matrixStats.benchmarks

---

colCummins() and rowCummins() benchmarks

This report benchmark the performance of colCummins() and rowCummins() against alternative methods.

Alternative methods

• apply() + cummin()

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"]]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5198154 277.7    8529671 455.6  8529671 455.6
Vcells 9816353  74.9   31876688 243.2 60562128 462.1
> colStats <- microbenchmark(colCummins = colCummins(X), `apply+cummin` = apply(X, MARGIN = 2L, FUN = cummin), 
+     unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5189148 277.2    8529671 455.6  8529671 455.6
Vcells 9786783  74.7   31876688 243.2 60562128 462.1
> rowStats <- microbenchmark(rowCummins = rowCummins(X), `apply+cummin` = apply(X, MARGIN = 1L, FUN = cummin), 
+     unit = "ms")

Table: Benchmarking of colCummins() and apply+cummin() 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	colCummins	0.001886	0.0021910	0.0027902	0.0024685	0.0030985	0.012003
2	apply+cummin	0.044299	0.0450565	0.0465846	0.0455360	0.0459505	0.124705

	expr	min	lq	mean	median	uq	max
1	colCummins	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000
2	apply+cummin	23.48834	20.56435	16.69593	18.44683	14.82992	10.38949

Table: Benchmarking of rowCummins() and apply+cummin() 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	rowCummins	0.002059	0.0024045	0.0030497	0.0031610	0.003344	0.011764
2	apply+cummin	0.044494	0.0454810	0.0470173	0.0459775	0.046496	0.126300

	expr	min	lq	mean	median	uq	max
1	rowCummins	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000
2	apply+cummin	21.60952	18.91495	15.41704	14.54524	13.90431	10.73614

Figure: Benchmarking of colCummins() and apply+cummin() on integer+10x10 data as well as rowCummins() and apply+cummin() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

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

	expr	min	lq	mean	median	uq	max
1	colCummins	1.886	2.1910	2.79018	2.4685	3.0985	12.003
2	rowCummins	2.059	2.4045	3.04970	3.1610	3.3440	11.764

	expr	min	lq	mean	median	uq	max
1	colCummins	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	rowCummins	1.091729	1.097444	1.093012	1.280535	1.079232	0.9800883

Figure: Benchmarking of colCummins() and rowCummins() on integer+10x10 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

100x100 integer matrix

> X <- data[["100x100"]]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5187728 277.1    8529671 455.6  8529671 455.6
Vcells 9403305  71.8   31876688 243.2 60562128 462.1
> colStats <- microbenchmark(colCummins = colCummins(X), `apply+cummin` = apply(X, MARGIN = 2L, FUN = cummin), 
+     unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5187704 277.1    8529671 455.6  8529671 455.6
Vcells 9408318  71.8   31876688 243.2 60562128 462.1
> rowStats <- microbenchmark(rowCummins = rowCummins(X), `apply+cummin` = apply(X, MARGIN = 1L, FUN = cummin), 
+     unit = "ms")

Table: Benchmarking of colCummins() and apply+cummin() 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	colCummins	0.024733	0.0256655	0.0287101	0.027601	0.030879	0.049008
2	apply+cummin	0.242463	0.2534060	0.2830028	0.270452	0.304802	0.434979

	expr	min	lq	mean	median	uq	max
1	colCummins	1.000000	1.00000	1.000000	1.00000	1.000000	1.000000
2	apply+cummin	9.803218	9.87341	9.857254	9.79863	9.870851	8.875673

Table: Benchmarking of rowCummins() and apply+cummin() 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	rowCummins	0.032356	0.0346660	0.0378749	0.0366845	0.0405270	0.053534
2	apply+cummin	0.236244	0.2457645	0.2763660	0.2697430	0.2988075	0.453301

	expr	min	lq	mean	median	uq	max
1	rowCummins	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	apply+cummin	7.301397	7.089497	7.296813	7.353051	7.373048	8.467535

