colRowSums2_subset

matrixStats: Benchmark report

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colSums2() and rowSums2() benchmarks on subsetted computation

This report benchmark the performance of colSums2() and rowSums2() 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 3197358 170.8    5709258 305.0  5709258 305.0
Vcells 6445683  49.2   22343563 170.5 56666022 432.4
> colStats <- microbenchmark(colSums2_X_S = colSums2(X_S, na.rm = FALSE), `colSums2(X, rows, cols)` = colSums2(X, 
+     rows = rows, cols = cols, na.rm = FALSE), `colSums2(X[rows, cols])` = colSums2(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 3196131 170.7    5709258 305.0  5709258 305.0
Vcells 6442354  49.2   22343563 170.5 56666022 432.4
> rowStats <- microbenchmark(rowSums2_X_S = rowSums2(X_S, na.rm = FALSE), `rowSums2(X, cols, rows)` = rowSums2(X, 
+     rows = cols, cols = rows, na.rm = FALSE), `rowSums2(X[cols, rows])` = rowSums2(X[cols, rows], 
+     na.rm = FALSE), unit = "ms")

Table: Benchmarking of colSums2_X_S(), colSums2(X, rows, cols)() and colSums2(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	colSums2_X_S	0.001035	0.001066	0.0018599	0.0010845	0.0011400	0.074674
2	colSums2(X, rows, cols)	0.001170	0.001233	0.0012997	0.0012715	0.0013005	0.002546
3	colSums2(X[rows, cols])	0.001517	0.001661	0.0018492	0.0017720	0.0018835	0.005732

	expr	min	lq	mean	median	uq	max
1	colSums2_X_S	1.000000	1.000000	1.0000000	1.000000	1.000000	1.0000000
2	colSums2(X, rows, cols)	1.130435	1.156660	0.6988171	1.172430	1.140789	0.0340949
3	colSums2(X[rows, cols])	1.465701	1.558161	0.9942578	1.633933	1.652193	0.0767603

Table: Benchmarking of rowSums2_X_S(), rowSums2(X, cols, rows)() and rowSums2(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	rowSums2_X_S	0.001019	0.0010450	0.0011153	0.0010630	0.0010995	0.003100
2	rowSums2(X, cols, rows)	0.001180	0.0012170	0.0021450	0.0012405	0.0012955	0.087927
3	rowSums2(X[cols, rows])	0.001502	0.0017075	0.0018608	0.0017550	0.0019160	0.004949

	expr	min	lq	mean	median	uq	max
1	rowSums2_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowSums2(X, cols, rows)	1.157998	1.164593	1.923225	1.166980	1.178263	28.363548
3	rowSums2(X[cols, rows])	1.473994	1.633971	1.668415	1.650988	1.742610	1.596452

Figure: Benchmarking of colSums2_X_S(), colSums2(X, rows, cols)() and colSums2(X[rows, cols])() on integer+10x10 data as well as rowSums2_X_S(), rowSums2(X, cols, rows)() and rowSums2(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colSums2_X_S() and rowSums2_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	rowSums2_X_S	1.019	1.045	1.11534	1.0630	1.0995	3.100
1	colSums2_X_S	1.035	1.066	1.85990	1.0845	1.1400	74.674

	expr	min	lq	mean	median	uq	max
2	rowSums2_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.00000
1	colSums2_X_S	1.015702	1.020096	1.667563	1.020226	1.036835	24.08839

Figure: Benchmarking of colSums2_X_S() and rowSums2_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 3194737 170.7    5709258 305.0  5709258 305.0
Vcells 6111235  46.7   22343563 170.5 56666022 432.4
> colStats <- microbenchmark(colSums2_X_S = colSums2(X_S, na.rm = FALSE), `colSums2(X, rows, cols)` = colSums2(X, 
+     rows = rows, cols = cols, na.rm = FALSE), `colSums2(X[rows, cols])` = colSums2(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 3194728 170.7    5709258 305.0  5709258 305.0
Vcells 6116313  46.7   22343563 170.5 56666022 432.4
> rowStats <- microbenchmark(rowSums2_X_S = rowSums2(X_S, na.rm = FALSE), `rowSums2(X, cols, rows)` = rowSums2(X, 
+     rows = cols, cols = rows, na.rm = FALSE), `rowSums2(X[cols, rows])` = rowSums2(X[cols, rows], 
+     na.rm = FALSE), unit = "ms")

Table: Benchmarking of colSums2_X_S(), colSums2(X, rows, cols)() and colSums2(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	colSums2_X_S	0.005432	0.0058030	0.0059374	0.0058855	0.0060230	0.008229
2	colSums2(X, rows, cols)	0.006752	0.0072665	0.0074621	0.0074010	0.0075470	0.009545
3	colSums2(X[rows, cols])	0.013047	0.0134105	0.0139184	0.0135125	0.0136595	0.041372

	expr	min	lq	mean	median	uq	max
1	colSums2_X_S	1.000000	1.000000	1.000000	1.000000	1.00000	1.000000
2	colSums2(X, rows, cols)	1.243004	1.252197	1.256805	1.257497	1.25303	1.159922
3	colSums2(X[rows, cols])	2.401878	2.310960	2.344192	2.295897	2.26789	5.027585

