benchmarks

---

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  5286540 282.4    7916910 422.9  7916910 422.9
Vcells 10399958  79.4   33191153 253.3 53339345 407.0
> 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  5286494 282.4    7916910 422.9  7916910 422.9
Vcells 10400187  79.4   33191153 253.3 53339345 407.0
> 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	0.002429	0.0025230	0.0045352	0.0025705	0.0026750	0.192259
2	colRanges(X, rows, cols)	0.002760	0.0029060	0.0030436	0.0029765	0.0030885	0.005978
3	colRanges(X[rows, cols])	0.003300	0.0035505	0.0037769	0.0036605	0.0038130	0.008942

	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)	1.136270	1.151803	0.6711163	1.157946	1.154579	0.0310935
3	colRanges(X[rows, cols])	1.358584	1.407253	0.8327964	1.424042	1.425421	0.0465102

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	0.002432	0.0025395	0.0028104	0.0026275	0.0028200	0.005653
2	rowRanges(X, cols, rows)	0.002707	0.0028760	0.0050589	0.0029800	0.0031490	0.198719
3	rowRanges(X[cols, rows])	0.003381	0.0036620	0.0041524	0.0038220	0.0041005	0.009042

	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)	1.113076	1.132506	1.800041	1.134158	1.116667	35.152839
3	rowRanges(X[cols, rows])	1.390214	1.442016	1.477482	1.454615	1.454078	1.599505

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
1	colRanges_X_S	2.429	2.5230	4.53519	2.5705	2.675	192.259
2	rowRanges_X_S	2.432	2.5395	2.81043	2.6275	2.820	5.653

	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	1.001235	1.00654	0.619694	1.022175	1.054206	0.029403

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  5285199 282.3    7916910 422.9  7916910 422.9
Vcells 10069267  76.9   33191153 253.3 53339345 407.0
> 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  5285193 282.3    7916910 422.9  7916910 422.9
Vcells 10074350  76.9   33191153 253.3 53339345 407.0
> 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	0.018263	0.0191235	0.0199667	0.0195985	0.0201090	0.032874
2	colRanges(X, rows, cols)	0.024193	0.0250990	0.0262194	0.0258730	0.0264135	0.036460
3	colRanges(X[rows, cols])	0.028780	0.0298775	0.0324142	0.0306310	0.0317825	0.119464

	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)	1.324700	1.312469	1.313156	1.320152	1.313516	1.109083
3	colRanges(X[rows, cols])	1.575864	1.562345	1.623414	1.562926	1.580511	3.633996

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
2	rowRanges(X, cols, rows)	0.018723	0.0197620	0.0210402	0.0205205	0.0212820	0.053944
1	rowRanges_X_S	0.018643	0.0197330	0.0206572	0.0205820	0.0212820	0.028640
3	rowRanges(X[cols, rows])	0.029805	0.0306285	0.0321890	0.0315595	0.0329135	0.046549

	expr	min	lq	mean	median	uq	max
2	rowRanges(X, cols, rows)	1.0000000	1.0000000	1.0000000	1.000000	1.000000	1.0000000
1	rowRanges_X_S	0.9957272	0.9985325	0.9817963	1.002997	1.000000	0.5309210
3	rowRanges(X[cols, rows])	1.5918923	1.5498684	1.5298830	1.537950	1.546542	0.8629134

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
1	colRanges_X_S	18.263	19.1235	19.96671	19.5985	20.109	32.874
2	rowRanges_X_S	18.643	19.7330	20.65718	20.5820	21.282	28.640

	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	1.020807	1.031872	1.034581	1.050182	1.058332	0.8712052

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  5285942 282.3    7916910 422.9  7916910 422.9
Vcells 10073307  76.9   33191153 253.3 53339345 407.0
> 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  5285936 282.3    7916910 422.9  7916910 422.9
Vcells 10078390  76.9   33191153 253.3 53339345 407.0
> 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	0.014292	0.0148750	0.0152153	0.0150390	0.0152155	0.029705
2	colRanges(X, rows, cols)	0.020695	0.0214260	0.0217751	0.0218355	0.0220275	0.026319
3	colRanges(X[rows, cols])	0.025865	0.0264345	0.0275296	0.0272225	0.0274980	0.057222

