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  5284711 282.3    8529671 455.6  8529671 455.6
Vcells 10380208  79.2   31876688 243.2 60562128 462.1
> 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  5275333 281.8    8529671 455.6  8529671 455.6
Vcells 10349535  79.0   31876688 243.2 60562128 462.1
> 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	0.001961	0.0020475	0.0030051	0.0020880	0.0021865	0.088867
2	colRanges(X, rows, cols)	0.002340	0.0024575	0.0025985	0.0025365	0.0026355	0.005529
3	colRanges(X[rows, cols])	0.002814	0.0029880	0.0032172	0.0031035	0.0032710	0.007759

	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)	1.193269	1.200244	0.8646856	1.214799	1.205351	0.0622166
3	colRanges(X[rows, cols])	1.434982	1.459341	1.0705655	1.486351	1.495998	0.0873103

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	0.002009	0.0021170	0.0022792	0.0021795	0.0023175	0.005540
2	rowRanges(X, cols, rows)	0.002383	0.0024685	0.0036978	0.0025320	0.0026815	0.112314
3	rowRanges(X[cols, rows])	0.002881	0.0030920	0.0034571	0.0032020	0.0033840	0.015251

	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)	1.186162	1.166037	1.622394	1.161734	1.157066	20.273285
3	rowRanges(X[cols, rows])	1.434047	1.460557	1.516809	1.469144	1.460194	2.752888

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	1.961	2.0475	3.00515	2.0880	2.1865	88.867
2	rowRanges_X_S	2.009	2.1170	2.27920	2.1795	2.3175	5.540

	expr	min	lq	mean	median	uq	max
1	colRanges_X_S	1.000000	1.000000	1.0000000	1.000000	1.000000	1.0000000
2	rowRanges_X_S	1.024477	1.033944	0.7584314	1.043822	1.059913	0.0623404

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  5273952 281.7    8529671 455.6  8529671 455.6
Vcells 10018404  76.5   31876688 243.2 60562128 462.1
> 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  5273928 281.7    8529671 455.6  8529671 455.6
Vcells 10023457  76.5   31876688 243.2 60562128 462.1
> 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	0.019220	0.0202785	0.0209938	0.0209020	0.021548	0.028393
2	colRanges(X, rows, cols)	0.022091	0.0230705	0.0240528	0.0237480	0.024272	0.036341
3	colRanges(X[rows, cols])	0.029285	0.0308255	0.0328113	0.0317855	0.033014	0.076312

	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)	1.149376	1.137683	1.145712	1.136159	1.126415	1.279928
3	colRanges(X[rows, cols])	1.523673	1.520107	1.562908	1.520692	1.532114	2.687705

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)	0.019247	0.0199475	0.0212810	0.0208235	0.0217910	0.047923
1	rowRanges_X_S	0.019995	0.0208815	0.0218721	0.0219785	0.0226165	0.028548
3	rowRanges(X[cols, rows])	0.030854	0.0314480	0.0333112	0.0327595	0.0342245	0.053218

	expr	min	lq	mean	median	uq	max
2	rowRanges(X, cols, rows)	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
1	rowRanges_X_S	1.038863	1.046823	1.027775	1.055466	1.037883	0.5957056
3	rowRanges(X[cols, rows])	1.603055	1.576538	1.565302	1.573198	1.570580	1.1104897

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	19.220	20.2785	20.99377	20.9020	21.5480	28.393
2	rowRanges_X_S	19.995	20.8815	21.87208	21.9785	22.6165	28.548

	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	1.040323	1.029736	1.041837	1.051502	1.049587	1.005459

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  5274695 281.7    8529671 455.6  8529671 455.6
Vcells 10022443  76.5   31876688 243.2 60562128 462.1
> 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  5274671 281.7    8529671 455.6  8529671 455.6
Vcells 10027496  76.6   31876688 243.2 60562128 462.1
> 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	0.014498	0.0148325	0.0151943	0.0152545	0.0153600	0.017962
2	colRanges(X, rows, cols)	0.019393	0.0199415	0.0206006	0.0204100	0.0207565	0.034746
3	colRanges(X[rows, cols])	0.025697	0.0261810	0.0272128	0.0268035	0.0273605	0.055022