Table: Benchmarking of rowSums2_X_S(), rowSums2(X, cols, rows)() and rowSums2(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	rowSums2_X_S	0.005461	0.0059245	0.0062030	0.0060805	0.006241	0.011569
2	rowSums2(X, cols, rows)	0.006495	0.0069330	0.0070705	0.0070375	0.007206	0.008143
3	rowSums2(X[cols, rows])	0.013174	0.0136170	0.0142560	0.0137660	0.013919	0.041166

	expr	min	lq	mean	median	uq	max
1	rowSums2_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	rowSums2(X, cols, rows)	1.189343	1.170225	1.139843	1.157388	1.154623	0.7038638
3	rowSums2(X[cols, rows])	2.412379	2.298422	2.298229	2.263959	2.230252	3.5583024

Figure: Benchmarking of colSums2_X_S(), colSums2(X, rows, cols)() and colSums2(X[rows, cols])() on integer+100x100 data as well as rowSums2_X_S(), rowSums2(X, cols, rows)() and rowSums2(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colSums2_X_S() and rowSums2_X_S() on integer+100x100 data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colSums2_X_S	5.432	5.8030	5.93739	5.8855	6.023	8.229
2	rowSums2_X_S	5.461	5.9245	6.20305	6.0805	6.241	11.569

	expr	min	lq	mean	median	uq	max
1	colSums2_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowSums2_X_S	1.005339	1.020937	1.044744	1.033132	1.036195	1.405882

Figure: Benchmarking of colSums2_X_S() and rowSums2_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 3195490 170.7    5709258 305.0  5709258 305.0
Vcells 6115306  46.7   22343563 170.5 56666022 432.4
> colStats <- microbenchmark(colSums2_X_S = colSums2(X_S, na.rm = FALSE), `colSums2(X, rows, cols)` = colSums2(X, 
+     rows = rows, cols = cols, na.rm = FALSE), `colSums2(X[rows, cols])` = colSums2(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 3195481 170.7    5709258 305.0  5709258 305.0
Vcells 6120384  46.7   22343563 170.5 56666022 432.4
> rowStats <- microbenchmark(rowSums2_X_S = rowSums2(X_S, na.rm = FALSE), `rowSums2(X, cols, rows)` = rowSums2(X, 
+     rows = cols, cols = rows, na.rm = FALSE), `rowSums2(X[cols, rows])` = rowSums2(X[cols, rows], 
+     na.rm = FALSE), unit = "ms")

Table: Benchmarking of colSums2_X_S(), colSums2(X, rows, cols)() and colSums2(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	colSums2_X_S	0.006711	0.0069925	0.0071730	0.0070970	0.0072185	0.009723
2	colSums2(X, rows, cols)	0.007772	0.0079840	0.0083783	0.0080865	0.0082790	0.031142
3	colSums2(X[rows, cols])	0.014392	0.0146925	0.0150488	0.0148230	0.0150795	0.023435

	expr	min	lq	mean	median	uq	max
1	colSums2_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	colSums2(X, rows, cols)	1.158099	1.141795	1.168035	1.139425	1.146914	3.202921
3	colSums2(X[rows, cols])	2.144539	2.101180	2.097980	2.088629	2.089007	2.410264

Table: Benchmarking of rowSums2_X_S(), rowSums2(X, cols, rows)() and rowSums2(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	rowSums2_X_S	0.006746	0.0070305	0.0075092	0.0071610	0.0073315	0.032304
2	rowSums2(X, cols, rows)	0.007753	0.0080340	0.0082928	0.0081565	0.0082985	0.017996
3	rowSums2(X[cols, rows])	0.015381	0.0158460	0.0162522	0.0160045	0.0162585	0.025505

	expr	min	lq	mean	median	uq	max
1	rowSums2_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	rowSums2(X, cols, rows)	1.149274	1.142735	1.104351	1.139017	1.131897	0.5570827
3	rowSums2(X[cols, rows])	2.280018	2.253894	2.164301	2.234953	2.217623	0.7895307

Figure: Benchmarking of colSums2_X_S(), colSums2(X, rows, cols)() and colSums2(X[rows, cols])() on integer+1000x10 data as well as rowSums2_X_S(), rowSums2(X, cols, rows)() and rowSums2(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colSums2_X_S() and rowSums2_X_S() on integer+1000x10 data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colSums2_X_S	6.711	6.9925	7.17302	7.097	7.2185	9.723
2	rowSums2_X_S	6.746	7.0305	7.50921	7.161	7.3315	32.304

	expr	min	lq	mean	median	uq	max
1	colSums2_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowSums2_X_S	1.005215	1.005434	1.046869	1.009018	1.015654	3.322431

