colRowRanks_subset

matrixStats: Benchmark report

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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 3189675 170.4    5709258 305.0  5709258 305.0
Vcells 6391544  48.8   22343563 170.5 56666022 432.4
> 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 3188374 170.3    5709258 305.0  5709258 305.0
Vcells 6387831  48.8   22343563 170.5 56666022 432.4
> 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	0.006350	0.0066575	0.0085987	0.0068765	0.0070935	0.172760
2	colRanks(X, rows, cols)	0.006669	0.0070055	0.0071930	0.0071355	0.0073320	0.009230
3	colRanks(X[rows, cols])	0.007167	0.0075945	0.0078656	0.0077860	0.0079945	0.013184

	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)	1.050236	1.052272	0.8365305	1.037664	1.033622	0.0534267
3	colRanks(X[rows, cols])	1.128661	1.140743	0.9147438	1.132262	1.127018	0.0763140

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	0.002687	0.0030055	0.0031705	0.0031265	0.0032875	0.005524
2	rowRanks(X, cols, rows)	0.002975	0.0031885	0.0044617	0.0032855	0.0034170	0.114276
3	rowRanks(X[cols, rows])	0.003320	0.0037140	0.0038791	0.0038280	0.0039815	0.005509

	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)	1.107183	1.060888	1.407260	1.050856	1.039392	20.6871832
3	rowRanks(X[cols, rows])	1.235579	1.235734	1.223493	1.224372	1.211103	0.9972846

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	2.687	3.0055	3.17048	3.1265	3.2875	5.524
1	colRanks_X_S	6.350	6.6575	8.59867	6.8765	7.0935	172.760

	expr	min	lq	mean	median	uq	max
2	rowRanks_X_S	1.00000	1.000000	1.000000	1.000000	1.000000	1.00000
1	colRanks_X_S	2.36323	2.215106	2.712104	2.199424	2.157719	31.27444

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 3187051 170.3    5709258 305.0  5709258 305.0
Vcells 6056825  46.3   22343563 170.5 56666022 432.4
> 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 3187042 170.3    5709258 305.0  5709258 305.0
Vcells 6061903  46.3   22343563 170.5 56666022 432.4
> 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	0.146100	0.1470375	0.1479697	0.1476670	0.148275	0.164900
2	colRanks(X, rows, cols)	0.152015	0.1531385	0.1540843	0.1537925	0.154551	0.162070
3	colRanks(X[rows, cols])	0.154721	0.1560120	0.1575690	0.1566260	0.157623	0.214578

	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)	1.040486	1.041493	1.041323	1.041482	1.042327	0.9828381
3	colRanks(X[rows, cols])	1.059008	1.061036	1.064874	1.060670	1.063045	1.3012614

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	0.132711	0.1344080	0.1353779	0.1348785	0.1354535	0.158191
2	rowRanks(X, cols, rows)	0.134021	0.1358705	0.1368152	0.1364685	0.1372090	0.151167
3	rowRanks(X[cols, rows])	0.141178	0.1427820	0.1440559	0.1433960	0.1440355	0.182017

	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)	1.009871	1.010881	1.010617	1.011788	1.012960	0.955598
3	rowRanks(X[cols, rows])	1.063800	1.062303	1.064102	1.063149	1.063357	1.150615

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	132.711	134.4080	135.3779	134.8785	135.4535	158.191
1	colRanks_X_S	146.100	147.0375	147.9697	147.6670	148.2750	164.900

	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	1.100888	1.093964	1.093012	1.094815	1.094656	1.042411

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 3187804 170.3    5709258 305.0  5709258 305.0
Vcells 6060876  46.3   22343563 170.5 56666022 432.4
> 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 3187795 170.3    5709258 305.0  5709258 305.0
Vcells 6065954  46.3   22343563 170.5 56666022 432.4
> 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
1	colRanks_X_S	0.191569	0.1933455	0.1941410	0.1937770	0.194706	0.201388
2	colRanks(X, rows, cols)	0.195026	0.1968875	0.1984131	0.1974230	0.198151	0.257300
3	colRanks(X[rows, cols])	0.200973	0.2023190	0.2033049	0.2028635	0.203853	0.212349