Figure: Benchmarking of colCummins() and apply+cummin() on integer+100x100 data as well as rowCummins() and apply+cummin() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colCummins() and rowCummins() 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	colCummins	24.733	25.6655	28.71010	27.6010	30.879	49.008
2	rowCummins	32.356	34.6660	37.87489	36.6845	40.527	53.534

	expr	min	lq	mean	median	uq	max
1	colCummins	1.000000	1.000000	1.000000	1.0000	1.000000	1.000000
2	rowCummins	1.308212	1.350685	1.319218	1.3291	1.312445	1.092352

Figure: Benchmarking of colCummins() and rowCummins() on integer+100x100 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

1000x10 integer matrix

> X <- data[["1000x10"]]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5188460 277.1    8529671 455.6  8529671 455.6
Vcells 9406815  71.8   31876688 243.2 60562128 462.1
> colStats <- microbenchmark(colCummins = colCummins(X), `apply+cummin` = apply(X, MARGIN = 2L, FUN = cummin), 
+     unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5188436 277.1    8529671 455.6  8529671 455.6
Vcells 9411828  71.9   31876688 243.2 60562128 462.1
> rowStats <- microbenchmark(rowCummins = rowCummins(X), `apply+cummin` = apply(X, MARGIN = 1L, FUN = cummin), 
+     unit = "ms")

Table: Benchmarking of colCummins() and apply+cummin() 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	colCummins	0.025385	0.0273175	0.0294291	0.029176	0.030712	0.051438
2	apply+cummin	0.151176	0.1623765	0.1747166	0.174761	0.186009	0.271083

	expr	min	lq	mean	median	uq	max
1	colCummins	1.000000	1.000000	1.00000	1.000000	1.000000	1.000000
2	apply+cummin	5.955328	5.944047	5.93686	5.989889	6.056558	5.270092

Table: Benchmarking of rowCummins() and apply+cummin() 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	rowCummins	0.036634	0.040133	0.0427804	0.0425475	0.0447395	0.076496
2	apply+cummin	0.149369	0.165891	0.1775126	0.1793380	0.1865220	0.287679

	expr	min	lq	mean	median	uq	max
1	rowCummins	1.000000	1.000000	1.00000	1.000000	1.000000	1.000000
2	apply+cummin	4.077332	4.133531	4.14939	4.215007	4.169068	3.760706

Figure: Benchmarking of colCummins() and apply+cummin() on integer+1000x10 data as well as rowCummins() and apply+cummin() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colCummins() and rowCummins() 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	colCummins	25.385	27.3175	29.42912	29.1760	30.7120	51.438
2	rowCummins	36.634	40.1330	42.78042	42.5475	44.7395	76.496

	expr	min	lq	mean	median	uq	max
1	colCummins	1.000000	1.000000	1.000000	1.000000	1.000000	1.00000
2	rowCummins	1.443136	1.469132	1.453677	1.458305	1.456743	1.48715

Figure: Benchmarking of colCummins() and rowCummins() on integer+1000x10 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

10x1000 integer matrix

> X <- data[["10x1000"]]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5188648 277.2    8529671 455.6  8529671 455.6
Vcells 9407502  71.8   31876688 243.2 60562128 462.1
> colStats <- microbenchmark(colCummins = colCummins(X), `apply+cummin` = apply(X, MARGIN = 2L, FUN = cummin), 
+     unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5188624 277.2    8529671 455.6  8529671 455.6
Vcells 9412515  71.9   31876688 243.2 60562128 462.1
> rowStats <- microbenchmark(rowCummins = rowCummins(X), `apply+cummin` = apply(X, MARGIN = 1L, FUN = cummin), 
+     unit = "ms")

Table: Benchmarking of colCummins() and apply+cummin() 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	colCummins	0.018638	0.0201440	0.0227576	0.021014	0.0241855	0.040666
2	apply+cummin	0.889760	0.9123055	1.0213205	0.972172	1.1060745	1.469159

	expr	min	lq	mean	median	uq	max
1	colCummins	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000
2	apply+cummin	47.73903	45.28919	44.87824	46.26306	45.73296	36.12745

