matrixStats.benchmarks

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colRanks() and rowRanks() benchmarks on subsetted computation

This report benchmark the performance of colRanks() and rowRanks() 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  5294418 282.8    7916910 422.9  7916910 422.9
Vcells 10454721  79.8   33191153 253.3 53339345 407.0
> colStats <- microbenchmark(colRanks_X_S = colRanks(X_S, na.rm = FALSE), `colRanks(X, rows, cols)` = colRanks(X, 
+     rows = rows, cols = cols, na.rm = FALSE), `colRanks(X[rows, cols])` = colRanks(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  5294519 282.8    7916910 422.9  7916910 422.9
Vcells 10455276  79.8   33191153 253.3 53339345 407.0
> rowStats <- microbenchmark(rowRanks_X_S = rowRanks(X_S, na.rm = FALSE), `rowRanks(X, cols, rows)` = rowRanks(X, 
+     rows = cols, cols = rows, na.rm = FALSE), `rowRanks(X[cols, rows])` = rowRanks(X[cols, rows], 
+     na.rm = FALSE), unit = "ms")

Table: Benchmarking of colRanks_X_S(), colRanks(X, rows, cols)() and colRanks(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	colRanks_X_S	0.010032	0.0105045	0.0138274	0.0108680	0.0111200	0.304142
2	colRanks(X, rows, cols)	0.010547	0.0110480	0.0114338	0.0114070	0.0116500	0.015685
3	colRanks(X[rows, cols])	0.011377	0.0117655	0.0124191	0.0122655	0.0125965	0.027460

	expr	min	lq	mean	median	uq	max
1	colRanks_X_S	1.000000	1.000000	1.0000000	1.000000	1.000000	1.0000000
2	colRanks(X, rows, cols)	1.051336	1.051740	0.8268954	1.049595	1.047662	0.0515713
3	colRanks(X[rows, cols])	1.134071	1.120044	0.8981509	1.128588	1.132779	0.0902868

Table: Benchmarking of rowRanks_X_S(), rowRanks(X, cols, rows)() and rowRanks(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	rowRanks_X_S	0.005725	0.0059555	0.0061417	0.0060835	0.0061995	0.009428
2	rowRanks(X, cols, rows)	0.006005	0.0063055	0.0086534	0.0064075	0.0065660	0.221239
3	rowRanks(X[cols, rows])	0.006649	0.0071440	0.0073792	0.0072575	0.0074620	0.013028

	expr	min	lq	mean	median	uq	max
1	rowRanks_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowRanks(X, cols, rows)	1.048908	1.058769	1.408969	1.053259	1.059118	23.466165
3	rowRanks(X[cols, rows])	1.161397	1.199563	1.201487	1.192981	1.203645	1.381841

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

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

	expr	min	lq	mean	median	uq	max
2	rowRanks_X_S	5.725	5.9555	6.14168	6.0835	6.1995	9.428
1	colRanks_X_S	10.032	10.5045	13.82742	10.8680	11.1200	304.142

	expr	min	lq	mean	median	uq	max
2	rowRanks_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.00000
1	colRanks_X_S	1.752314	1.763832	2.251407	1.786472	1.793693	32.25944

Figure: Benchmarking of colRanks_X_S() and rowRanks_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  5293327 282.7    7916910 422.9  7916910 422.9
Vcells 10124574  77.3   33191153 253.3 53339345 407.0
> colStats <- microbenchmark(colRanks_X_S = colRanks(X_S, na.rm = FALSE), `colRanks(X, rows, cols)` = colRanks(X, 
+     rows = rows, cols = cols, na.rm = FALSE), `colRanks(X[rows, cols])` = colRanks(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  5293321 282.7    7916910 422.9  7916910 422.9
Vcells 10129657  77.3   33191153 253.3 53339345 407.0
> rowStats <- microbenchmark(rowRanks_X_S = rowRanks(X_S, na.rm = FALSE), `rowRanks(X, cols, rows)` = rowRanks(X, 
+     rows = cols, cols = rows, na.rm = FALSE), `rowRanks(X[cols, rows])` = rowRanks(X[cols, rows], 
+     na.rm = FALSE), unit = "ms")

Table: Benchmarking of colRanks_X_S(), colRanks(X, rows, cols)() and colRanks(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	colRanks_X_S	0.152024	0.1657050	0.1908988	0.189321	0.2064810	0.254377
2	colRanks(X, rows, cols)	0.159376	0.1747150	0.1975440	0.194722	0.2074785	0.267923
3	colRanks(X[rows, cols])	0.159383	0.1781465	0.2018076	0.197223	0.2167080	0.323973

	expr	min	lq	mean	median	uq	max
1	colRanks_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	colRanks(X, rows, cols)	1.048361	1.054374	1.034810	1.028528	1.004831	1.053252
3	colRanks(X[rows, cols])	1.048407	1.075082	1.057144	1.041739	1.049530	1.273594