	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)	1.337633	1.344446	1.355816	1.337966	1.351335	1.934417
3	colRanges(X[rows, cols])	1.772451	1.765110	1.790989	1.757088	1.781283	3.063245

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	0.019007	0.0198945	0.0206969	0.0206480	0.0209895	0.035138
2	rowRanges(X, cols, rows)	0.019214	0.0201775	0.0209782	0.0209150	0.0213425	0.026627
3	rowRanges(X[cols, rows])	0.031743	0.0332035	0.0346958	0.0346355	0.0350590	0.069478

	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)	1.010891	1.014225	1.013588	1.012931	1.016818	0.7577836
3	rowRanges(X[cols, rows])	1.670069	1.668979	1.676372	1.677426	1.670311	1.9772895

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	14.498	14.8325	15.19428	15.2545	15.3600	17.962
2	rowRanges_X_S	19.007	19.8945	20.69693	20.6480	20.9895	35.138

	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	1.311008	1.341278	1.362153	1.353568	1.366504	1.956241

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  5274900 281.8    8529671 455.6  8529671 455.6
Vcells 10023320  76.5   31876688 243.2 60562128 462.1
> 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  5274876 281.8    8529671 455.6  8529671 455.6
Vcells 10028373  76.6   31876688 243.2 60562128 462.1
> 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	0.036023	0.0397245	0.0420837	0.0417455	0.0434585	0.067160
2	colRanges(X, rows, cols)	0.042470	0.0468715	0.0502354	0.0495945	0.0531000	0.073264
3	colRanges(X[rows, cols])	0.048893	0.0530160	0.0553122	0.0554755	0.0571870	0.070152

	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)	1.178969	1.179914	1.193703	1.188020	1.221855	1.090887
3	colRanges(X[rows, cols])	1.357272	1.334592	1.314339	1.328898	1.315899	1.044550

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	0.033585	0.0362560	0.0377057	0.0372760	0.0386595	0.053485
2	rowRanges(X, cols, rows)	0.032384	0.0378620	0.0401970	0.0399965	0.0419260	0.068506
3	rowRanges(X[cols, rows])	0.044299	0.0469715	0.0491028	0.0489445	0.0509770	0.061887

	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)	0.964240	1.044296	1.066075	1.072983	1.084494	1.280845
3	rowRanges(X[cols, rows])	1.319012	1.295551	1.302267	1.313030	1.318615	1.157091

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	33.585	36.2560	37.70566	37.2760	38.6595	53.485
1	colRanges_X_S	36.023	39.7245	42.08370	41.7455	43.4585	67.160

	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	1.072592	1.095667	1.116111	1.119903	1.124135	1.255679

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  5275110 281.8    8529671 455.6  8529671 455.6
Vcells 10045996  76.7   31876688 243.2 60562128 462.1
> 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  5275086 281.8    8529671 455.6  8529671 455.6
Vcells 10096049  77.1   31876688 243.2 60562128 462.1
> 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	0.130499	0.1368120	0.1611784	0.1553205	0.1767130	0.252218
2	colRanges(X, rows, cols)	0.147868	0.1536695	0.1785957	0.1747015	0.1988680	0.299349
3	colRanges(X[rows, cols])	0.201111	0.2122050	0.2486858	0.2401130	0.2693935	0.368923

	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)	1.133097	1.123217	1.108062	1.124781	1.125373	1.186866
3	colRanges(X[rows, cols])	1.541092	1.551070	1.542923	1.545920	1.524469	1.462715

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)	0.121502	0.1296910	0.1472742	0.1401625	0.1594755	0.250781
1	rowRanges_X_S	0.126744	0.1407030	0.1592755	0.1589260	0.1697035	0.227490
3	rowRanges(X[cols, rows])	0.189912	0.2061865	0.2352388	0.2257905	0.2542060	0.319791

	expr	min	lq	mean	median	uq	max
2	rowRanges(X, cols, rows)	1.000000	1.000000	1.000000	1.00000	1.000000	1.0000000
1	rowRanges_X_S	1.043143	1.084909	1.081489	1.13387	1.064135	0.9071261
3	rowRanges(X[cols, rows])	1.563036	1.589829	1.597284	1.61092	1.594013	1.2751803