Table: Benchmarking of rowCummins() and apply+cummin() 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	rowCummins	0.023915	0.0253565	0.0287733	0.027086	0.029738	0.050699
2	apply+cummin	0.874432	0.9180855	1.0328556	0.978348	1.124165	1.477055

	expr	min	lq	mean	median	uq	max
1	rowCummins	1.00000	1.00000	1.00000	1.00000	1.00000	1.00000
2	apply+cummin	36.56416	36.20711	35.89628	36.12006	37.80231	29.13381

Figure: Benchmarking of colCummins() and apply+cummin() on integer+10x1000 data as well as rowCummins() and apply+cummin() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colCummins() and rowCummins() 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	colCummins	18.638	20.1440	22.75759	21.014	24.1855	40.666
2	rowCummins	23.915	25.3565	28.77333	27.086	29.7380	50.699

	expr	min	lq	mean	median	uq	max
1	colCummins	1.000000	1.000000	1.00000	1.00000	1.00000	1.000000
2	rowCummins	1.283131	1.258762	1.26434	1.28895	1.22958	1.246717

Figure: Benchmarking of colCummins() and rowCummins() on integer+10x1000 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

100x1000 integer matrix

> X <- data[["100x1000"]]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5188810 277.2    8529671 455.6  8529671 455.6
Vcells 9407948  71.8   31876688 243.2 60562128 462.1
> colStats <- microbenchmark(colCummins = colCummins(X), `apply+cummin` = apply(X, MARGIN = 2L, FUN = cummin), 
+     unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5188804 277.2    8529671 455.6  8529671 455.6
Vcells 9457991  72.2   31876688 243.2 60562128 462.1
> rowStats <- microbenchmark(rowCummins = rowCummins(X), `apply+cummin` = apply(X, MARGIN = 1L, FUN = cummin), 
+     unit = "ms")

Table: Benchmarking of colCummins() and apply+cummin() 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	colCummins	0.178364	0.192117	0.2073618	0.200255	0.220669	0.307334
2	apply+cummin	1.649031	1.759540	2.0480233	1.835325	1.955503	15.951853

	expr	min	lq	mean	median	uq	max
1	colCummins	1.000000	1.000000	1.000000	1.00000	1.000000	1.00000
2	apply+cummin	9.245313	9.158687	9.876572	9.16494	8.861702	51.90396

Table: Benchmarking of rowCummins() and apply+cummin() 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	rowCummins	0.233481	0.2464885	0.2627796	0.254444	0.269249	0.403114
2	apply+cummin	1.682163	1.7761245	2.0652739	1.834779	1.987249	16.439867

	expr	min	lq	mean	median	uq	max
1	rowCummins	1.00000	1.000000	1.000000	1.000000	1.000000	1.00000
2	apply+cummin	7.20471	7.205709	7.859339	7.210933	7.380711	40.78218

Figure: Benchmarking of colCummins() and apply+cummin() on integer+100x1000 data as well as rowCummins() and apply+cummin() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colCummins() and rowCummins() 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	colCummins	178.364	192.1170	207.3618	200.255	220.669	307.334
2	rowCummins	233.481	246.4885	262.7796	254.444	269.249	403.114

	expr	min	lq	mean	median	uq	max
1	colCummins	1.000000	1.000000	1.000000	1.0000	1.000000	1.000000
2	rowCummins	1.309014	1.283012	1.267252	1.2706	1.220149	1.311648

Figure: Benchmarking of colCummins() and rowCummins() on integer+100x1000 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

1000x100 integer matrix

> X <- data[["1000x100"]]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5189027 277.2    8529671 455.6  8529671 455.6
Vcells 9408553  71.8   31876688 243.2 60562128 462.1
> colStats <- microbenchmark(colCummins = colCummins(X), `apply+cummin` = apply(X, MARGIN = 2L, FUN = cummin), 
+     unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5189003 277.2    8529671 455.6  8529671 455.6
Vcells 9458566  72.2   31876688 243.2 60562128 462.1
> rowStats <- microbenchmark(rowCummins = rowCummins(X), `apply+cummin` = apply(X, MARGIN = 1L, FUN = cummin), 
+     unit = "ms")