Figure: Benchmarking of colSums2_X_S() and rowSums2_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 3195696 170.7    5709258 305.0  5709258 305.0
Vcells 6116129  46.7   22343563 170.5 56666022 432.4
> colStats <- microbenchmark(colSums2_X_S = colSums2(X_S, na.rm = FALSE), `colSums2(X, rows, cols)` = colSums2(X, 
+     rows = rows, cols = cols, na.rm = FALSE), `colSums2(X[rows, cols])` = colSums2(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 3195687 170.7    5709258 305.0  5709258 305.0
Vcells 6121207  46.8   22343563 170.5 56666022 432.4
> rowStats <- microbenchmark(rowSums2_X_S = rowSums2(X_S, na.rm = FALSE), `rowSums2(X, cols, rows)` = rowSums2(X, 
+     rows = cols, cols = rows, na.rm = FALSE), `rowSums2(X[cols, rows])` = rowSums2(X[cols, rows], 
+     na.rm = FALSE), unit = "ms")

Table: Benchmarking of colSums2_X_S(), colSums2(X, rows, cols)() and colSums2(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	colSums2_X_S	0.005439	0.0056395	0.0058294	0.0057710	0.005896	0.007870
2	colSums2(X, rows, cols)	0.007919	0.0081770	0.0087653	0.0083025	0.008490	0.048252
3	colSums2(X[rows, cols])	0.013958	0.0143835	0.0148283	0.0145720	0.014812	0.025299

	expr	min	lq	mean	median	uq	max
1	colSums2_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	colSums2(X, rows, cols)	1.455966	1.449951	1.503625	1.438659	1.439959	6.131131
3	colSums2(X[rows, cols])	2.566281	2.550492	2.543701	2.525039	2.512212	3.214612

Table: Benchmarking of rowSums2_X_S(), rowSums2(X, cols, rows)() and rowSums2(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	rowSums2_X_S	0.005446	0.0056410	0.0062585	0.0057940	0.0059285	0.042603
2	rowSums2(X, cols, rows)	0.007578	0.0077690	0.0079893	0.0078845	0.0080590	0.010186
3	rowSums2(X[cols, rows])	0.012853	0.0132495	0.0138788	0.0134000	0.0136290	0.030771

	expr	min	lq	mean	median	uq	max
1	rowSums2_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	rowSums2(X, cols, rows)	1.391480	1.377238	1.276537	1.360804	1.359366	0.2390911
3	rowSums2(X[cols, rows])	2.360081	2.348786	2.217586	2.312737	2.298895	0.7222731

Figure: Benchmarking of colSums2_X_S(), colSums2(X, rows, cols)() and colSums2(X[rows, cols])() on integer+10x1000 data as well as rowSums2_X_S(), rowSums2(X, cols, rows)() and rowSums2(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colSums2_X_S() and rowSums2_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
1	colSums2_X_S	5.439	5.6395	5.82942	5.771	5.8960	7.870
2	rowSums2_X_S	5.446	5.6410	6.25854	5.794	5.9285	42.603

	expr	min	lq	mean	median	uq	max
1	colSums2_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowSums2_X_S	1.001287	1.000266	1.073613	1.003985	1.005512	5.413342

Figure: Benchmarking of colSums2_X_S() and rowSums2_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 3195895 170.7    5709258 305.0  5709258 305.0
Vcells 6138787  46.9   22343563 170.5 56666022 432.4
> colStats <- microbenchmark(colSums2_X_S = colSums2(X_S, na.rm = FALSE), `colSums2(X, rows, cols)` = colSums2(X, 
+     rows = rows, cols = cols, na.rm = FALSE), `colSums2(X[rows, cols])` = colSums2(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 3195886 170.7    5709258 305.0  5709258 305.0
Vcells 6188865  47.3   22343563 170.5 56666022 432.4
> rowStats <- microbenchmark(rowSums2_X_S = rowSums2(X_S, na.rm = FALSE), `rowSums2(X, cols, rows)` = rowSums2(X, 
+     rows = cols, cols = rows, na.rm = FALSE), `rowSums2(X[cols, rows])` = rowSums2(X[cols, rows], 
+     na.rm = FALSE), unit = "ms")

Table: Benchmarking of colSums2_X_S(), colSums2(X, rows, cols)() and colSums2(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	colSums2_X_S	0.044255	0.0445630	0.0448186	0.0447360	0.0449230	0.047774
2	colSums2(X, rows, cols)	0.060130	0.0619820	0.0628707	0.0621915	0.0624515	0.131813
3	colSums2(X[rows, cols])	0.115757	0.1163415	0.1178107	0.1167395	0.1178850	0.134544

	expr	min	lq	mean	median	uq	max
1	colSums2_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	colSums2(X, rows, cols)	1.358717	1.390885	1.402781	1.390189	1.390190	2.759095
3	colSums2(X[rows, cols])	2.615682	2.610720	2.628611	2.609520	2.624157	2.816260