	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)	1.448013	1.440403	1.431134	1.451925	1.447701	0.8860125
3	colRanges(X[rows, cols])	1.809754	1.777109	1.809345	1.810127	1.807236	1.9263424

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	0.016498	0.0170325	0.0187920	0.0171910	0.0174425	0.038112
2	rowRanges(X, cols, rows)	0.019760	0.0203165	0.0215158	0.0204675	0.0207180	0.041567
3	rowRanges(X[cols, rows])	0.030434	0.0309375	0.0334244	0.0312260	0.0317680	0.065033

	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)	1.197721	1.192808	1.144944	1.190594	1.187788	1.090654
3	rowRanges(X[cols, rows])	1.844708	1.816380	1.778650	1.816416	1.821299	1.706365

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	14.292	14.8750	15.21526	15.039	15.2155	29.705
2	rowRanges_X_S	16.498	17.0325	18.79199	17.191	17.4425	38.112

	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	1.154352	1.145042	1.235075	1.143095	1.146364	1.283016

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  5286147 282.4    7916910 422.9  7916910 422.9
Vcells 10074184  76.9   33191153 253.3 53339345 407.0
> 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  5286141 282.4    7916910 422.9  7916910 422.9
Vcells 10079267  76.9   33191153 253.3 53339345 407.0
> 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	0.036287	0.0399420	0.0417527	0.0414315	0.0427125	0.069458
2	colRanges(X, rows, cols)	0.046975	0.0496465	0.0530047	0.0525550	0.0560960	0.072611
3	colRanges(X[rows, cols])	0.048037	0.0532930	0.0559516	0.0557955	0.0575620	0.070806

	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)	1.294541	1.242965	1.269492	1.268479	1.313339	1.045394
3	colRanges(X[rows, cols])	1.323807	1.334260	1.340071	1.346693	1.347662	1.019407

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	0.033575	0.0362075	0.0379317	0.0375405	0.0388425	0.063951
2	rowRanges(X, cols, rows)	0.030558	0.0362890	0.0390079	0.0385115	0.0403125	0.084435
3	rowRanges(X[cols, rows])	0.043924	0.0468115	0.0501583	0.0489815	0.0516125	0.077777

	expr	min	lq	mean	median	uq	max
1	rowRanges_X_S	1.0000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowRanges(X, cols, rows)	0.9101415	1.002251	1.028371	1.025865	1.037845	1.320308
3	rowRanges(X[cols, rows])	1.3082353	1.292868	1.322332	1.304764	1.328764	1.216197

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	33.575	36.2075	37.93173	37.5405	38.8425	63.951
1	colRanges_X_S	36.287	39.9420	41.75269	41.4315	42.7125	69.458

	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	1.080774	1.103142	1.100733	1.103648	1.099633	1.086113

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  5286357 282.4    7916910 422.9  7916910 422.9
Vcells 10096860  77.1   33191153 253.3 53339345 407.0
> 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  5286351 282.4    7916910 422.9  7916910 422.9
Vcells 10146943  77.5   33191153 253.3 53339345 407.0
> 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	0.121061	0.1310110	0.1499712	0.1450570	0.1636735	0.218091
2	colRanges(X, rows, cols)	0.163439	0.1770130	0.2042014	0.2006975	0.2224445	0.356483
3	colRanges(X[rows, cols])	0.189031	0.2068755	0.2365141	0.2336550	0.2556970	0.324776

	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)	1.350055	1.351131	1.361604	1.383577	1.359075	1.634561
3	colRanges(X[rows, cols])	1.561452	1.579070	1.577063	1.610781	1.562238	1.489176

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
2	rowRanges(X, cols, rows)	0.135684	0.1475800	0.1689758	0.1584085	0.1880520	0.277803
1	rowRanges_X_S	0.135035	0.1561050	0.1710458	0.1665050	0.1901745	0.247119
3	rowRanges(X[cols, rows])	0.203372	0.2347565	0.2555773	0.2445805	0.2842865	0.351589

	expr	min	lq	mean	median	uq	max
2	rowRanges(X, cols, rows)	1.0000000	1.000000	1.000000	1.000000	1.000000	1.0000000
1	rowRanges_X_S	0.9952168	1.057765	1.012250	1.051111	1.011287	0.8895476
3	rowRanges(X[cols, rows])	1.4988650	1.590707	1.512508	1.543986	1.511744	1.2656055