	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)	1.018046	1.018320	1.022005	1.018815	1.017693	1.277633
3	colRanks(X[rows, cols])	1.049089	1.046412	1.047202	1.046892	1.046979	1.054427

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	0.180956	0.1820075	0.1833657	0.1826700	0.1833715	0.219384
2	rowRanks(X, cols, rows)	0.183317	0.1844490	0.1854869	0.1851870	0.1856900	0.197945
3	rowRanks(X[cols, rows])	0.190575	0.1917190	0.1927902	0.1924555	0.1932595	0.200437

	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)	1.013047	1.013414	1.011568	1.013779	1.012644	0.9022764
3	rowRanks(X[cols, rows])	1.053157	1.053358	1.051397	1.053569	1.053923	0.9136355

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	180.956	182.0075	183.3657	182.670	183.3715	219.384
1	colRanks_X_S	191.569	193.3455	194.1410	193.777	194.7060	201.388

	expr	min	lq	mean	median	uq	max
2	rowRanks_X_S	1.00000	1.000000	1.000000	1.000000	1.000000	1.0000000
1	colRanks_X_S	1.05865	1.062294	1.058764	1.060804	1.061812	0.9179703

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 3188010 170.3    5709258 305.0  5709258 305.0
Vcells 6061730  46.3   22343563 170.5 56666022 432.4
> 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 3188001 170.3    5709258 305.0  5709258 305.0
Vcells 6066808  46.3   22343563 170.5 56666022 432.4
> 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	0.082267	0.0838140	0.0850312	0.0848640	0.0859740	0.091612
2	colRanks(X, rows, cols)	0.089568	0.0924520	0.0940159	0.0932900	0.0940160	0.160850
3	colRanks(X[rows, cols])	0.091614	0.0932985	0.0947615	0.0948925	0.0955365	0.102056

	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)	1.088748	1.103061	1.105664	1.099288	1.093540	1.755774
3	colRanks(X[rows, cols])	1.113618	1.113161	1.114433	1.118171	1.111225	1.114003

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	0.071534	0.0730685	0.0750661	0.0747220	0.0754385	0.114470
2	rowRanks(X, cols, rows)	0.075961	0.0782735	0.0794601	0.0792075	0.0799975	0.093830
3	rowRanks(X[cols, rows])	0.079881	0.0814475	0.0825500	0.0823015	0.0833625	0.093299

	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)	1.061887	1.071235	1.058536	1.060029	1.060433	0.8196907
3	rowRanks(X[cols, rows])	1.116686	1.114673	1.099697	1.101436	1.105039	0.8150520

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	71.534	73.0685	75.06606	74.722	75.4385	114.470
1	colRanks_X_S	82.267	83.8140	85.03116	84.864	85.9740	91.612

	expr	min	lq	mean	median	uq	max
2	rowRanks_X_S	1.000000	1.000000	1.000000	1.00000	1.000000	1.0000000
1	colRanks_X_S	1.150041	1.147061	1.132751	1.13573	1.139657	0.8003145

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 3188209 170.3    5709258 305.0  5709258 305.0
Vcells 6084391  46.5   22343563 170.5 56666022 432.4
> 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 3188200 170.3    5709258 305.0  5709258 305.0
Vcells 6134469  46.9   22343563 170.5 56666022 432.4
> 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	1.411227	1.421110	1.492093	1.527478	1.536074	1.753078
2	colRanks(X, rows, cols)	1.460636	1.472684	1.633215	1.581341	1.592728	10.278399
3	colRanks(X[rows, cols])	1.484481	1.496355	1.601260	1.653813	1.665463	1.830711

	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)	1.035011	1.036292	1.094580	1.035262	1.036882	5.863059
3	colRanks(X[rows, cols])	1.051908	1.052948	1.073164	1.082708	1.084234	1.044284

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	1.332971	1.362006	1.384078	1.368561	1.413801	1.445405
2	rowRanks(X, cols, rows)	1.344751	1.380868	1.402368	1.385862	1.432033	1.532496
3	rowRanks(X[cols, rows])	1.424064	1.441168	1.471645	1.444830	1.496293	1.836529