Table: Benchmarking of rowSums2_X_S(), rowSums2(X, cols, rows)() and rowSums2(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	rowSums2_X_S	0.046350	0.0467460	0.0482821	0.0469635	0.0472640	0.103522
2	rowSums2(X, cols, rows)	0.072971	0.0734845	0.0749694	0.0737825	0.0741945	0.140462
3	rowSums2(X[cols, rows])	0.109455	0.1105585	0.1134926	0.1113530	0.1130850	0.171994

	expr	min	lq	mean	median	uq	max
1	rowSums2_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowSums2(X, cols, rows)	1.574347	1.571996	1.552736	1.571060	1.569789	1.356832
3	rowSums2(X[cols, rows])	2.361489	2.365090	2.350614	2.371054	2.392624	1.661425

Figure: Benchmarking of colSums2_X_S(), colSums2(X, rows, cols)() and colSums2(X[rows, cols])() on integer+100x1000 data as well as rowSums2_X_S(), rowSums2(X, cols, rows)() and rowSums2(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colSums2_X_S() and rowSums2_X_S() on integer+100x1000 data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colSums2_X_S	44.255	44.563	44.81863	44.7360	44.923	47.774
2	rowSums2_X_S	46.350	46.746	48.28211	46.9635	47.264	103.522

	expr	min	lq	mean	median	uq	max
1	colSums2_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowSums2_X_S	1.047339	1.048987	1.077278	1.049792	1.052111	2.166911

Figure: Benchmarking of colSums2_X_S() and rowSums2_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 3196111 170.7    5709258 305.0  5709258 305.0
Vcells 6139567  46.9   22343563 170.5 56666022 432.4
> colStats <- microbenchmark(colSums2_X_S = colSums2(X_S, na.rm = FALSE), `colSums2(X, rows, cols)` = colSums2(X, 
+     rows = rows, cols = cols, na.rm = FALSE), `colSums2(X[rows, cols])` = colSums2(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 3196102 170.7    5709258 305.0  5709258 305.0
Vcells 6189645  47.3   22343563 170.5 56666022 432.4
> rowStats <- microbenchmark(rowSums2_X_S = rowSums2(X_S, na.rm = FALSE), `rowSums2(X, cols, rows)` = rowSums2(X, 
+     rows = cols, cols = rows, na.rm = FALSE), `rowSums2(X[cols, rows])` = rowSums2(X[cols, rows], 
+     na.rm = FALSE), unit = "ms")

Table: Benchmarking of colSums2_X_S(), colSums2(X, rows, cols)() and colSums2(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	colSums2_X_S	0.052243	0.0525990	0.0529400	0.052829	0.053070	0.057768
2	colSums2(X, rows, cols)	0.056656	0.0572155	0.0575972	0.057503	0.057793	0.061416
3	colSums2(X[rows, cols])	0.115427	0.1160940	0.1182083	0.116377	0.117003	0.205710

	expr	min	lq	mean	median	uq	max
1	colSums2_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	colSums2(X, rows, cols)	1.084471	1.087768	1.087970	1.088474	1.088996	1.063149
3	colSums2(X[rows, cols])	2.209425	2.207152	2.232872	2.202900	2.204692	3.560968

Table: Benchmarking of rowSums2_X_S(), rowSums2(X, cols, rows)() and rowSums2(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	rowSums2_X_S	0.065064	0.0692190	0.0721003	0.0702745	0.071174	0.115482
2	rowSums2(X, cols, rows)	0.092840	0.0965175	0.1038314	0.0992115	0.099822	0.260106
3	rowSums2(X[cols, rows])	0.153781	0.1610900	0.1711527	0.1642380	0.172786	0.281755

	expr	min	lq	mean	median	uq	max
1	rowSums2_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowSums2(X, cols, rows)	1.426903	1.394379	1.440096	1.411771	1.402507	2.252351
3	rowSums2(X[cols, rows])	2.363534	2.327251	2.373813	2.337092	2.427656	2.439818

Figure: Benchmarking of colSums2_X_S(), colSums2(X, rows, cols)() and colSums2(X[rows, cols])() on integer+1000x100 data as well as rowSums2_X_S(), rowSums2(X, cols, rows)() and rowSums2(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colSums2_X_S() and rowSums2_X_S() on integer+1000x100 data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colSums2_X_S	52.243	52.599	52.94004	52.8290	53.070	57.768
2	rowSums2_X_S	65.064	69.219	72.10032	70.2745	71.174	115.482

	expr	min	lq	mean	median	uq	max
1	colSums2_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowSums2_X_S	1.245411	1.315976	1.361924	1.330226	1.341134	1.999065