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	130.499	136.812	161.1784	155.3205	176.7130	252.218
2	rowRanges_X_S	126.744	140.703	159.2755	158.9260	169.7035	227.490

	expr	min	lq	mean	median	uq	max
1	colRanges_X_S	1.0000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	rowRanges_X_S	0.9712258	1.028441	0.988194	1.023213	0.960334	0.9019578

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  5275323 281.8    8529671 455.6  8529671 455.6
Vcells 10046784  76.7   31876688 243.2 60562128 462.1
> 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  5275299 281.8    8529671 455.6  8529671 455.6
Vcells 10096837  77.1   31876688 243.2 60562128 462.1
> 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	0.091087	0.105285	0.1180325	0.1127475	0.1205720	0.287098
2	colRanges(X, rows, cols)	0.105857	0.122238	0.1351061	0.1304855	0.1461225	0.209747
3	colRanges(X[rows, cols])	0.160686	0.184816	0.2075819	0.2014940	0.2322715	0.322001

	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)	1.162153	1.161020	1.144651	1.157325	1.211911	0.7305763
3	colRanges(X[rows, cols])	1.764094	1.755388	1.758684	1.787126	1.926413	1.1215717

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
2	rowRanges(X, cols, rows)	0.098893	0.111990	0.1254097	0.1228230	0.1313675	0.236047
1	rowRanges_X_S	0.105175	0.114969	0.1302448	0.1297190	0.1390275	0.175607
3	rowRanges(X[cols, rows])	0.173518	0.189835	0.2160340	0.2137265	0.2341840	0.294645

	expr	min	lq	mean	median	uq	max
2	rowRanges(X, cols, rows)	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
1	rowRanges_X_S	1.063523	1.026601	1.038555	1.056146	1.058310	0.7439493
3	rowRanges(X[cols, rows])	1.754603	1.695107	1.722626	1.740118	1.782663	1.2482472

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	91.087	105.285	118.0325	112.7475	120.5720	287.098
2	rowRanges_X_S	105.175	114.969	130.2448	129.7190	139.0275	175.607

	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	1.154665	1.091979	1.103465	1.150527	1.153066	0.6116622

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  5275540 281.8    8529671 455.6  8529671 455.6
Vcells 10137899  77.4   31876688 243.2 60562128 462.1
> 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  5275507 281.8    8529671 455.6  8529671 455.6
Vcells 10138037  77.4   31876688 243.2 60562128 462.1
> 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	0.001966	0.002051	0.0023074	0.0020970	0.0021895	0.018612
2	colRanges(X, rows, cols)	0.002367	0.002446	0.0025814	0.0025165	0.0026375	0.005537
3	colRanges(X[rows, cols])	0.002874	0.003100	0.0033132	0.0031760	0.0032800	0.007898

	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)	1.203967	1.192589	1.118785	1.200048	1.204613	0.2974962
3	colRanges(X[rows, cols])	1.461852	1.511458	1.435938	1.514545	1.498059	0.4243499

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	0.002013	0.0021200	0.0022276	0.0021775	0.0022460	0.004895
2	rowRanges(X, cols, rows)	0.002385	0.0024675	0.0027750	0.0025410	0.0026565	0.020980
3	rowRanges(X[cols, rows])	0.002935	0.0031355	0.0033161	0.0032275	0.0033730	0.006645

	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)	1.184799	1.163915	1.245741	1.166935	1.182769	4.286006
3	rowRanges(X[cols, rows])	1.458023	1.479009	1.488622	1.482204	1.501781	1.357508

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	1.966	2.051	2.30737	2.0970	2.1895	18.612
2	rowRanges_X_S	2.013	2.120	2.22763	2.1775	2.2460	4.895

	expr	min	lq	mean	median	uq	max
1	colRanges_X_S	1.000000	1.000000	1.0000000	1.000000	1.000000	1.0000000
2	rowRanges_X_S	1.023906	1.033642	0.9654412	1.038388	1.025805	0.2630024