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

	expr	min	lq	mean	median	uq	max
1	rowRanks_X_S	0.147362	0.1576265	0.1824829	0.176650	0.2026855	0.241691
2	rowRanks(X, cols, rows)	0.150039	0.1639530	0.1838277	0.178487	0.1924285	0.279286
3	rowRanks(X[cols, rows])	0.155485	0.1697270	0.1902174	0.185682	0.2033160	0.254544

	expr	min	lq	mean	median	uq	max
1	rowRanks_X_S	1.000000	1.000000	1.000000	1.000000	1.0000000	1.000000
2	rowRanks(X, cols, rows)	1.018166	1.040136	1.007370	1.010399	0.9493945	1.155550
3	rowRanks(X[cols, rows])	1.055123	1.076767	1.042385	1.051129	1.0031107	1.053179

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

Table: Benchmarking of colRanks_X_S() and rowRanks_X_S() on integer+100x100 data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
2	rowRanks_X_S	147.362	157.6265	182.4829	176.650	202.6855	241.691
1	colRanks_X_S	152.024	165.7050	190.8988	189.321	206.4810	254.377

	expr	min	lq	mean	median	uq	max
2	rowRanks_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	colRanks_X_S	1.031636	1.051251	1.046119	1.071729	1.018726	1.052488

Figure: Benchmarking of colRanks_X_S() and rowRanks_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  5294070 282.8    7916910 422.9  7916910 422.9
Vcells 10128619  77.3   33191153 253.3 53339345 407.0
> colStats <- microbenchmark(colRanks_X_S = colRanks(X_S, na.rm = FALSE), `colRanks(X, rows, cols)` = colRanks(X, 
+     rows = rows, cols = cols, na.rm = FALSE), `colRanks(X[rows, cols])` = colRanks(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  5294064 282.8    7916910 422.9  7916910 422.9
Vcells 10133702  77.4   33191153 253.3 53339345 407.0
> rowStats <- microbenchmark(rowRanks_X_S = rowRanks(X_S, na.rm = FALSE), `rowRanks(X, cols, rows)` = rowRanks(X, 
+     rows = cols, cols = rows, na.rm = FALSE), `rowRanks(X[cols, rows])` = rowRanks(X[cols, rows], 
+     na.rm = FALSE), unit = "ms")

Table: Benchmarking of colRanks_X_S(), colRanks(X, rows, cols)() and colRanks(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
2	colRanks(X, rows, cols)	0.200874	0.2034770	0.2376866	0.2251025	0.262953	0.340451
1	colRanks_X_S	0.196767	0.2035565	0.2382044	0.2266240	0.258712	0.331434
3	colRanks(X[rows, cols])	0.205192	0.2069565	0.2419362	0.2271965	0.269603	0.398267

	expr	min	lq	mean	median	uq	max
2	colRanks(X, rows, cols)	1.0000000	1.000000	1.000000	1.000000	1.0000000	1.0000000
1	colRanks_X_S	0.9795543	1.000391	1.002178	1.006759	0.9838716	0.9735145
3	colRanks(X[rows, cols])	1.0214961	1.017100	1.017879	1.009302	1.0252897	1.1698218

Table: Benchmarking of rowRanks_X_S(), rowRanks(X, cols, rows)() and rowRanks(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	rowRanks_X_S	0.186699	0.1894420	0.2191481	0.201473	0.2365900	0.316220
3	rowRanks(X[cols, rows])	0.196371	0.1989575	0.2305956	0.220234	0.2556160	0.366882
2	rowRanks(X, cols, rows)	0.190173	0.1930620	0.2264596	0.220360	0.2491185	0.316662

	expr	min	lq	mean	median	uq	max
1	rowRanks_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	rowRanks(X[cols, rows])	1.051805	1.050229	1.052236	1.093119	1.080418	1.160211
2	rowRanks(X, cols, rows)	1.018608	1.019109	1.033363	1.093745	1.052955	1.001398

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

Table: Benchmarking of colRanks_X_S() and rowRanks_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
2	rowRanks_X_S	186.699	189.4420	219.1481	201.473	236.590	316.220
1	colRanks_X_S	196.767	203.5565	238.2044	226.624	258.712	331.434

	expr	min	lq	mean	median	uq	max
2	rowRanks_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	colRanks_X_S	1.053926	1.074506	1.086956	1.124836	1.093503	1.048112