Table: Benchmarking of colCummins() and apply+cummin() 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	colCummins	0.174509	0.1822660	0.2074065	0.197533	0.2223985	0.333364
2	apply+cummin	0.905970	0.9472705	1.1705592	1.079732	1.2157880	7.502145

	expr	min	lq	mean	median	uq	max
1	colCummins	1.000000	1.000000	1.000000	1.000000	1.00000	1.00000
2	apply+cummin	5.191537	5.197187	5.643793	5.466084	5.46671	22.50436

Table: Benchmarking of rowCummins() and apply+cummin() 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	rowCummins	0.244375	0.2579560	0.2846074	0.272723	0.308065	0.409614
2	apply+cummin	0.917421	0.9672895	1.1625622	1.049845	1.191480	7.619652

	expr	min	lq	mean	median	uq	max
1	rowCummins	1.000000	1.000000	1.000000	1.000000	1.000000	1.00000
2	apply+cummin	3.754152	3.749824	4.084793	3.849492	3.867627	18.60203

Figure: Benchmarking of colCummins() and apply+cummin() on integer+1000x100 data as well as rowCummins() and apply+cummin() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colCummins() and rowCummins() 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	colCummins	174.509	182.266	207.4065	197.533	222.3985	333.364
2	rowCummins	244.375	257.956	284.6074	272.723	308.0650	409.614

	expr	min	lq	mean	median	uq	max
1	colCummins	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowCummins	1.400358	1.415272	1.372221	1.380645	1.385194	1.228729

Figure: Benchmarking of colCummins() and rowCummins() 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"]]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5189227 277.2    8529671 455.6  8529671 455.6
Vcells 9524276  72.7   31876688 243.2 60562128 462.1
> colStats <- microbenchmark(colCummins = colCummins(X), `apply+cummin` = apply(X, MARGIN = 2L, FUN = cummin), 
+     unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5189194 277.2    8529671 455.6  8529671 455.6
Vcells 9524374  72.7   31876688 243.2 60562128 462.1
> rowStats <- microbenchmark(rowCummins = rowCummins(X), `apply+cummin` = apply(X, MARGIN = 1L, FUN = cummin), 
+     unit = "ms")

Table: Benchmarking of colCummins() and apply+cummin() 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	colCummins	0.002177	0.0024525	0.0030570	0.0027830	0.003423	0.011925
2	apply+cummin	0.044764	0.0453260	0.0471603	0.0459335	0.046323	0.121587

	expr	min	lq	mean	median	uq	max
1	colCummins	1.00000	1.00000	1.000	1.00000	1.00000	1.00000
2	apply+cummin	20.56224	18.48155	15.427	16.50503	13.53287	10.19597

Table: Benchmarking of rowCummins() and apply+cummin() 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	rowCummins	0.001983	0.002371	0.0030468	0.0031530	0.0033100	0.014055
2	apply+cummin	0.042842	0.045173	0.0466742	0.0457285	0.0461585	0.119895

	expr	min	lq	mean	median	uq	max
1	rowCummins	1.00000	1.0000	1.00000	1.00000	1.00000	1.000000
2	apply+cummin	21.60464	19.0523	15.31934	14.50317	13.94517	8.530416

Figure: Benchmarking of colCummins() and apply+cummin() on double+10x10 data as well as rowCummins() and apply+cummin() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

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

	expr	min	lq	mean	median	uq	max
1	colCummins	2.177	2.4525	3.05700	2.783	3.423	11.925
2	rowCummins	1.983	2.3710	3.04675	3.153	3.310	14.055

	expr	min	lq	mean	median	uq	max
1	colCummins	1.0000000	1.0000000	1.000000	1.00000	1.000000	1.000000
2	rowCummins	0.9108865	0.9667686	0.996647	1.13295	0.966988	1.178616

Figure: Benchmarking of colCummins() and rowCummins() on double+10x10 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