	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)	1.008837	1.013848	1.013214	1.012642	1.012896	1.060254
3	rowRanks(X[cols, rows])	1.068338	1.058121	1.063267	1.055730	1.058348	1.270598

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	1.332971	1.362006	1.384078	1.368561	1.413801	1.445405
1	colRanks_X_S	1.411227	1.421110	1.492093	1.527478	1.536074	1.753078

	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	1.058708	1.043394	1.078041	1.116121	1.086485	1.212863

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 3188425 170.3    5709258 305.0  5709258 305.0
Vcells 6085174  46.5   22343563 170.5 56666022 432.4
> 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 3188416 170.3    5709258 305.0  5709258 305.0
Vcells 6135252  46.9   22343563 170.5 56666022 432.4
> 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	1.853096	1.861617	1.913827	1.868316	1.969691	2.282829
2	colRanks(X, rows, cols)	1.870430	1.879618	1.928878	1.889724	1.989823	2.049448
3	colRanks(X[rows, cols])	1.915856	1.924066	2.091811	1.931529	2.086304	10.843138

	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)	1.009354	1.009670	1.007864	1.011458	1.010220	0.8977668
3	colRanks(X[rows, cols])	1.033868	1.033546	1.092999	1.033834	1.059203	4.7498687

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	1.786084	1.830661	1.845799	1.834907	1.873118	1.972461
2	rowRanks(X, cols, rows)	1.792278	1.842586	1.860069	1.846207	1.889986	2.008695
3	rowRanks(X[cols, rows])	1.860194	1.907249	1.943382	1.916070	2.005546	2.294774

	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)	1.003468	1.006514	1.007731	1.006158	1.009005	1.018370
3	rowRanks(X[cols, rows])	1.041493	1.041836	1.052868	1.044232	1.070699	1.163407

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	1.786084	1.830661	1.845799	1.834907	1.873118	1.972461
1	colRanks_X_S	1.853096	1.861617	1.913827	1.868316	1.969691	2.282829

	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	1.037519	1.016909	1.036855	1.018207	1.051558	1.157351

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 3188643 170.3    5709258 305.0  5709258 305.0
Vcells 6176271  47.2   22343563 170.5 56666022 432.4
> 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 3188625 170.3    5709258 305.0  5709258 305.0
Vcells 6176434  47.2   22343563 170.5 56666022 432.4
> 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	0.006295	0.0065565	0.0074843	0.006799	0.0069920	0.073618
2	colRanks(X, rows, cols)	0.006528	0.0069690	0.0072039	0.007145	0.0073405	0.011847
3	colRanks(X[rows, cols])	0.007289	0.0077305	0.0079796	0.007899	0.0080720	0.013689

	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)	1.037014	1.062915	0.9625348	1.050890	1.049843	0.1609253
3	colRanks(X[rows, cols])	1.157903	1.179059	1.0661787	1.161788	1.154462	0.1859464

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	0.002713	0.0030020	0.0032059	0.0030990	0.0032590	0.005540
2	rowRanks(X, cols, rows)	0.002975	0.0031710	0.0037138	0.0032460	0.0034215	0.043964
3	rowRanks(X[cols, rows])	0.003426	0.0037305	0.0039460	0.0038625	0.0040475	0.006218

	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)	1.096572	1.056296	1.158430	1.047435	1.049862	7.935740
3	rowRanks(X[cols, rows])	1.262809	1.242672	1.230868	1.246370	1.241945	1.122383

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	2.713	3.0020	3.20590	3.099	3.259	5.540
1	colRanks_X_S	6.295	6.5565	7.48428	6.799	6.992	73.618

	expr	min	lq	mean	median	uq	max
2	rowRanks_X_S	1.00000	1.000000	1.000000	1.000000	1.000000	1.00000
1	colRanks_X_S	2.32031	2.184044	2.334533	2.193933	2.145443	13.28845