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  5275737 281.8    8529671 455.6  8529671 455.6
Vcells 10143856  77.4   31876688 243.2 60562128 462.1
> 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  5275713 281.8    8529671 455.6  8529671 455.6
Vcells 10153909  77.5   31876688 243.2 60562128 462.1
> 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
2	colRanges(X, rows, cols)	0.020479	0.022388	0.0236069	0.0230210	0.023640	0.038637
1	colRanges_X_S	0.021569	0.022727	0.0235964	0.0235820	0.024032	0.030008
3	colRanges(X[rows, cols])	0.036079	0.038117	0.0397602	0.0393795	0.039892	0.073133

	expr	min	lq	mean	median	uq	max
2	colRanges(X, rows, cols)	1.000000	1.000000	1.0000000	1.000000	1.000000	1.0000000
1	colRanges_X_S	1.053225	1.015142	0.9995556	1.024369	1.016582	0.7766649
3	colRanges(X[rows, cols])	1.761756	1.702564	1.6842642	1.710590	1.687479	1.8928229

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
2	rowRanges(X, cols, rows)	0.024003	0.0256810	0.0274516	0.0265875	0.0274190	0.064424
1	rowRanges_X_S	0.025524	0.0270575	0.0284155	0.0283500	0.0292785	0.043727
3	rowRanges(X[cols, rows])	0.040490	0.0423565	0.0438055	0.0437525	0.0450170	0.051882

	expr	min	lq	mean	median	uq	max
2	rowRanges(X, cols, rows)	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
1	rowRanges_X_S	1.063367	1.053600	1.035112	1.066291	1.067818	0.6787377
3	rowRanges(X[cols, rows])	1.686872	1.649332	1.595735	1.645604	1.641818	0.8053210

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	21.569	22.7270	23.59637	23.582	24.0320	30.008
2	rowRanges_X_S	25.524	27.0575	28.41547	28.350	29.2785	43.727

	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	1.183365	1.190544	1.204231	1.202188	1.218313	1.457178

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  5275937 281.8    8529671 455.6  8529671 455.6
Vcells 10145279  77.5   31876688 243.2 60562128 462.1
> 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  5275913 281.8    8529671 455.6  8529671 455.6
Vcells 10155332  77.5   31876688 243.2 60562128 462.1
> 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	0.017208	0.0180405	0.0186029	0.0185750	0.018823	0.032158
2	colRanges(X, rows, cols)	0.018402	0.0192105	0.0197464	0.0196890	0.020151	0.026949
3	colRanges(X[rows, cols])	0.032242	0.0335585	0.0348291	0.0340055	0.035160	0.069628

	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)	1.069386	1.064854	1.061468	1.059973	1.070552	0.8380185
3	colRanges(X[rows, cols])	1.873663	1.860176	1.872238	1.830713	1.867927	2.1651844

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	0.023957	0.025030	0.0260793	0.0253655	0.026453	0.038574
2	rowRanges(X, cols, rows)	0.024391	0.025670	0.0267179	0.0267205	0.027449	0.042101
3	rowRanges(X[cols, rows])	0.041732	0.043514	0.0453701	0.0451915	0.045685	0.081784

	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)	1.018116	1.025569	1.024488	1.053419	1.037652	1.091435
3	rowRanges(X[cols, rows])	1.741954	1.738474	1.739697	1.781613	1.727025	2.120185

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	17.208	18.0405	18.60291	18.5750	18.823	32.158
2	rowRanges_X_S	23.957	25.0300	26.07930	25.3655	26.453	38.574

	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	1.392201	1.387434	1.401894	1.365572	1.405355	1.199515

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  5276142 281.8    8529671 455.6  8529671 455.6
Vcells 10145415  77.5   31876688 243.2 60562128 462.1
> 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  5276118 281.8    8529671 455.6  8529671 455.6
Vcells 10155468  77.5   31876688 243.2 60562128 462.1
> 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	0.036116	0.0401485	0.0430188	0.0421410	0.0448010	0.074487
2	colRanges(X, rows, cols)	0.035282	0.0444070	0.0493891	0.0478195	0.0527630	0.087755
3	colRanges(X[rows, cols])	0.052511	0.0589570	0.0622248	0.0616820	0.0645095	0.087244

	expr	min	lq	mean	median	uq	max
1	colRanges_X_S	1.0000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	colRanges(X, rows, cols)	0.9769077	1.106069	1.148082	1.134750	1.177719	1.178125
3	colRanges(X[rows, cols])	1.4539539	1.468473	1.446457	1.463705	1.439912	1.171265