100x100 double matrix

> X <- data[["100x100"]]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5189402 277.2    8529671 455.6  8529671 455.6
Vcells 9525083  72.7   31876688 243.2 60562128 462.1
> colStats <- microbenchmark(colCummins = colCummins(X), `apply+cummin` = apply(X, MARGIN = 2L, FUN = cummin), 
+     unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5189378 277.2    8529671 455.6  8529671 455.6
Vcells 9535096  72.8   31876688 243.2 60562128 462.1
> rowStats <- microbenchmark(rowCummins = rowCummins(X), `apply+cummin` = apply(X, MARGIN = 1L, FUN = cummin), 
+     unit = "ms")

Table: Benchmarking of colCummins() and apply+cummin() 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	colCummins	0.050157	0.0518250	0.0578719	0.0557995	0.062964	0.096468
2	apply+cummin	0.231659	0.2453715	0.2752217	0.2680900	0.300108	0.461173

	expr	min	lq	mean	median	uq	max
1	colCummins	1.000000	1.000000	1.000000	1.000000	1.000000	1.00000
2	apply+cummin	4.618677	4.734616	4.755709	4.804523	4.766343	4.78058

Table: Benchmarking of rowCummins() and apply+cummin() 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	rowCummins	0.029088	0.0312755	0.0348647	0.033629	0.0375210	0.053455
2	apply+cummin	0.230281	0.2432255	0.2759684	0.271336	0.2923345	0.442749

	expr	min	lq	mean	median	uq	max
1	rowCummins	1.000000	1.00000	1.000000	1.000000	1.000000	1.000000
2	apply+cummin	7.916701	7.77687	7.915408	8.068512	7.791224	8.282649

Figure: Benchmarking of colCummins() and apply+cummin() on double+100x100 data as well as rowCummins() and apply+cummin() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colCummins() and rowCummins() 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	rowCummins	29.088	31.2755	34.86471	33.6290	37.521	53.455
1	colCummins	50.157	51.8250	57.87185	55.7995	62.964	96.468

	expr	min	lq	mean	median	uq	max
2	rowCummins	1.000000	1.000000	1.000000	1.000000	1.0000	1.000000
1	colCummins	1.724319	1.657048	1.659898	1.659267	1.6781	1.804658

Figure: Benchmarking of colCummins() and rowCummins() on double+100x100 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

1000x10 double matrix

> X <- data[["1000x10"]]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5189594 277.2    8529671 455.6  8529671 455.6
Vcells 9525983  72.7   31876688 243.2 60562128 462.1
> colStats <- microbenchmark(colCummins = colCummins(X), `apply+cummin` = apply(X, MARGIN = 2L, FUN = cummin), 
+     unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5189570 277.2    8529671 455.6  8529671 455.6
Vcells 9535996  72.8   31876688 243.2 60562128 462.1
> rowStats <- microbenchmark(rowCummins = rowCummins(X), `apply+cummin` = apply(X, MARGIN = 1L, FUN = cummin), 
+     unit = "ms")

Table: Benchmarking of colCummins() and apply+cummin() 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	colCummins	0.046003	0.0486265	0.0526381	0.0519725	0.0558435	0.075975
2	apply+cummin	0.145842	0.1526555	0.1697425	0.1703740	0.1826430	0.268411

	expr	min	lq	mean	median	uq	max
1	colCummins	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	apply+cummin	3.170272	3.139348	3.224706	3.278157	3.270622	3.532886

Table: Benchmarking of rowCummins() and apply+cummin() 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	rowCummins	0.031482	0.034577	0.0369696	0.0364095	0.0396045	0.057190
2	apply+cummin	0.145639	0.157844	0.1731680	0.1706120	0.1871330	0.267424

	expr	min	lq	mean	median	uq	max
1	rowCummins	1.000000	1.000	1.000000	1.00000	1.000000	1.000000
2	apply+cummin	4.626104	4.565	4.684065	4.68592	4.725044	4.676062