Figure: Benchmarking of colRanks_X_S() and rowRanks_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  5294275 282.8    7916910 422.9  7916910 422.9
Vcells 10129469  77.3   33191153 253.3 53339345 407.0
> colStats <- microbenchmark(colRanks_X_S = colRanks(X_S, na.rm = FALSE), `colRanks(X, rows, cols)` = colRanks(X, 
+     rows = rows, cols = cols, na.rm = FALSE), `colRanks(X[rows, cols])` = colRanks(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  5294269 282.8    7916910 422.9  7916910 422.9
Vcells 10134552  77.4   33191153 253.3 53339345 407.0
> rowStats <- microbenchmark(rowRanks_X_S = rowRanks(X_S, na.rm = FALSE), `rowRanks(X, cols, rows)` = rowRanks(X, 
+     rows = cols, cols = rows, na.rm = FALSE), `rowRanks(X[cols, rows])` = rowRanks(X[cols, rows], 
+     na.rm = FALSE), unit = "ms")

Table: Benchmarking of colRanks_X_S(), colRanks(X, rows, cols)() and colRanks(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	colRanks_X_S	0.092344	0.1017765	0.1126097	0.1093100	0.121655	0.198021
3	colRanks(X[rows, cols])	0.105324	0.1146230	0.1232231	0.1197880	0.130496	0.157222
2	colRanks(X, rows, cols)	0.104418	0.1161910	0.1247681	0.1209115	0.128946	0.174461

	expr	min	lq	mean	median	uq	max
1	colRanks_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
3	colRanks(X[rows, cols])	1.140561	1.126223	1.094249	1.095856	1.072673	0.7939663
2	colRanks(X, rows, cols)	1.130750	1.141629	1.107969	1.106134	1.059932	0.8810227

Table: Benchmarking of rowRanks_X_S(), rowRanks(X, cols, rows)() and rowRanks(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	rowRanks_X_S	0.088483	0.0934085	0.1013918	0.0972985	0.1076370	0.135862
2	rowRanks(X, cols, rows)	0.092686	0.0990780	0.1078311	0.1036120	0.1129945	0.179000
3	rowRanks(X[cols, rows])	0.095436	0.1031545	0.1134725	0.1105220	0.1215570	0.141407

	expr	min	lq	mean	median	uq	max
1	rowRanks_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowRanks(X, cols, rows)	1.047501	1.060696	1.063509	1.064888	1.049774	1.317513
3	rowRanks(X[cols, rows])	1.078580	1.104337	1.119149	1.135906	1.129324	1.040814

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

Table: Benchmarking of colRanks_X_S() and rowRanks_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	rowRanks_X_S	88.483	93.4085	101.3918	97.2985	107.637	135.862
1	colRanks_X_S	92.344	101.7765	112.6097	109.3100	121.655	198.021

	expr	min	lq	mean	median	uq	max
2	rowRanks_X_S	1.000000	1.000000	1.00000	1.00000	1.000000	1.000000
1	colRanks_X_S	1.043635	1.089585	1.11064	1.12345	1.130234	1.457516

Figure: Benchmarking of colRanks_X_S() and rowRanks_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  5294485 282.8    7916910 422.9  7916910 422.9
Vcells 10152141  77.5   33191153 253.3 53339345 407.0
> colStats <- microbenchmark(colRanks_X_S = colRanks(X_S, na.rm = FALSE), `colRanks(X, rows, cols)` = colRanks(X, 
+     rows = rows, cols = cols, na.rm = FALSE), `colRanks(X[rows, cols])` = colRanks(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  5294479 282.8    7916910 422.9  7916910 422.9
Vcells 10202224  77.9   33191153 253.3 53339345 407.0
> rowStats <- microbenchmark(rowRanks_X_S = rowRanks(X_S, na.rm = FALSE), `rowRanks(X, cols, rows)` = rowRanks(X, 
+     rows = cols, cols = rows, na.rm = FALSE), `rowRanks(X[cols, rows])` = rowRanks(X[cols, rows], 
+     na.rm = FALSE), unit = "ms")

Table: Benchmarking of colRanks_X_S(), colRanks(X, rows, cols)() and colRanks(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	colRanks_X_S	1.441827	1.451632	1.509335	1.456159	1.500632	2.319045
2	colRanks(X, rows, cols)	1.509743	1.515233	1.562314	1.519967	1.533454	2.389907
3	colRanks(X[rows, cols])	1.512313	1.518984	1.577089	1.524574	1.547218	2.142405

	expr	min	lq	mean	median	uq	max
1	colRanks_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	colRanks(X, rows, cols)	1.047104	1.043814	1.035101	1.043820	1.021872	1.0305565
3	colRanks(X[rows, cols])	1.048887	1.046397	1.044890	1.046983	1.031044	0.9238307