Figure: Benchmarking of colSums2_X_S() and rowSums2_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 3196329 170.8    5709258 305.0  5709258 305.0
Vcells 6230664  47.6   22343563 170.5 56666022 432.4
> colStats <- microbenchmark(colSums2_X_S = colSums2(X_S, na.rm = FALSE), `colSums2(X, rows, cols)` = colSums2(X, 
+     rows = rows, cols = cols, na.rm = FALSE), `colSums2(X[rows, cols])` = colSums2(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 3196311 170.8    5709258 305.0  5709258 305.0
Vcells 6230827  47.6   22343563 170.5 56666022 432.4
> rowStats <- microbenchmark(rowSums2_X_S = rowSums2(X_S, na.rm = FALSE), `rowSums2(X, cols, rows)` = rowSums2(X, 
+     rows = cols, cols = rows, na.rm = FALSE), `rowSums2(X[cols, rows])` = rowSums2(X[cols, rows], 
+     na.rm = FALSE), unit = "ms")

Table: Benchmarking of colSums2_X_S(), colSums2(X, rows, cols)() and colSums2(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	colSums2_X_S	0.001048	0.0010800	0.0013177	0.0010990	0.0011815	0.016339
2	colSums2(X, rows, cols)	0.001226	0.0012795	0.0014174	0.0013015	0.0013915	0.005124
3	colSums2(X[rows, cols])	0.001623	0.0018745	0.0020363	0.0019320	0.0020305	0.007173

	expr	min	lq	mean	median	uq	max
1	colSums2_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	colSums2(X, rows, cols)	1.169847	1.184722	1.075631	1.184258	1.177740	0.3136055
3	colSums2(X[rows, cols])	1.548664	1.735648	1.545343	1.757962	1.718578	0.4390110

Table: Benchmarking of rowSums2_X_S(), rowSums2(X, cols, rows)() and rowSums2(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	rowSums2_X_S	0.001026	0.0010550	0.0011601	0.0010805	0.0012100	0.003327
2	rowSums2(X, cols, rows)	0.001187	0.0012355	0.0015811	0.0012590	0.0013490	0.027926
3	rowSums2(X[cols, rows])	0.001505	0.0017420	0.0019006	0.0017880	0.0018955	0.006839

	expr	min	lq	mean	median	uq	max
1	rowSums2_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowSums2(X, cols, rows)	1.156920	1.171090	1.362856	1.165201	1.114876	8.393748
3	rowSums2(X[cols, rows])	1.466862	1.651185	1.638222	1.654789	1.566529	2.055606

Figure: Benchmarking of colSums2_X_S(), colSums2(X, rows, cols)() and colSums2(X[rows, cols])() on double+10x10 data as well as rowSums2_X_S(), rowSums2(X, cols, rows)() and rowSums2(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colSums2_X_S() and rowSums2_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	rowSums2_X_S	1.026	1.055	1.16013	1.0805	1.2100	3.327
1	colSums2_X_S	1.048	1.080	1.31772	1.0990	1.1815	16.339

	expr	min	lq	mean	median	uq	max
2	rowSums2_X_S	1.000000	1.000000	1.000000	1.000000	1.0000000	1.000000
1	colSums2_X_S	1.021443	1.023697	1.135838	1.017122	0.9764463	4.911031

Figure: Benchmarking of colSums2_X_S() and rowSums2_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 3196530 170.8    5709258 305.0  5709258 305.0
Vcells 6236610  47.6   22343563 170.5 56666022 432.4
> colStats <- microbenchmark(colSums2_X_S = colSums2(X_S, na.rm = FALSE), `colSums2(X, rows, cols)` = colSums2(X, 
+     rows = rows, cols = cols, na.rm = FALSE), `colSums2(X[rows, cols])` = colSums2(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 3196521 170.8    5709258 305.0  5709258 305.0
Vcells 6246688  47.7   22343563 170.5 56666022 432.4
> rowStats <- microbenchmark(rowSums2_X_S = rowSums2(X_S, na.rm = FALSE), `rowSums2(X, cols, rows)` = rowSums2(X, 
+     rows = cols, cols = rows, na.rm = FALSE), `rowSums2(X[cols, rows])` = rowSums2(X[cols, rows], 
+     na.rm = FALSE), unit = "ms")

Table: Benchmarking of colSums2_X_S(), colSums2(X, rows, cols)() and colSums2(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	colSums2_X_S	0.005751	0.0062130	0.0063442	0.0063030	0.0064105	0.009819
2	colSums2(X, rows, cols)	0.007573	0.0082465	0.0085612	0.0083970	0.0085255	0.023860
3	colSums2(X[rows, cols])	0.021516	0.0219135	0.0225538	0.0220415	0.0222065	0.054601

	expr	min	lq	mean	median	uq	max
1	colSums2_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	colSums2(X, rows, cols)	1.316814	1.327298	1.349457	1.332223	1.329927	2.429983
3	colSums2(X[rows, cols])	3.741262	3.527040	3.555020	3.496986	3.464082	5.560750