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
1	colRanges_X_S	121.061	131.011	149.9712	145.057	163.6735	218.091
2	rowRanges_X_S	135.035	156.105	171.0458	166.505	190.1745	247.119

	expr	min	lq	mean	median	uq	max
1	colRanges_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000
2	rowRanges_X_S	1.115429	1.191541	1.140524	1.147859	1.161914	1.1331

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  5286570 282.4    7916910 422.9  7916910 422.9
Vcells 10097649  77.1   33191153 253.3 53339345 407.0
> 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  5286564 282.4    7916910 422.9  7916910 422.9
Vcells 10147732  77.5   33191153 253.3 53339345 407.0
> 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	0.087504	0.0968285	0.1052984	0.1020730	0.1139185	0.166509
2	colRanges(X, rows, cols)	0.124524	0.1404510	0.1518258	0.1456435	0.1648415	0.196296
3	colRanges(X[rows, cols])	0.158885	0.1747185	0.1949558	0.1863845	0.2092925	0.341581

	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)	1.423066	1.450513	1.441863	1.426856	1.447013	1.178891
3	colRanges(X[rows, cols])	1.815746	1.804412	1.851461	1.825992	1.837213	2.051427

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	0.107949	0.1166520	0.1290034	0.120817	0.1381405	0.231508
2	rowRanges(X, cols, rows)	0.112029	0.1226735	0.1361048	0.127053	0.1439360	0.252502
3	rowRanges(X[cols, rows])	0.187629	0.2013710	0.2265817	0.213288	0.2416135	0.413159

	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)	1.037796	1.051619	1.055048	1.051615	1.041954	1.090684
3	rowRanges(X[cols, rows])	1.738126	1.726254	1.756401	1.765381	1.749042	1.784642

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	87.504	96.8285	105.2983	102.073	113.9185	166.509
2	rowRanges_X_S	107.949	116.6520	129.0034	120.817	138.1405	231.508

	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	1.233647	1.204728	1.225123	1.183633	1.212626	1.390363

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  5286787 282.4    7916910 422.9  7916910 422.9
Vcells 10188761  77.8   33191153 253.3 53339345 407.0
> 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  5286772 282.4    7916910 422.9  7916910 422.9
Vcells 10188929  77.8   33191153 253.3 53339345 407.0
> 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	0.002648	0.0027175	0.0030374	0.0027695	0.0028870	0.023938
2	colRanges(X, rows, cols)	0.002846	0.0029985	0.0031765	0.0030745	0.0031780	0.008000
3	colRanges(X[rows, cols])	0.003630	0.0038905	0.0040578	0.0039640	0.0040485	0.009945

	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)	1.074773	1.103404	1.045779	1.110128	1.100797	0.3341967
3	colRanges(X[rows, cols])	1.370846	1.431647	1.335921	1.431305	1.402321	0.4154482

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	0.002416	0.0025295	0.0028057	0.0026555	0.002944	0.006265
2	rowRanges(X, cols, rows)	0.002661	0.0028575	0.0035743	0.0030600	0.003388	0.037900
3	rowRanges(X[cols, rows])	0.003379	0.0037035	0.0042449	0.0039990	0.004406	0.008822

	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)	1.101407	1.129670	1.273917	1.152325	1.150815	6.049481
3	rowRanges(X[cols, rows])	1.398593	1.464123	1.512934	1.505931	1.496603	1.408141

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
2	rowRanges_X_S	2.416	2.5295	2.80574	2.6555	2.944	6.265
1	colRanges_X_S	2.648	2.7175	3.03744	2.7695	2.887	23.938

	expr	min	lq	mean	median	uq	max
2	rowRanges_X_S	1.000000	1.000000	1.000000	1.00000	1.0000000	1.00000
1	colRanges_X_S	1.096027	1.074323	1.082581	1.04293	0.9806386	3.82091