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

	expr	min	lq	mean	median	uq	max
1	rowRanks_X_S	1.393368	1.397792	1.446303	1.399810	1.405717	2.236869
2	rowRanks(X, cols, rows)	1.421849	1.426300	1.442784	1.428338	1.430756	2.075620
3	rowRanks(X[cols, rows])	1.440722	1.478462	1.509710	1.483329	1.489240	1.993883

	expr	min	lq	mean	median	uq	max
1	rowRanks_X_S	1.000000	1.000000	1.0000000	1.000000	1.000000	1.0000000
2	rowRanks(X, cols, rows)	1.020440	1.020395	0.9975671	1.020380	1.017813	0.9279131
3	rowRanks(X[cols, rows])	1.033985	1.057713	1.0438406	1.059664	1.059417	0.8913723

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

Table: Benchmarking of colRanks_X_S() and rowRanks_X_S() on integer+100x1000 data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
2	rowRanks_X_S	1.393368	1.397792	1.446303	1.399810	1.405717	2.236869
1	colRanks_X_S	1.441827	1.451632	1.509335	1.456159	1.500632	2.319045

	expr	min	lq	mean	median	uq	max
2	rowRanks_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	colRanks_X_S	1.034778	1.038518	1.043582	1.040255	1.067521	1.036737

Figure: Benchmarking of colRanks_X_S() and rowRanks_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  5294689 282.8    7916910 422.9  7916910 422.9
Vcells 10152907  77.5   33191153 253.3 53339345 407.0
> colStats <- microbenchmark(colRanks_X_S = colRanks(X_S, na.rm = FALSE), `colRanks(X, rows, cols)` = colRanks(X, 
+     rows = rows, cols = cols, na.rm = FALSE), `colRanks(X[rows, cols])` = colRanks(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  5294692 282.8    7916910 422.9  7916910 422.9
Vcells 10203005  77.9   33191153 253.3 53339345 407.0
> rowStats <- microbenchmark(rowRanks_X_S = rowRanks(X_S, na.rm = FALSE), `rowRanks(X, cols, rows)` = rowRanks(X, 
+     rows = cols, cols = rows, na.rm = FALSE), `rowRanks(X[cols, rows])` = rowRanks(X[cols, rows], 
+     na.rm = FALSE), unit = "ms")

Table: Benchmarking of colRanks_X_S(), colRanks(X, rows, cols)() and colRanks(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	colRanks_X_S	1.871106	1.878167	1.936659	1.881288	1.889145	3.456286
2	colRanks(X, rows, cols)	1.895522	1.907013	1.980835	1.910787	1.925739	2.920142
3	colRanks(X[rows, cols])	1.934019	1.939526	1.982213	1.943576	1.950561	3.214206

	expr	min	lq	mean	median	uq	max
1	colRanks_X_S	1.000000	1.000000	1.000000	1.000000	1.00000	1.0000000
2	colRanks(X, rows, cols)	1.013049	1.015359	1.022810	1.015680	1.01937	0.8448786
3	colRanks(X[rows, cols])	1.033623	1.032670	1.023521	1.033109	1.03251	0.9299595

Table: Benchmarking of rowRanks_X_S(), rowRanks(X, cols, rows)() and rowRanks(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	rowRanks_X_S	1.849571	1.858715	1.900790	1.861840	1.868486	2.572422
2	rowRanks(X, cols, rows)	1.869737	1.876607	1.935731	1.880375	1.884843	3.134836
3	rowRanks(X[cols, rows])	1.926088	1.935420	1.974058	1.940850	1.949309	3.109879

	expr	min	lq	mean	median	uq	max
1	rowRanks_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowRanks(X, cols, rows)	1.010903	1.009626	1.018383	1.009955	1.008754	1.218632
3	rowRanks(X[cols, rows])	1.041370	1.041268	1.038546	1.042436	1.043255	1.208930

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

Table: Benchmarking of colRanks_X_S() and rowRanks_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
2	rowRanks_X_S	1.849571	1.858715	1.900790	1.861840	1.868486	2.572422
1	colRanks_X_S	1.871106	1.878167	1.936659	1.881288	1.889145	3.456286

	expr	min	lq	mean	median	uq	max
2	rowRanks_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	colRanks_X_S	1.011643	1.010466	1.018871	1.010446	1.011056	1.343592