Figure: Benchmarking of colCummins() and apply+cummin() on double+1000x10 data as well as rowCummins() and apply+cummin() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colCummins() and rowCummins() 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	rowCummins	31.482	34.5770	36.96960	36.4095	39.6045	57.190
1	colCummins	46.003	48.6265	52.63814	51.9725	55.8435	75.975

	expr	min	lq	mean	median	uq	max
2	rowCummins	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	colCummins	1.461248	1.406325	1.423822	1.427443	1.410029	1.328466

Figure: Benchmarking of colCummins() and rowCummins() on double+1000x10 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

10x1000 double matrix

> X <- data[["10x1000"]]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5189782 277.2    8529671 455.6  8529671 455.6
Vcells 9526108  72.7   31876688 243.2 60562128 462.1
> colStats <- microbenchmark(colCummins = colCummins(X), `apply+cummin` = apply(X, MARGIN = 2L, FUN = cummin), 
+     unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5189758 277.2    8529671 455.6  8529671 455.6
Vcells 9536121  72.8   31876688 243.2 60562128 462.1
> rowStats <- microbenchmark(rowCummins = rowCummins(X), `apply+cummin` = apply(X, MARGIN = 1L, FUN = cummin), 
+     unit = "ms")

Table: Benchmarking of colCummins() and apply+cummin() 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	colCummins	0.055189	0.0589240	0.0666508	0.062529	0.0730375	0.101868
2	apply+cummin	0.873355	0.9106935	0.9989185	0.954950	1.0559315	1.420541

	expr	min	lq	mean	median	uq	max
1	colCummins	1.0000	1.00000	1.00000	1.00000	1.00000	1.00000
2	apply+cummin	15.8248	15.45539	14.98735	15.27211	14.45739	13.94492

Table: Benchmarking of rowCummins() and apply+cummin() 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	rowCummins	0.021802	0.0232630	0.026568	0.0251415	0.027942	0.046176
2	apply+cummin	0.865968	0.9134995	1.005937	0.9485950	1.078388	1.386644

	expr	min	lq	mean	median	uq	max
1	rowCummins	1.00000	1.00000	1.00000	1.00000	1.0000	1.00000
2	apply+cummin	39.71966	39.26834	37.86274	37.73025	38.5938	30.02954

Figure: Benchmarking of colCummins() and apply+cummin() on double+10x1000 data as well as rowCummins() and apply+cummin() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colCummins() and rowCummins() 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	rowCummins	21.802	23.263	26.56798	25.1415	27.9420	46.176
1	colCummins	55.189	58.924	66.65079	62.5290	73.0375	101.868

	expr	min	lq	mean	median	uq	max
2	rowCummins	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	colCummins	2.531373	2.532949	2.508689	2.487083	2.613897	2.206081

Figure: Benchmarking of colCummins() and rowCummins() on double+10x1000 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

100x1000 double matrix

> X <- data[["100x1000"]]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5189944 277.2    8529671 455.6  8529671 455.6
Vcells 9527123  72.7   31876688 243.2 60562128 462.1
> colStats <- microbenchmark(colCummins = colCummins(X), `apply+cummin` = apply(X, MARGIN = 2L, FUN = cummin), 
+     unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5189938 277.2    8529671 455.6  8529671 455.6
Vcells 9627166  73.5   31876688 243.2 60562128 462.1
> rowStats <- microbenchmark(rowCummins = rowCummins(X), `apply+cummin` = apply(X, MARGIN = 1L, FUN = cummin), 
+     unit = "ms")

Table: Benchmarking of colCummins() and apply+cummin() 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	colCummins	0.378802	0.400746	0.4555599	0.4352395	0.489271	0.734539
2	apply+cummin	1.618510	1.707243	2.2195884	1.8661605	2.163435	13.735953

	expr	min	lq	mean	median	uq	max
1	colCummins	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000
2	apply+cummin	4.272707	4.260163	4.872221	4.287663	4.421751	18.7001