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  5286984 282.4    7916910 422.9  7916910 422.9
Vcells 10194718  77.8   33191153 253.3 53339345 407.0
> 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  5286978 282.4    7916910 422.9  7916910 422.9
Vcells 10204801  77.9   33191153 253.3 53339345 407.0
> 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	0.014804	0.0156435	0.0164760	0.0160425	0.0165285	0.031084
2	colRanges(X, rows, cols)	0.022258	0.0235135	0.0245857	0.0241055	0.0248250	0.036272
3	colRanges(X[rows, cols])	0.030014	0.0309435	0.0326331	0.0320050	0.0331350	0.071922

	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)	1.503513	1.503084	1.492214	1.502603	1.501951	1.166903
3	colRanges(X[rows, cols])	2.027425	1.978042	1.980643	1.995013	2.004719	2.313795

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	0.019971	0.0207755	0.0218117	0.0213895	0.0221455	0.036894
2	rowRanges(X, cols, rows)	0.022185	0.0230650	0.0244217	0.0236735	0.0242810	0.059576
3	rowRanges(X[cols, rows])	0.034940	0.0355005	0.0368289	0.0366905	0.0371045	0.050756

	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)	1.110861	1.110202	1.119659	1.106781	1.096430	1.614788
3	rowRanges(X[cols, rows])	1.749537	1.708767	1.688492	1.715351	1.675487	1.375725

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	14.804	15.6435	16.47601	16.0425	16.5285	31.084
2	rowRanges_X_S	19.971	20.7755	21.81173	21.3895	22.1455	36.894

	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	1.349027	1.32806	1.323848	1.333302	1.339837	1.186913

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  5287184 282.4    7916910 422.9  7916910 422.9
Vcells 10196139  77.8   33191153 253.3 53339345 407.0
> 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  5287178 282.4    7916910 422.9  7916910 422.9
Vcells 10206222  77.9   33191153 253.3 53339345 407.0
> 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	0.012846	0.013468	0.0140996	0.0140190	0.0147300	0.017321
2	colRanges(X, rows, cols)	0.019908	0.020758	0.0213048	0.0211275	0.0216505	0.027327
3	colRanges(X[rows, cols])	0.028491	0.029055	0.0309083	0.0299675	0.0311800	0.063680

	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)	1.549743	1.541283	1.511024	1.507062	1.469823	1.577680
3	colRanges(X[rows, cols])	2.217889	2.157336	2.192144	2.137635	2.116769	3.676462

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	0.017698	0.0184705	0.0191147	0.0188405	0.0195745	0.033716
2	rowRanges(X, cols, rows)	0.022616	0.0232655	0.0244184	0.0244065	0.0250650	0.039391
3	rowRanges(X[cols, rows])	0.036317	0.0370085	0.0396849	0.0381090	0.0396870	0.087991

	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)	1.277885	1.259603	1.277463	1.295427	1.280493	1.168318
3	rowRanges(X[cols, rows])	2.052040	2.003655	2.076142	2.022717	2.027485	2.609770

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	12.846	13.4680	14.09959	14.0190	14.7300	17.321
2	rowRanges_X_S	17.698	18.4705	19.11473	18.8405	19.5745	33.716

	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	1.377705	1.371436	1.355694	1.343926	1.328887	1.946539

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  5287389 282.4    7916910 422.9  7916910 422.9
Vcells 10196275  77.8   33191153 253.3 53339345 407.0
> 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  5287383 282.4    7916910 422.9  7916910 422.9
Vcells 10206358  77.9   33191153 253.3 53339345 407.0
> 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	0.028704	0.032903	0.0355011	0.0346615	0.036657	0.069500
2	colRanges(X, rows, cols)	0.042456	0.046587	0.0500407	0.0493735	0.052615	0.078863
3	colRanges(X[rows, cols])	0.045626	0.051482	0.0544494	0.0536330	0.056314	0.074838

	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)	1.479097	1.415889	1.409553	1.424448	1.435333	1.134719
3	colRanges(X[rows, cols])	1.589535	1.564660	1.533740	1.547336	1.536241	1.076806

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	0.036027	0.0380020	0.0407160	0.0401405	0.0422205	0.059171
2	rowRanges(X, cols, rows)	0.035582	0.0408245	0.0437152	0.0423745	0.0451050	0.090804
3	rowRanges(X[cols, rows])	0.050537	0.0533520	0.0569606	0.0566550	0.0593285	0.077522

	expr	min	lq	mean	median	uq	max
1	rowRanges_X_S	1.0000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowRanges(X, cols, rows)	0.9876482	1.074272	1.073662	1.055654	1.068320	1.534603
3	rowRanges(X[cols, rows])	1.4027535	1.403926	1.398972	1.411417	1.405206	1.310135