Table: Benchmarking of rowSums2_X_S(), rowSums2(X, cols, rows)() and rowSums2(X[cols, rows])() on double+100x100 data (transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	rowSums2_X_S	0.005822	0.0063485	0.0066357	0.0065035	0.0066975	0.013942
2	rowSums2(X, cols, rows)	0.007646	0.0080775	0.0082670	0.0082705	0.0083845	0.010039
3	rowSums2(X[cols, rows])	0.013164	0.0137735	0.0144908	0.0140090	0.0142765	0.056080

	expr	min	lq	mean	median	uq	max
1	rowSums2_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	rowSums2(X, cols, rows)	1.313294	1.272348	1.245838	1.271700	1.251885	0.7200545
3	rowSums2(X[cols, rows])	2.261079	2.169568	2.183781	2.154071	2.131616	4.0223784

Figure: Benchmarking of colSums2_X_S(), colSums2(X, rows, cols)() and colSums2(X[rows, cols])() on double+100x100 data as well as rowSums2_X_S(), rowSums2(X, cols, rows)() and rowSums2(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colSums2_X_S() and rowSums2_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	colSums2_X_S	5.751	6.2130	6.34421	6.3030	6.4105	9.819
2	rowSums2_X_S	5.822	6.3485	6.63566	6.5035	6.6975	13.942

	expr	min	lq	mean	median	uq	max
1	colSums2_X_S	1.000000	1.000000	1.00000	1.00000	1.00000	1.0000
2	rowSums2_X_S	1.012346	1.021809	1.04594	1.03181	1.04477	1.4199

Figure: Benchmarking of colSums2_X_S() and rowSums2_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 3196740 170.8    5709258 305.0  5709258 305.0
Vcells 6238016  47.6   22343563 170.5 56666022 432.4
> colStats <- microbenchmark(colSums2_X_S = colSums2(X_S, na.rm = FALSE), `colSums2(X, rows, cols)` = colSums2(X, 
+     rows = rows, cols = cols, na.rm = FALSE), `colSums2(X[rows, cols])` = colSums2(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 3196731 170.8    5709258 305.0  5709258 305.0
Vcells 6248094  47.7   22343563 170.5 56666022 432.4
> rowStats <- microbenchmark(rowSums2_X_S = rowSums2(X_S, na.rm = FALSE), `rowSums2(X, cols, rows)` = rowSums2(X, 
+     rows = cols, cols = rows, na.rm = FALSE), `rowSums2(X[cols, rows])` = rowSums2(X[cols, rows], 
+     na.rm = FALSE), unit = "ms")

Table: Benchmarking of colSums2_X_S(), colSums2(X, rows, cols)() and colSums2(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	colSums2_X_S	0.006733	0.0070935	0.0073250	0.0072105	0.0073500	0.010209
2	colSums2(X, rows, cols)	0.007890	0.0082095	0.0087692	0.0084055	0.0085800	0.043921
3	colSums2(X[rows, cols])	0.022668	0.0228990	0.0231386	0.0230505	0.0231465	0.029557

	expr	min	lq	mean	median	uq	max
1	colSums2_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	colSums2(X, rows, cols)	1.171840	1.157327	1.197152	1.165731	1.167347	4.302184
3	colSums2(X[rows, cols])	3.366701	3.228167	3.158856	3.196796	3.149184	2.895191

Table: Benchmarking of rowSums2_X_S(), rowSums2(X, cols, rows)() and rowSums2(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	rowSums2_X_S	0.006847	0.007174	0.0075666	0.0072990	0.0074375	0.024413
2	rowSums2(X, cols, rows)	0.008029	0.008363	0.0087446	0.0085465	0.0087715	0.022805
3	rowSums2(X[cols, rows])	0.015980	0.016389	0.0168253	0.0166000	0.0168715	0.029119

	expr	min	lq	mean	median	uq	max
1	rowSums2_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	rowSums2(X, cols, rows)	1.172630	1.165737	1.155671	1.170914	1.179361	0.9341335
3	rowSums2(X[cols, rows])	2.333869	2.284500	2.223621	2.274284	2.268437	1.1927661

Figure: Benchmarking of colSums2_X_S(), colSums2(X, rows, cols)() and colSums2(X[rows, cols])() on double+1000x10 data as well as rowSums2_X_S(), rowSums2(X, cols, rows)() and rowSums2(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colSums2_X_S() and rowSums2_X_S() on double+1000x10 data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colSums2_X_S	6.733	7.0935	7.32501	7.2105	7.3500	10.209
2	rowSums2_X_S	6.847	7.1740	7.56664	7.2990	7.4375	24.413

	expr	min	lq	mean	median	uq	max
1	colSums2_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowSums2_X_S	1.016932	1.011348	1.032987	1.012274	1.011905	2.391321