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 3188844 170.4    5709258 305.0  5709258 305.0
Vcells 6182220  47.2   22343563 170.5 56666022 432.4
> 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 3188835 170.4    5709258 305.0  5709258 305.0
Vcells 6192298  47.3   22343563 170.5 56666022 432.4
> 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	0.148067	0.1497760	0.1505854	0.1503645	0.1510290	0.157633
2	colRanks(X, rows, cols)	0.150516	0.1518635	0.1530059	0.1523510	0.1532895	0.175781
3	colRanks(X[rows, cols])	0.165477	0.1666115	0.1684137	0.1672240	0.1677795	0.242017

	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)	1.016540	1.013937	1.016074	1.013211	1.014967	1.115128
3	colRanks(X[rows, cols])	1.117582	1.112405	1.118394	1.112124	1.110909	1.535319

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)	0.141241	0.1425290	0.1433898	0.143155	0.1438515	0.149615
1	rowRanks_X_S	0.141344	0.1428305	0.1436151	0.143309	0.1438860	0.159763
3	rowRanks(X[cols, rows])	0.149741	0.1513290	0.1529057	0.151880	0.1525175	0.218880

	expr	min	lq	mean	median	uq	max
2	rowRanks(X, cols, rows)	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	rowRanks_X_S	1.000729	1.002115	1.001571	1.001076	1.000240	1.067827
3	rowRanks(X[cols, rows])	1.060181	1.061742	1.066364	1.060948	1.060243	1.462955

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
2	rowRanks_X_S	141.344	142.8305	143.6151	143.3090	143.886	159.763
1	colRanks_X_S	148.067	149.7760	150.5854	150.3645	151.029	157.633

	expr	min	lq	mean	median	uq	max
2	rowRanks_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
1	colRanks_X_S	1.047565	1.048628	1.048535	1.049233	1.049643	0.9866678

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 3189054 170.4    5709258 305.0  5709258 305.0
Vcells 6183638  47.2   22343563 170.5 56666022 432.4
> 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 3189045 170.4    5709258 305.0  5709258 305.0
Vcells 6193716  47.3   22343563 170.5 56666022 432.4
> 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
1	colRanks_X_S	0.207090	0.2092345	0.2107721	0.2100685	0.2111005	0.237037
2	colRanks(X, rows, cols)	0.209228	0.2105970	0.2123215	0.2113035	0.2121890	0.290789
3	colRanks(X[rows, cols])	0.224139	0.2258135	0.2272842	0.2265125	0.2279600	0.240405

	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)	1.010324	1.006512	1.007351	1.005879	1.005156	1.226766
3	colRanks(X[rows, cols])	1.082326	1.079236	1.078341	1.078279	1.079865	1.014209

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
2	rowRanks(X, cols, rows)	0.201641	0.2032225	0.2043059	0.2038650	0.2047990	0.226545
1	rowRanks_X_S	0.202491	0.2040215	0.2054759	0.2046010	0.2052390	0.261856
3	rowRanks(X[cols, rows])	0.212711	0.2141600	0.2153143	0.2148135	0.2155685	0.231875

	expr	min	lq	mean	median	uq	max
2	rowRanks(X, cols, rows)	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	rowRanks_X_S	1.004215	1.003932	1.005727	1.003610	1.002148	1.155868
3	rowRanks(X[cols, rows])	1.054900	1.053820	1.053882	1.053705	1.052586	1.023527

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	202.491	204.0215	205.4759	204.6010	205.2390	261.856
1	colRanks_X_S	207.090	209.2345	210.7721	210.0685	211.1005	237.037

	expr	min	lq	mean	median	uq	max
2	rowRanks_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
1	colRanks_X_S	1.022712	1.025551	1.025775	1.026723	1.028559	0.9052189

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 3189260 170.4    5709258 305.0  5709258 305.0
Vcells 6183775  47.2   22343563 170.5 56666022 432.4
> 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 3189251 170.4    5709258 305.0  5709258 305.0
Vcells 6193853  47.3   22343563 170.5 56666022 432.4
> 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	0.082742	0.0842600	0.0857819	0.0855470	0.0868900	0.092634
2	colRanks(X, rows, cols)	0.085389	0.0873045	0.0888712	0.0878320	0.0884315	0.170394
3	colRanks(X[rows, cols])	0.093323	0.0946875	0.0961895	0.0960605	0.0971350	0.105325