Table: Benchmarking of rowCummins() and apply+cummin() 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	rowCummins	0.208746	0.228693	0.2543664	0.247418	0.274127	0.376529
2	apply+cummin	1.628291	1.734182	2.2365019	1.950220	2.163453	14.515753

	expr	min	lq	mean	median	uq	max
1	rowCummins	1.000000	1.000000	1.000000	1.000000	1.000000	1.00000
2	apply+cummin	7.800346	7.583011	8.792443	7.882288	7.892154	38.55149

Figure: Benchmarking of colCummins() and apply+cummin() on double+100x1000 data as well as rowCummins() and apply+cummin() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colCummins() and rowCummins() 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
2	rowCummins	208.746	228.693	254.3664	247.4180	274.127	376.529
1	colCummins	378.802	400.746	455.5599	435.2395	489.271	734.539

	expr	min	lq	mean	median	uq	max
2	rowCummins	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	colCummins	1.814655	1.752332	1.790959	1.759126	1.784833	1.950816

Figure: Benchmarking of colCummins() and rowCummins() on double+100x1000 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

1000x100 double matrix

> X <- data[["1000x100"]]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5190161 277.2    8529671 455.6  8529671 455.6
Vcells 9527289  72.7   31876688 243.2 60562128 462.1
> colStats <- microbenchmark(colCummins = colCummins(X), `apply+cummin` = apply(X, MARGIN = 2L, FUN = cummin), 
+     unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5190137 277.2    8529671 455.6  8529671 455.6
Vcells 9627302  73.5   31876688 243.2 60562128 462.1
> rowStats <- microbenchmark(rowCummins = rowCummins(X), `apply+cummin` = apply(X, MARGIN = 1L, FUN = cummin), 
+     unit = "ms")

Table: Benchmarking of colCummins() and apply+cummin() 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	colCummins	0.343187	0.3731635	0.4179793	0.413587	0.446502	0.612018
2	apply+cummin	0.963508	1.0524870	1.2740686	1.161658	1.225065	6.679734

	expr	min	lq	mean	median	uq	max
1	colCummins	1.000000	1.000000	1.000000	1.00000	1.000000	1.00000
2	apply+cummin	2.807531	2.820445	3.048162	2.80874	2.743693	10.91428

Table: Benchmarking of rowCummins() and apply+cummin() 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	rowCummins	0.231058	0.2501835	0.3278522	0.2745775	0.2860575	5.789883
2	apply+cummin	1.011842	1.0951635	1.2619326	1.2010615	1.2805095	6.525997

	expr	min	lq	mean	median	uq	max
1	rowCummins	1.000000	1.000000	1.00000	1.000000	1.000000	1.000000
2	apply+cummin	4.379169	4.377441	3.84909	4.374217	4.476406	1.127138

Figure: Benchmarking of colCummins() and apply+cummin() on double+1000x100 data as well as rowCummins() and apply+cummin() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colCummins() and rowCummins() 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	rowCummins	231.058	250.1835	327.8522	274.5775	286.0575	5789.883
1	colCummins	343.187	373.1635	417.9793	413.5870	446.5020	612.018

	expr	min	lq	mean	median	uq	max
2	rowCummins	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
1	colCummins	1.485285	1.491559	1.274902	1.506267	1.560882	0.1057047

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

Appendix

Session information

R version 4.1.1 Patched (2021-08-10 r80727)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.5 LTS

Matrix products: default
BLAS:   /home/hb/software/R-devel/R-4-1-branch/lib/R/lib/libRblas.so
LAPACK: /home/hb/software/R-devel/R-4-1-branch/lib/R/lib/libRlapack.so

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

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

other attached packages:
[1] microbenchmark_1.4-7   matrixStats_0.60.1     ggplot2_3.3.5         
[4] knitr_1.33             R.devices_2.17.0       R.utils_2.10.1        
[7] R.oo_1.24.0            R.methodsS3_1.8.1-9001 history_0.0.1-9000    

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

Total processing time was 24.17 secs.

Reproducibility

To reproduce this report, do:

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

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Copyright Henrik Bengtsson. Last updated on 2021-08-25 18:53:07 (+0200 UTC). Powered by RSP.