Figure: Benchmarking of colRanks_X_S() and rowRanks_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  5294915 282.8    7916910 422.9  7916910 422.9
Vcells 10244025  78.2   33191153 253.3 53339345 407.0
> colStats <- microbenchmark(colRanks_X_S = colRanks(X_S, na.rm = FALSE), `colRanks(X, rows, cols)` = colRanks(X, 
+     rows = rows, cols = cols, na.rm = FALSE), `colRanks(X[rows, cols])` = colRanks(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  5294900 282.8    7916910 422.9  7916910 422.9
Vcells 10244193  78.2   33191153 253.3 53339345 407.0
> rowStats <- microbenchmark(rowRanks_X_S = rowRanks(X_S, na.rm = FALSE), `rowRanks(X, cols, rows)` = rowRanks(X, 
+     rows = cols, cols = rows, na.rm = FALSE), `rowRanks(X[cols, rows])` = rowRanks(X[cols, rows], 
+     na.rm = FALSE), unit = "ms")

Table: Benchmarking of colRanks_X_S(), colRanks(X, rows, cols)() and colRanks(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	colRanks_X_S	0.010046	0.0108215	0.0121828	0.0111040	0.0114215	0.078071
2	colRanks(X, rows, cols)	0.010503	0.0111190	0.0120167	0.0114835	0.0118055	0.023125
3	colRanks(X[rows, cols])	0.011499	0.0121265	0.0129971	0.0125630	0.0128560	0.023611

	expr	min	lq	mean	median	uq	max
1	colRanks_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	colRanks(X, rows, cols)	1.045491	1.027492	0.986366	1.034177	1.033621	0.2962047
3	colRanks(X[rows, cols])	1.144635	1.120593	1.066838	1.131394	1.125597	0.3024298

Table: Benchmarking of rowRanks_X_S(), rowRanks(X, cols, rows)() and rowRanks(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	rowRanks_X_S	0.005589	0.0059690	0.0061898	0.0060755	0.0062490	0.009853
2	rowRanks(X, cols, rows)	0.005804	0.0062175	0.0067400	0.0063605	0.0065025	0.039315
3	rowRanks(X[cols, rows])	0.006751	0.0071200	0.0073298	0.0072655	0.0074530	0.009750

	expr	min	lq	mean	median	uq	max
1	rowRanks_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	rowRanks(X, cols, rows)	1.038468	1.041632	1.088905	1.046910	1.040566	3.9901553
3	rowRanks(X[cols, rows])	1.207908	1.192830	1.184182	1.195869	1.192671	0.9895463

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

Table: Benchmarking of colRanks_X_S() and rowRanks_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	rowRanks_X_S	5.589	5.9690	6.18975	6.0755	6.2490	9.853
1	colRanks_X_S	10.046	10.8215	12.18282	11.1040	11.4215	78.071

	expr	min	lq	mean	median	uq	max
2	rowRanks_X_S	1.000000	1.00000	1.000000	1.000000	1.000000	1.000000
1	colRanks_X_S	1.797459	1.81295	1.968225	1.827668	1.827732	7.923577

Figure: Benchmarking of colRanks_X_S() and rowRanks_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  5295112 282.8    7916910 422.9  7916910 422.9
Vcells 10249968  78.3   33191153 253.3 53339345 407.0
> colStats <- microbenchmark(colRanks_X_S = colRanks(X_S, na.rm = FALSE), `colRanks(X, rows, cols)` = colRanks(X, 
+     rows = rows, cols = cols, na.rm = FALSE), `colRanks(X[rows, cols])` = colRanks(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  5295106 282.8    7916910 422.9  7916910 422.9
Vcells 10260051  78.3   33191153 253.3 53339345 407.0
> rowStats <- microbenchmark(rowRanks_X_S = rowRanks(X_S, na.rm = FALSE), `rowRanks(X, cols, rows)` = rowRanks(X, 
+     rows = cols, cols = rows, na.rm = FALSE), `rowRanks(X[cols, rows])` = rowRanks(X[cols, rows], 
+     na.rm = FALSE), unit = "ms")

Table: Benchmarking of colRanks_X_S(), colRanks(X, rows, cols)() and colRanks(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	colRanks_X_S	0.151390	0.1581345	0.1843986	0.181861	0.199447	0.287467
2	colRanks(X, rows, cols)	0.155479	0.1655370	0.1875884	0.185985	0.203799	0.291003
3	colRanks(X[rows, cols])	0.164027	0.1734645	0.2010427	0.196166	0.215847	0.371377

	expr	min	lq	mean	median	uq	max
1	colRanks_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	colRanks(X, rows, cols)	1.027010	1.046811	1.017298	1.022677	1.021820	1.012301
3	colRanks(X[rows, cols])	1.083473	1.096943	1.090261	1.078659	1.082227	1.291894