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	0.036295	0.0401505	0.0422042	0.0416980	0.0440115	0.057381
2	rowRanges(X, cols, rows)	0.030486	0.0412595	0.0453842	0.0447375	0.0485925	0.088057
3	rowRanges(X[cols, rows])	0.049509	0.0561305	0.0585581	0.0581410	0.0612455	0.072385

	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)	0.8399504	1.027621	1.075348	1.072893	1.104086	1.534602
3	rowRanges(X[cols, rows])	1.3640722	1.398003	1.387495	1.394335	1.391579	1.261480

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
2	rowRanges_X_S	36.295	40.1505	42.20418	41.698	44.0115	57.381
1	colRanges_X_S	36.116	40.1485	43.01877	42.141	44.8010	74.487

	expr	min	lq	mean	median	uq	max
2	rowRanges_X_S	1.0000000	1.0000000	1.000000	1.000000	1.000000	1.000000
1	colRanges_X_S	0.9950682	0.9999502	1.019301	1.010624	1.017939	1.298113

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  5276352 281.8    8529671 455.6  8529671 455.6
Vcells 10190887  77.8   31876688 243.2 60562128 462.1
> 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  5276328 281.8    8529671 455.6  8529671 455.6
Vcells 10290940  78.6   31876688 243.2 60562128 462.1
> 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	0.148704	0.1520220	0.1773611	0.168714	0.1937670	0.268810
2	colRanges(X, rows, cols)	0.157781	0.1618165	0.1887665	0.178095	0.2070165	0.350054
3	colRanges(X[rows, cols])	0.243819	0.2551330	0.2953810	0.291198	0.3187445	0.410486

	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)	1.061041	1.064428	1.064306	1.055603	1.068378	1.302236
3	colRanges(X[rows, cols])	1.639626	1.678264	1.665422	1.725986	1.644989	1.527049

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
2	rowRanges(X, cols, rows)	0.177058	0.1798430	0.2033431	0.1887905	0.2189920	0.383434
1	rowRanges_X_S	0.180120	0.1882065	0.2183224	0.2100115	0.2364915	0.327934
3	rowRanges(X[cols, rows])	0.273754	0.2862750	0.3264317	0.3092660	0.3556315	0.469742

	expr	min	lq	mean	median	uq	max
2	rowRanges(X, cols, rows)	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
1	rowRanges_X_S	1.017294	1.046504	1.073665	1.112405	1.079909	0.8552554
3	rowRanges(X[cols, rows])	1.546126	1.591805	1.605324	1.638144	1.623948	1.2250922

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	148.704	152.0220	177.3611	168.7140	193.7670	268.810
2	rowRanges_X_S	180.120	188.2065	218.3224	210.0115	236.4915	327.934

	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	1.211265	1.238022	1.230949	1.244778	1.220494	1.219947

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  5276565 281.8    8529671 455.6  8529671 455.6
Vcells 10191031  77.8   31876688 243.2 60562128 462.1
> 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  5276541 281.8    8529671 455.6  8529671 455.6
Vcells 10291084  78.6   31876688 243.2 60562128 462.1
> 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	0.111994	0.1185700	0.1348464	0.1296555	0.1422635	0.212458
2	colRanges(X, rows, cols)	0.106181	0.1143010	0.1302994	0.1299525	0.1363570	0.202443
3	colRanges(X[rows, cols])	0.207885	0.2264065	0.2566025	0.2520265	0.2767080	0.425901

	expr	min	lq	mean	median	uq	max
1	colRanges_X_S	1.0000000	1.000000	1.0000000	1.000000	1.000000	1.0000000
2	colRanges(X, rows, cols)	0.9480954	0.963996	0.9662801	1.002291	0.958482	0.9528613
3	colRanges(X[rows, cols])	1.8562155	1.909475	1.9029248	1.943817	1.945039	2.0046362