	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)	1.031991	1.036132	1.036014	1.026711	1.017741	1.839433
3	colRanks(X[rows, cols])	1.127879	1.123754	1.121326	1.122897	1.117908	1.137001

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	0.075401	0.0769525	0.0786147	0.0781230	0.0791155	0.121670
2	rowRanks(X, cols, rows)	0.075996	0.0786775	0.0793945	0.0794095	0.0799530	0.086530
3	rowRanks(X[cols, rows])	0.084051	0.0855795	0.0866718	0.0865045	0.0872585	0.102024

	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)	1.007891	1.022416	1.009919	1.016468	1.010586	0.7111860
3	rowRanks(X[cols, rows])	1.114720	1.112108	1.102488	1.107286	1.102926	0.8385305

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	75.401	76.9525	78.61471	78.123	79.1155	121.670
1	colRanks_X_S	82.742	84.2600	85.78187	85.547	86.8900	92.634

	expr	min	lq	mean	median	uq	max
2	rowRanks_X_S	1.00000	1.000000	1.000000	1.00000	1.000000	1.0000000
1	colRanks_X_S	1.09736	1.094961	1.091168	1.09503	1.098268	0.7613545

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 3189459 170.4    5709258 305.0  5709258 305.0
Vcells 6229227  47.6   22343563 170.5 56666022 432.4
> 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 3189450 170.4    5709258 305.0  5709258 305.0
Vcells 6329305  48.3   22343563 170.5 56666022 432.4
> 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	1.477775	1.486052	1.543858	1.495405	1.600798	1.818087
2	colRanks(X, rows, cols)	1.497266	1.504472	1.552929	1.509940	1.620300	1.794940
3	colRanks(X[rows, cols])	1.631080	1.640476	1.786297	1.651414	1.855884	7.422274

	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)	1.013189	1.012395	1.005876	1.009720	1.012182	0.9872685
3	colRanks(X[rows, cols])	1.103740	1.103916	1.157035	1.104325	1.159349	4.0824636

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
2	rowRanks(X, cols, rows)	1.474042	1.481378	1.506420	1.489056	1.531859	1.604075
1	rowRanks_X_S	1.464498	1.471701	1.502593	1.516644	1.526038	1.575866
3	rowRanks(X[cols, rows])	1.559180	1.576128	1.639721	1.588039	1.708402	1.898545

	expr	min	lq	mean	median	uq	max
2	rowRanks(X, cols, rows)	1.0000000	1.0000000	1.000000	1.000000	1.0000000	1.0000000
1	rowRanks_X_S	0.9935253	0.9934676	0.997460	1.018527	0.9961997	0.9824142
3	rowRanks(X[cols, rows])	1.0577582	1.0639607	1.088489	1.066474	1.1152472	1.1835762

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	1.477775	1.486052	1.543858	1.495405	1.600798	1.818087
2	rowRanks_X_S	1.464498	1.471701	1.502593	1.516644	1.526038	1.575866

	expr	min	lq	mean	median	uq	max
1	colRanks_X_S	1.0000000	1.0000000	1.0000000	1.000000	1.0000000	1.0000000
2	rowRanks_X_S	0.9910155	0.9903429	0.9732721	1.014202	0.9532983	0.8667715

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 3189675 170.4    5709258 305.0  5709258 305.0
Vcells 6229374  47.6   22343563 170.5 56666022 432.4
> 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 3189666 170.4    5709258 305.0  5709258 305.0
Vcells 6329452  48.3   22343563 170.5 56666022 432.4
> 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	2.013273	2.073523	2.094985	2.078577	2.092253	2.226805
2	colRanks(X, rows, cols)	2.022983	2.080008	2.121242	2.085494	2.167508	2.623835
3	colRanks(X[rows, cols])	2.086775	2.142638	2.246980	2.147093	2.157801	8.187684

	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)	1.004823	1.003127	1.012533	1.003328	1.035969	1.178296
3	colRanks(X[rows, cols])	1.036509	1.033332	1.072552	1.032963	1.031329	3.676875