Table: Benchmarking of rowRanks_X_S(), rowRanks(X, cols, rows)() and rowRanks(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	rowRanks(X, cols, rows)	0.147523	0.1628170	0.1851003	0.185114	0.1927970	0.297328
1	rowRanks_X_S	0.147318	0.1609815	0.1858971	0.186161	0.2048955	0.264698
3	rowRanks(X[cols, rows])	0.157847	0.1744075	0.1966930	0.193793	0.2108030	0.268770

	expr	min	lq	mean	median	uq	max
2	rowRanks(X, cols, rows)	1.0000000	1.0000000	1.000000	1.000000	1.000000	1.0000000
1	rowRanks_X_S	0.9986104	0.9887266	1.004304	1.005656	1.062752	0.8902559
3	rowRanks(X[cols, rows])	1.0699823	1.0711873	1.062629	1.046885	1.093394	0.9039512

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

Table: Benchmarking of colRanks_X_S() and rowRanks_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	colRanks_X_S	151.390	158.1345	184.3986	181.861	199.4470	287.467
2	rowRanks_X_S	147.318	160.9815	185.8971	186.161	204.8955	264.698

	expr	min	lq	mean	median	uq	max
1	colRanks_X_S	1.0000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	rowRanks_X_S	0.9731026	1.018004	1.008126	1.023644	1.027318	0.9207944

Figure: Benchmarking of colRanks_X_S() and rowRanks_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  5295312 282.9    7916910 422.9  7916910 422.9
Vcells 10251380  78.3   33191153 253.3 53339345 407.0
> colStats <- microbenchmark(colRanks_X_S = colRanks(X_S, na.rm = FALSE), `colRanks(X, rows, cols)` = colRanks(X, 
+     rows = rows, cols = cols, na.rm = FALSE), `colRanks(X[rows, cols])` = colRanks(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  5295306 282.8    7916910 422.9  7916910 422.9
Vcells 10261463  78.3   33191153 253.3 53339345 407.0
> rowStats <- microbenchmark(rowRanks_X_S = rowRanks(X_S, na.rm = FALSE), `rowRanks(X, cols, rows)` = rowRanks(X, 
+     rows = cols, cols = rows, na.rm = FALSE), `rowRanks(X[cols, rows])` = rowRanks(X[cols, rows], 
+     na.rm = FALSE), unit = "ms")

Table: Benchmarking of colRanks_X_S(), colRanks(X, rows, cols)() and colRanks(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
2	colRanks(X, rows, cols)	0.216300	0.2195605	0.2471112	0.2313465	0.270918	0.333435
1	colRanks_X_S	0.213868	0.2188160	0.2468778	0.2342785	0.270833	0.329352
3	colRanks(X[rows, cols])	0.224252	0.2269050	0.2577789	0.2412950	0.285416	0.434080

	expr	min	lq	mean	median	uq	max
2	colRanks(X, rows, cols)	1.0000000	1.0000000	1.0000000	1.000000	1.0000000	1.0000000
1	colRanks_X_S	0.9887564	0.9966091	0.9990553	1.012674	0.9996863	0.9877547
3	colRanks(X[rows, cols])	1.0367638	1.0334509	1.0431692	1.043003	1.0535143	1.3018429

Table: Benchmarking of rowRanks_X_S(), rowRanks(X, cols, rows)() and rowRanks(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	rowRanks_X_S	0.208932	0.2122895	0.2423210	0.2212945	0.2607075	0.360016
2	rowRanks(X, cols, rows)	0.210265	0.2119360	0.2458155	0.2361405	0.2737270	0.349649
3	rowRanks(X[cols, rows])	0.222993	0.2261535	0.2600201	0.2451795	0.2823920	0.427432

	expr	min	lq	mean	median	uq	max
1	rowRanks_X_S	1.000000	1.0000000	1.000000	1.000000	1.000000	1.0000000
2	rowRanks(X, cols, rows)	1.006380	0.9983348	1.014421	1.067087	1.049939	0.9712041
3	rowRanks(X[cols, rows])	1.067299	1.0653070	1.073040	1.107933	1.083176	1.1872583

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

Table: Benchmarking of colRanks_X_S() and rowRanks_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
2	rowRanks_X_S	208.932	212.2895	242.3210	221.2945	260.7075	360.016
1	colRanks_X_S	213.868	218.8160	246.8778	234.2785	270.8330	329.352

	expr	min	lq	mean	median	uq	max
2	rowRanks_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	colRanks_X_S	1.023625	1.030743	1.018805	1.058673	1.038838	0.914826