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
2	rowRanges(X, cols, rows)	0.140555	0.1476530	0.1711768	0.1670870	0.1838090	0.351313
1	rowRanges_X_S	0.151332	0.1561730	0.1810896	0.1710405	0.1980135	0.267442
3	rowRanges(X[cols, rows])	0.245382	0.2556045	0.2956437	0.2792345	0.3227230	0.421361

	expr	min	lq	mean	median	uq	max
2	rowRanges(X, cols, rows)	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
1	rowRanges_X_S	1.076675	1.057703	1.057910	1.023661	1.077279	0.7612642
3	rowRanges(X[cols, rows])	1.745808	1.731116	1.727125	1.671192	1.755752	1.1993891

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	111.994	118.570	134.8464	129.6555	142.2635	212.458
2	rowRanges_X_S	151.332	156.173	181.0897	171.0405	198.0135	267.442

	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	1.351251	1.317138	1.342933	1.319192	1.391879	1.258799

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_0.60.1     ggplot2_3.3.5         
[4] knitr_1.33             R.devices_2.17.0       R.utils_2.10.1        
[7] R.oo_1.24.0            R.methodsS3_1.8.1-9001 history_0.0.1-9000    

loaded via a namespace (and not attached):
 [1] Biobase_2.52.0          httr_1.4.2              splines_4.1.1          
 [4] bit64_4.0.5             network_1.17.1          assertthat_0.2.1       
 [7] highr_0.9               stats4_4.1.1            blob_1.2.2             
[10] GenomeInfoDbData_1.2.6  robustbase_0.93-8       pillar_1.6.2           
[13] RSQLite_2.2.8           lattice_0.20-44         glue_1.4.2             
[16] digest_0.6.27           XVector_0.32.0          colorspace_2.0-2       
[19] Matrix_1.3-4            XML_3.99-0.7            pkgconfig_2.0.3        
[22] zlibbioc_1.38.0         genefilter_1.74.0       purrr_0.3.4            
[25] ergm_4.1.2              xtable_1.8-4            scales_1.1.1           
[28] tibble_3.1.4            annotate_1.70.0         KEGGREST_1.32.0        
[31] farver_2.1.0            generics_0.1.0          IRanges_2.26.0         
[34] ellipsis_0.3.2          cachem_1.0.6            withr_2.4.2            
[37] BiocGenerics_0.38.0     mime_0.11               survival_3.2-13        
[40] magrittr_2.0.1          crayon_1.4.1            statnet.common_4.5.0   
[43] memoise_2.0.0           laeken_0.5.1            fansi_0.5.0            
[46] R.cache_0.15.0          MASS_7.3-54             R.rsp_0.44.0           
[49] progressr_0.8.0         tools_4.1.1             lifecycle_1.0.0        
[52] S4Vectors_0.30.0        trust_0.1-8             munsell_0.5.0          
[55] tabby_0.0.1-9001        AnnotationDbi_1.54.1    Biostrings_2.60.2      
[58] compiler_4.1.1          GenomeInfoDb_1.28.1     rlang_0.4.11           
[61] grid_4.1.1              RCurl_1.98-1.4          cwhmisc_6.6            
[64] rappdirs_0.3.3          startup_0.15.0          labeling_0.4.2         
[67] bitops_1.0-7            base64enc_0.1-3         boot_1.3-28            
[70] gtable_0.3.0            DBI_1.1.1               markdown_1.1           
[73] R6_2.5.1                lpSolveAPI_5.5.2.0-17.7 rle_0.9.2              
[76] dplyr_1.0.7             fastmap_1.1.0           bit_4.0.4              
[79] utf8_1.2.2              parallel_4.1.1          Rcpp_1.0.7             
[82] vctrs_0.3.8             png_0.1-7               DEoptimR_1.0-9         
[85] tidyselect_1.1.1        xfun_0.25               coda_0.19-4            

Total processing time was 23.36 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 19:06:56 (+0200 UTC). Powered by RSP.