Figure: Benchmarking of colSums2_X_S() and rowSums2_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 3196946 170.8    5709258 305.0  5709258 305.0
Vcells 6238153  47.6   22343563 170.5 56666022 432.4
> colStats <- microbenchmark(colSums2_X_S = colSums2(X_S, na.rm = FALSE), `colSums2(X, rows, cols)` = colSums2(X, 
+     rows = rows, cols = cols, na.rm = FALSE), `colSums2(X[rows, cols])` = colSums2(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 3196937 170.8    5709258 305.0  5709258 305.0
Vcells 6248231  47.7   22343563 170.5 56666022 432.4
> rowStats <- microbenchmark(rowSums2_X_S = rowSums2(X_S, na.rm = FALSE), `rowSums2(X, cols, rows)` = rowSums2(X, 
+     rows = cols, cols = rows, na.rm = FALSE), `rowSums2(X[cols, rows])` = rowSums2(X[cols, rows], 
+     na.rm = FALSE), unit = "ms")

Table: Benchmarking of colSums2_X_S(), colSums2(X, rows, cols)() and colSums2(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	colSums2_X_S	0.007668	0.0079515	0.0081400	0.0080375	0.0081905	0.013034
2	colSums2(X, rows, cols)	0.009935	0.0102115	0.0108178	0.0104055	0.0105705	0.046167
3	colSums2(X[rows, cols])	0.016587	0.0170885	0.0173450	0.0172285	0.0174825	0.023320

	expr	min	lq	mean	median	uq	max
1	colSums2_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	colSums2(X, rows, cols)	1.295644	1.284223	1.328963	1.294619	1.290581	3.542044
3	colSums2(X[rows, cols])	2.163146	2.149091	2.130834	2.143515	2.134485	1.789167

Table: Benchmarking of rowSums2_X_S(), rowSums2(X, cols, rows)() and rowSums2(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	rowSums2_X_S	0.007668	0.0079165	0.0082255	0.0079875	0.0081200	0.024201
2	rowSums2(X, cols, rows)	0.009576	0.0098405	0.0100772	0.0099830	0.0101225	0.014620
3	rowSums2(X[cols, rows])	0.015401	0.0157295	0.0162166	0.0159225	0.0161545	0.031760

	expr	min	lq	mean	median	uq	max
1	rowSums2_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	rowSums2(X, cols, rows)	1.248826	1.243037	1.225110	1.249828	1.246613	0.6041073
3	rowSums2(X[cols, rows])	2.008477	1.986926	1.971497	1.993427	1.989470	1.3123425

Figure: Benchmarking of colSums2_X_S(), colSums2(X, rows, cols)() and colSums2(X[rows, cols])() on double+10x1000 data as well as rowSums2_X_S(), rowSums2(X, cols, rows)() and rowSums2(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colSums2_X_S() and rowSums2_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	rowSums2_X_S	7.668	7.9165	8.22553	7.9875	8.1200	24.201
1	colSums2_X_S	7.668	7.9515	8.14000	8.0375	8.1905	13.034

	expr	min	lq	mean	median	uq	max
2	rowSums2_X_S	1	1.000000	1.0000000	1.00000	1.000000	1.0000000
1	colSums2_X_S	1	1.004421	0.9896019	1.00626	1.008682	0.5385728

Figure: Benchmarking of colSums2_X_S() and rowSums2_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 3197145 170.8    5709258 305.0  5709258 305.0
Vcells 6283589  48.0   22343563 170.5 56666022 432.4
> colStats <- microbenchmark(colSums2_X_S = colSums2(X_S, na.rm = FALSE), `colSums2(X, rows, cols)` = colSums2(X, 
+     rows = rows, cols = cols, na.rm = FALSE), `colSums2(X[rows, cols])` = colSums2(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 3197136 170.8    5709258 305.0  5709258 305.0
Vcells 6383667  48.8   22343563 170.5 56666022 432.4
> rowStats <- microbenchmark(rowSums2_X_S = rowSums2(X_S, na.rm = FALSE), `rowSums2(X, cols, rows)` = rowSums2(X, 
+     rows = cols, cols = rows, na.rm = FALSE), `rowSums2(X[cols, rows])` = rowSums2(X[cols, rows], 
+     na.rm = FALSE), unit = "ms")

Table: Benchmarking of colSums2_X_S(), colSums2(X, rows, cols)() and colSums2(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	colSums2_X_S	0.047286	0.047713	0.0533056	0.0480610	0.0491315	0.098006
2	colSums2(X, rows, cols)	0.068441	0.068929	0.0806109	0.0691405	0.0700825	0.166081
3	colSums2(X[rows, cols])	0.194958	0.196620	0.2215189	0.1983200	0.2033505	0.398980

	expr	min	lq	mean	median	uq	max
1	colSums2_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	colSums2(X, rows, cols)	1.447384	1.444659	1.512241	1.438599	1.426427	1.694600
3	colSums2(X[rows, cols])	4.122954	4.120889	4.155640	4.126423	4.138903	4.070975