Figure: Benchmarking of colRanks_X_S() and rowRanks_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  5295517 282.9    7916910 422.9  7916910 422.9
Vcells 10251516  78.3   33191153 253.3 53339345 407.0
> colStats <- microbenchmark(colRanks_X_S = colRanks(X_S, na.rm = FALSE), `colRanks(X, rows, cols)` = colRanks(X, 
+     rows = rows, cols = cols, na.rm = FALSE), `colRanks(X[rows, cols])` = colRanks(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  5295511 282.9    7916910 422.9  7916910 422.9
Vcells 10261599  78.3   33191153 253.3 53339345 407.0
> rowStats <- microbenchmark(rowRanks_X_S = rowRanks(X_S, na.rm = FALSE), `rowRanks(X, cols, rows)` = rowRanks(X, 
+     rows = cols, cols = rows, na.rm = FALSE), `rowRanks(X[cols, rows])` = rowRanks(X[cols, rows], 
+     na.rm = FALSE), unit = "ms")

Table: Benchmarking of colRanks_X_S(), colRanks(X, rows, cols)() and colRanks(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	colRanks_X_S	0.105517	0.112539	0.1269707	0.1180565	0.131107	0.206649
2	colRanks(X, rows, cols)	0.112464	0.117770	0.1287253	0.1207140	0.132215	0.218159
3	colRanks(X[rows, cols])	0.121687	0.129823	0.1405388	0.1353255	0.144698	0.220886

	expr	min	lq	mean	median	uq	max
1	colRanks_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	colRanks(X, rows, cols)	1.065838	1.046482	1.013819	1.022510	1.008451	1.055698
3	colRanks(X[rows, cols])	1.153245	1.153582	1.106860	1.146277	1.103663	1.068895

Table: Benchmarking of rowRanks_X_S(), rowRanks(X, cols, rows)() and rowRanks(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	rowRanks_X_S	0.099094	0.105963	0.1162706	0.1105740	0.1247770	0.160034
2	rowRanks(X, cols, rows)	0.099429	0.107063	0.1174934	0.1121625	0.1234985	0.187703
3	rowRanks(X[cols, rows])	0.111495	0.120578	0.1337787	0.1261040	0.1439960	0.222497

	expr	min	lq	mean	median	uq	max
1	rowRanks_X_S	1.000000	1.000000	1.000000	1.000000	1.0000000	1.000000
2	rowRanks(X, cols, rows)	1.003381	1.010381	1.010517	1.014366	0.9897537	1.172894
3	rowRanks(X[cols, rows])	1.125144	1.137925	1.150580	1.140449	1.1540268	1.390311

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

Table: Benchmarking of colRanks_X_S() and rowRanks_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	rowRanks_X_S	99.094	105.963	116.2707	110.5740	124.777	160.034
1	colRanks_X_S	105.517	112.539	126.9707	118.0565	131.107	206.649

	expr	min	lq	mean	median	uq	max
2	rowRanks_X_S	1.000000	1.000000	1.000000	1.00000	1.000000	1.000000
1	colRanks_X_S	1.064817	1.062059	1.092027	1.06767	1.050731	1.291282

Figure: Benchmarking of colRanks_X_S() and rowRanks_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  5295727 282.9    7916910 422.9  7916910 422.9
Vcells 10296973  78.6   33191153 253.3 53339345 407.0
> colStats <- microbenchmark(colRanks_X_S = colRanks(X_S, na.rm = FALSE), `colRanks(X, rows, cols)` = colRanks(X, 
+     rows = rows, cols = cols, na.rm = FALSE), `colRanks(X[rows, cols])` = colRanks(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  5295721 282.9    7916910 422.9  7916910 422.9
Vcells 10397056  79.4   33191153 253.3 53339345 407.0
> rowStats <- microbenchmark(rowRanks_X_S = rowRanks(X_S, na.rm = FALSE), `rowRanks(X, cols, rows)` = rowRanks(X, 
+     rows = cols, cols = rows, na.rm = FALSE), `rowRanks(X[cols, rows])` = rowRanks(X[cols, rows], 
+     na.rm = FALSE), unit = "ms")

Table: Benchmarking of colRanks_X_S(), colRanks(X, rows, cols)() and colRanks(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	colRanks_X_S	1.515504	1.533683	1.674026	1.595538	1.735742	2.411300
2	colRanks(X, rows, cols)	1.546539	1.562464	1.717956	1.668411	1.807972	2.359373
3	colRanks(X[rows, cols])	1.614494	1.636637	1.844790	1.744627	2.031112	2.523000

	expr	min	lq	mean	median	uq	max
1	colRanks_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	colRanks(X, rows, cols)	1.020478	1.018766	1.026242	1.045673	1.041613	0.9784651
3	colRanks(X[rows, cols])	1.065318	1.067128	1.102008	1.093441	1.170169	1.0463236