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	28.704	32.903	35.50110	34.6615	36.6570	69.500
2	rowRanges_X_S	36.027	38.002	40.71601	40.1405	42.2205	59.171

	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	1.255121	1.154971	1.146894	1.158072	1.151772	0.8513813

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  5287599 282.4    7916910 422.9  7916910 422.9
Vcells 10241747  78.2   33191153 253.3 53339345 407.0
> 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  5287593 282.4    7916910 422.9  7916910 422.9
Vcells 10341830  79.0   33191153 253.3 53339345 407.0
> 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	0.115716	0.119494	0.1377585	0.133537	0.1482535	0.228903
2	colRanges(X, rows, cols)	0.167278	0.172464	0.1981186	0.192518	0.2179425	0.342660
3	colRanges(X[rows, cols])	0.211085	0.219519	0.2561519	0.251808	0.2778925	0.374834

	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)	1.445591	1.443286	1.438158	1.441683	1.470066	1.496966
3	colRanges(X[rows, cols])	1.824164	1.837071	1.859426	1.885680	1.874441	1.637523

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	0.141148	0.1544995	0.1772561	0.1762555	0.1918750	0.260212
2	rowRanges(X, cols, rows)	0.168059	0.1731190	0.2013434	0.1937780	0.2198775	0.360953
3	rowRanges(X[cols, rows])	0.235521	0.2544455	0.2918780	0.2847835	0.3122105	0.399382

	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)	1.190658	1.120515	1.135890	1.099415	1.145941	1.387150
3	rowRanges(X[cols, rows])	1.668610	1.646902	1.646646	1.615743	1.627156	1.534833

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	115.716	119.4940	137.7585	133.5370	148.2535	228.903
2	rowRanges_X_S	141.148	154.4995	177.2561	176.2555	191.8750	260.212

	expr	min	lq	mean	median	uq	max
1	colRanges_X_S	1.00000	1.000000	1.000000	1.0000	1.000000	1.000000
2	rowRanges_X_S	1.21978	1.292948	1.286716	1.3199	1.294236	1.136778

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  5287812 282.4    7916910 422.9  7916910 422.9
Vcells 10241891  78.2   33191153 253.3 53339345 407.0
> 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  5287806 282.4    7916910 422.9  7916910 422.9
Vcells 10341974  79.0   33191153 253.3 53339345 407.0
> 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	0.078203	0.0857650	0.0937409	0.0912830	0.0977130	0.154135
2	colRanges(X, rows, cols)	0.122774	0.1380060	0.1483435	0.1431605	0.1530525	0.211057
3	colRanges(X[rows, cols])	0.178420	0.1920585	0.2161007	0.2146410	0.2310360	0.385859

	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)	1.569940	1.609118	1.582484	1.568315	1.566347	1.369300
3	colRanges(X[rows, cols])	2.281498	2.239358	2.305297	2.351380	2.364435	2.503383

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	0.115917	0.1219150	0.1560951	0.1353270	0.150800	0.334374
2	rowRanges(X, cols, rows)	0.140326	0.1512790	0.1886024	0.1645535	0.182025	0.512825
3	rowRanges(X[cols, rows])	0.218378	0.2341655	0.2826284	0.2562180	0.281038	0.565039

	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)	1.210573	1.240856	1.208254	1.215969	1.207062	1.533687
3	rowRanges(X[cols, rows])	1.883917	1.920728	1.810617	1.893325	1.863647	1.689841

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	78.203	85.765	93.74093	91.283	97.713	154.135
2	rowRanges_X_S	115.917	121.915	156.09508	135.327	150.800	334.374

	expr	min	lq	mean	median	uq	max
1	colRanges_X_S	1.000000	1.000000	1.000000	1.0000	1.000000	1.000000
2	rowRanges_X_S	1.482258	1.421501	1.665175	1.4825	1.543295	2.169358

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 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 24.33 secs.

Reproducibility

To reproduce this report, do:

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

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Copyright Dongcan Jiang. Last updated on 2021-08-25 22:27:19 (+0200 UTC). Powered by RSP.