Table: Benchmarking of rowSums2_X_S(), rowSums2(X, cols, rows)() and rowSums2(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	rowSums2_X_S	0.054898	0.0599035	0.0642159	0.0632010	0.0658030	0.175677
2	rowSums2(X, cols, rows)	0.098190	0.1074955	0.1120233	0.1146325	0.1156095	0.172798
3	rowSums2(X[cols, rows])	0.134321	0.1497480	0.1604563	0.1582800	0.1666785	0.283316

	expr	min	lq	mean	median	uq	max
1	rowSums2_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowSums2(X, cols, rows)	1.788590	1.794478	1.744479	1.813777	1.756903	0.983612
3	rowSums2(X[cols, rows])	2.446738	2.499820	2.498700	2.504391	2.532992	1.612710

Figure: Benchmarking of colSums2_X_S(), colSums2(X, rows, cols)() and colSums2(X[rows, cols])() on double+100x1000 data as well as rowSums2_X_S(), rowSums2(X, cols, rows)() and rowSums2(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colSums2_X_S() and rowSums2_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	colSums2_X_S	47.286	47.7130	53.30559	48.061	49.1315	98.006
2	rowSums2_X_S	54.898	59.9035	64.21591	63.201	65.8030	175.677

	expr	min	lq	mean	median	uq	max
1	colSums2_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowSums2_X_S	1.160978	1.255496	1.204675	1.315016	1.339324	1.792513

Figure: Benchmarking of colSums2_X_S() and rowSums2_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 3197361 170.8    5709258 305.0  5709258 305.0
Vcells 6283736  48.0   22343563 170.5 56666022 432.4
> colStats <- microbenchmark(colSums2_X_S = colSums2(X_S, na.rm = FALSE), `colSums2(X, rows, cols)` = colSums2(X, 
+     rows = rows, cols = cols, na.rm = FALSE), `colSums2(X[rows, cols])` = colSums2(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 3197352 170.8    5709258 305.0  5709258 305.0
Vcells 6383814  48.8   22343563 170.5 56666022 432.4
> rowStats <- microbenchmark(rowSums2_X_S = rowSums2(X_S, na.rm = FALSE), `rowSums2(X, cols, rows)` = rowSums2(X, 
+     rows = cols, cols = rows, na.rm = FALSE), `rowSums2(X[cols, rows])` = rowSums2(X[cols, rows], 
+     na.rm = FALSE), unit = "ms")

Table: Benchmarking of colSums2_X_S(), colSums2(X, rows, cols)() and colSums2(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	colSums2_X_S	0.065700	0.0679140	0.0717249	0.0688030	0.070108	0.224654
2	colSums2(X, rows, cols)	0.076613	0.0775345	0.0824399	0.0797165	0.080192	0.179692
3	colSums2(X[rows, cols])	0.151623	0.1551100	0.1647623	0.1575745	0.159926	0.473810

	expr	min	lq	mean	median	uq	max
1	colSums2_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	colSums2(X, rows, cols)	1.166103	1.141657	1.149390	1.158619	1.143835	0.7998611
3	colSums2(X[rows, cols])	2.307808	2.283918	2.297142	2.290227	2.281138	2.1090655

Table: Benchmarking of rowSums2_X_S(), rowSums2(X, cols, rows)() and rowSums2(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	rowSums2_X_S	0.053853	0.0543835	0.0553172	0.054743	0.0551475	0.095029
2	rowSums2(X, cols, rows)	0.090008	0.0904435	0.0908911	0.090631	0.0909460	0.096691
3	rowSums2(X[cols, rows])	0.130907	0.1357240	0.1411041	0.139254	0.1423040	0.299043

	expr	min	lq	mean	median	uq	max
1	rowSums2_X_S	1.000000	1.000000	1.00000	1.000000	1.000000	1.000000
2	rowSums2(X, cols, rows)	1.671365	1.663069	1.64309	1.655572	1.649141	1.017489
3	rowSums2(X[cols, rows])	2.430821	2.495683	2.55082	2.543777	2.580425	3.146860

Figure: Benchmarking of colSums2_X_S(), colSums2(X, rows, cols)() and colSums2(X[rows, cols])() on double+1000x100 data as well as rowSums2_X_S(), rowSums2(X, cols, rows)() and rowSums2(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colSums2_X_S() and rowSums2_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	rowSums2_X_S	53.853	54.3835	55.31715	54.743	55.1475	95.029
1	colSums2_X_S	65.700	67.9140	71.72489	68.803	70.1080	224.654

	expr	min	lq	mean	median	uq	max
2	rowSums2_X_S	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	colSums2_X_S	1.219988	1.248798	1.296612	1.256836	1.271282	2.364057

Figure: Benchmarking of colSums2_X_S() and rowSums2_X_S() on double+1000x100 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

Appendix

Session information

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

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

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

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

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

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

Total processing time was 23.4 secs.

Reproducibility

To reproduce this report, do:

html <- matrixStats:::benchmark('colRowSums2_subset')

---

Copyright Henrik Bengtsson. Last updated on 2019-09-10 20:53:07 (-0700 UTC). Powered by RSP.

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