Table: Benchmarking of rowRanks_X_S(), rowRanks(X, cols, rows)() and rowRanks(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	rowRanks_X_S	1.511452	1.554996	1.690989	1.600645	1.747926	2.597745
2	rowRanks(X, cols, rows)	1.511784	1.541399	1.689750	1.622421	1.804791	2.376141
3	rowRanks(X[cols, rows])	1.631885	1.668545	1.806874	1.756641	1.888040	2.417612

	expr	min	lq	mean	median	uq	max
1	rowRanks_X_S	1.00000	1.0000000	1.000000	1.000000	1.000000	1.0000000
2	rowRanks(X, cols, rows)	1.00022	0.9912559	0.999267	1.013604	1.032533	0.9146937
3	rowRanks(X[cols, rows])	1.07968	1.0730221	1.068531	1.097458	1.080160	0.9306579

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

Table: Benchmarking of colRanks_X_S() and rowRanks_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	colRanks_X_S	1.515504	1.533683	1.674026	1.595538	1.735742	2.411300
2	rowRanks_X_S	1.511452	1.554996	1.690989	1.600645	1.747926	2.597745

	expr	min	lq	mean	median	uq	max
1	colRanks_X_S	1.0000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowRanks_X_S	0.9973263	1.013896	1.010133	1.003201	1.007019	1.077321

Figure: Benchmarking of colRanks_X_S() and rowRanks_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  5295940 282.9    7916910 422.9  7916910 422.9
Vcells 10297117  78.6   33191153 253.3 53339345 407.0
> colStats <- microbenchmark(colRanks_X_S = colRanks(X_S, na.rm = FALSE), `colRanks(X, rows, cols)` = colRanks(X, 
+     rows = rows, cols = cols, na.rm = FALSE), `colRanks(X[rows, cols])` = colRanks(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  5295934 282.9    7916910 422.9  7916910 422.9
Vcells 10397200  79.4   33191153 253.3 53339345 407.0
> rowStats <- microbenchmark(rowRanks_X_S = rowRanks(X_S, na.rm = FALSE), `rowRanks(X, cols, rows)` = rowRanks(X, 
+     rows = cols, cols = rows, na.rm = FALSE), `rowRanks(X[cols, rows])` = rowRanks(X[cols, rows], 
+     na.rm = FALSE), unit = "ms")

Table: Benchmarking of colRanks_X_S(), colRanks(X, rows, cols)() and colRanks(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	colRanks_X_S	2.104173	2.132992	2.332693	2.241137	2.445591	3.469060
3	colRanks(X[rows, cols])	2.190613	2.211988	2.392780	2.258703	2.511791	3.488755
2	colRanks(X, rows, cols)	2.126134	2.165003	2.371664	2.306563	2.537124	3.207789

	expr	min	lq	mean	median	uq	max
1	colRanks_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
3	colRanks(X[rows, cols])	1.041080	1.037035	1.025759	1.007838	1.027069	1.0056773
2	colRanks(X, rows, cols)	1.010437	1.015008	1.016706	1.029193	1.037428	0.9246854

Table: Benchmarking of rowRanks_X_S(), rowRanks(X, cols, rows)() and rowRanks(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	rowRanks_X_S	2.104285	2.128067	2.356135	2.163998	2.571961	3.210471
2	rowRanks(X, cols, rows)	2.100223	2.111956	2.411410	2.189487	2.561288	3.889469
3	rowRanks(X[cols, rows])	2.218066	2.236652	2.459743	2.270361	2.644265	3.865038

	expr	min	lq	mean	median	uq	max
1	rowRanks_X_S	1.0000000	1.0000000	1.000000	1.000000	1.0000000	1.000000
2	rowRanks(X, cols, rows)	0.9980697	0.9924288	1.023460	1.011779	0.9958501	1.211495
3	rowRanks(X[cols, rows])	1.0540711	1.0510249	1.043974	1.049151	1.0281126	1.203885

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

Table: Benchmarking of colRanks_X_S() and rowRanks_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
2	rowRanks_X_S	2.104285	2.128067	2.356135	2.163998	2.571961	3.210471
1	colRanks_X_S	2.104173	2.132992	2.332693	2.241137	2.445591	3.469060

	expr	min	lq	mean	median	uq	max
2	rowRanks_X_S	1.0000000	1.000000	1.0000000	1.000000	1.0000000	1.000000
1	colRanks_X_S	0.9999468	1.002314	0.9900505	1.035646	0.9508663	1.080545

Figure: Benchmarking of colRanks_X_S() and rowRanks_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 28.79 secs.

Reproducibility

To reproduce this report, do:

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

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Copyright Dongcan Jiang. Last updated on 2021-08-25 22:28:18 (+0200 UTC). Powered by RSP.