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
2	rowRanks(X, cols, rows)	2.023542	2.079500	2.104647	2.095444	2.126408	2.257241
1	rowRanks_X_S	2.030250	2.085611	2.113265	2.100188	2.133288	2.416232
3	rowRanks(X[cols, rows])	2.123181	2.177160	2.234303	2.183795	2.315200	2.404712

	expr	min	lq	mean	median	uq	max
2	rowRanks(X, cols, rows)	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	rowRanks_X_S	1.003315	1.002938	1.004095	1.002264	1.003235	1.070436
3	rowRanks(X[cols, rows])	1.049240	1.046963	1.061605	1.042164	1.088785	1.065332

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
1	colRanks_X_S	2.013273	2.073523	2.094985	2.078577	2.092253	2.226805
2	rowRanks_X_S	2.030250	2.085611	2.113265	2.100188	2.133288	2.416232

	expr	min	lq	mean	median	uq	max
1	colRanks_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowRanks_X_S	1.008433	1.005829	1.008725	1.010398	1.019613	1.085067

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 3.6.1 Patched (2019-08-27 r77078)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.3 LTS

Matrix products: default
BLAS:   /home/hb/software/R-devel/R-3-6-branch/lib/R/lib/libRblas.so
LAPACK: /home/hb/software/R-devel/R-3-6-branch/lib/R/lib/libRlapack.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] microbenchmark_1.4-6    matrixStats_0.55.0-9000 ggplot2_3.2.1          
[4] knitr_1.24              R.devices_2.16.0        R.utils_2.9.0          
[7] R.oo_1.22.0             R.methodsS3_1.7.1       history_0.0.0-9002     

loaded via a namespace (and not attached):
 [1] Biobase_2.45.0       bit64_0.9-7          splines_3.6.1       
 [4] network_1.15         assertthat_0.2.1     highr_0.8           
 [7] stats4_3.6.1         blob_1.2.0           robustbase_0.93-5   
[10] pillar_1.4.2         RSQLite_2.1.2        backports_1.1.4     
[13] lattice_0.20-38      glue_1.3.1           digest_0.6.20       
[16] colorspace_1.4-1     sandwich_2.5-1       Matrix_1.2-17       
[19] XML_3.98-1.20        lpSolve_5.6.13.3     pkgconfig_2.0.2     
[22] genefilter_1.66.0    purrr_0.3.2          ergm_3.10.4         
[25] xtable_1.8-4         mvtnorm_1.0-11       scales_1.0.0        
[28] tibble_2.1.3         annotate_1.62.0      IRanges_2.18.2      
[31] TH.data_1.0-10       withr_2.1.2          BiocGenerics_0.30.0 
[34] lazyeval_0.2.2       mime_0.7             survival_2.44-1.1   
[37] magrittr_1.5         crayon_1.3.4         statnet.common_4.3.0
[40] memoise_1.1.0        laeken_0.5.0         R.cache_0.13.0      
[43] MASS_7.3-51.4        R.rsp_0.43.1         tools_3.6.1         
[46] multcomp_1.4-10      S4Vectors_0.22.1     trust_0.1-7         
[49] munsell_0.5.0        AnnotationDbi_1.46.1 compiler_3.6.1      
[52] rlang_0.4.0          grid_3.6.1           RCurl_1.95-4.12     
[55] cwhmisc_6.6          rappdirs_0.3.1       labeling_0.3        
[58] bitops_1.0-6         base64enc_0.1-3      boot_1.3-23         
[61] gtable_0.3.0         codetools_0.2-16     DBI_1.0.0           
[64] markdown_1.1         R6_2.4.0             zoo_1.8-6           
[67] dplyr_0.8.3          bit_1.1-14           zeallot_0.1.0       
[70] parallel_3.6.1       Rcpp_1.0.2           vctrs_0.2.0         
[73] DEoptimR_1.0-8       tidyselect_0.2.5     xfun_0.9            
[76] coda_0.19-3         

Total processing time was 27.3 secs.

Reproducibility

To reproduce this report, do:

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

---

Copyright Dongcan Jiang. Last updated on 2019-09-10 20:51:58 (-0700 UTC). Powered by RSP.

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