matrixStats.benchmarks
colWeightedMeans() and rowWeightedMeans() benchmarks on subsetted computation
This report benchmark the performance of colWeightedMeans() and rowWeightedMeans() on subsetted computation.
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 = "double")
Results
10x10 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]
> w <- runif(nrow(X))
> w_S <- w[rows]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5327688 284.6 7916910 422.9 7916910 422.9
Vcells 10777907 82.3 33191153 253.3 53339345 407.0
> colStats <- microbenchmark(colWeightedMeans_X_w_S = colWeightedMeans(X_S, w = w_S, na.rm = FALSE),
+ `colWeightedMeans(X, w, rows, cols)` = colWeightedMeans(X, w = w, rows = rows, cols = cols, na.rm = FALSE),
+ `colWeightedMeans(X[rows, cols], w[rows])` = colWeightedMeans(X[rows, cols], w = w[rows], na.rm = FALSE),
+ unit = "ms")
> X <- t(X)
> X_S <- t(X_S)
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5328008 284.6 7916910 422.9 7916910 422.9
Vcells 10778913 82.3 33191153 253.3 53339345 407.0
> rowStats <- microbenchmark(rowWeightedMeans_X_w_S = rowWeightedMeans(X_S, w = w_S, na.rm = FALSE),
+ `rowWeightedMeans(X, w, cols, rows)` = rowWeightedMeans(X, w = w, rows = cols, cols = rows, na.rm = FALSE),
+ `rowWeightedMeans(X[cols, rows], w[rows])` = rowWeightedMeans(X[cols, rows], w = w[rows], na.rm = FALSE),
+ unit = "ms")
Table: Benchmarking of colWeightedMeans_X_w_S(), colWeightedMeans(X, w, rows, cols)() and colWeightedMeans(X[rows, cols], w[rows])() on 10x10 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | colWeightedMeans_X_w_S | 0.015171 | 0.0158180 | 0.0163423 | 0.0160910 | 0.016316 | 0.027558 |
| 3 | colWeightedMeans(X[rows, cols], w[rows]) | 0.017381 | 0.0179435 | 0.0185034 | 0.0181375 | 0.018412 | 0.034587 |
| 2 | colWeightedMeans(X, w, rows, cols) | 0.018913 | 0.0193955 | 0.0246758 | 0.0196560 | 0.019909 | 0.504739 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | colWeightedMeans_X_w_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 3 | colWeightedMeans(X[rows, cols], w[rows]) | 1.145673 | 1.134372 | 1.132238 | 1.127183 | 1.128463 | 1.255062 |
| 2 | colWeightedMeans(X, w, rows, cols) | 1.246655 | 1.226166 | 1.509933 | 1.221552 | 1.220213 | 18.315516 |
Table: Benchmarking of rowWeightedMeans_X_w_S(), rowWeightedMeans(X, w, cols, rows)() and rowWeightedMeans(X[cols, rows], w[rows])() on 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 | rowWeightedMeans_X_w_S | 0.019012 | 0.0201135 | 0.0215431 | 0.0203670 | 0.0214005 | 0.034597 |
| 3 | rowWeightedMeans(X[cols, rows], w[rows]) | 0.020468 | 0.0219490 | 0.0247819 | 0.0227865 | 0.0261800 | 0.057266 |
| 2 | rowWeightedMeans(X, w, cols, rows) | 0.022483 | 0.0239145 | 0.0323603 | 0.0243945 | 0.0277340 | 0.631384 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | rowWeightedMeans_X_w_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.00000 |
| 3 | rowWeightedMeans(X[cols, rows], w[rows]) | 1.076583 | 1.091257 | 1.150340 | 1.118795 | 1.223336 | 1.65523 |
| 2 | rowWeightedMeans(X, w, cols, rows) | 1.182569 | 1.188978 | 1.502122 | 1.197746 | 1.295951 | 18.24967 |
Figure: Benchmarking of colWeightedMeans_X_w_S(), colWeightedMeans(X, w, rows, cols)() and colWeightedMeans(X[rows, cols], w[rows])() on 10x10 data as well as rowWeightedMeans_X_w_S(), rowWeightedMeans(X, w, cols, rows)() and rowWeightedMeans(X[cols, rows], w[rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.
Table: Benchmarking of colWeightedMeans_X_w_S() and rowWeightedMeans_X_w_S() on 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 | colWeightedMeans_X_w_S | 15.171 | 15.8180 | 16.34232 | 16.091 | 16.3160 | 27.558 |
| 2 | rowWeightedMeans_X_w_S | 19.012 | 20.1135 | 21.54308 | 20.367 | 21.4005 | 34.597 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | colWeightedMeans_X_w_S | 1.00000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 2 | rowWeightedMeans_X_w_S | 1.25318 | 1.271558 | 1.318239 | 1.265739 | 1.311627 | 1.255425 |
Figure: Benchmarking of colWeightedMeans_X_w_S() and rowWeightedMeans_X_w_S() on 10x10 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.
100x100 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]
> w <- runif(nrow(X))
> w_S <- w[rows]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5327177 284.6 7916910 422.9 7916910 422.9
Vcells 10449688 79.8 33191153 253.3 53339345 407.0
> colStats <- microbenchmark(colWeightedMeans_X_w_S = colWeightedMeans(X_S, w = w_S, na.rm = FALSE),
+ `colWeightedMeans(X, w, rows, cols)` = colWeightedMeans(X, w = w, rows = rows, cols = cols, na.rm = FALSE),
+ `colWeightedMeans(X[rows, cols], w[rows])` = colWeightedMeans(X[rows, cols], w = w[rows], na.rm = FALSE),
+ unit = "ms")
> X <- t(X)
> X_S <- t(X_S)
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5327171 284.6 7916910 422.9 7916910 422.9
Vcells 10459771 79.9 33191153 253.3 53339345 407.0
> rowStats <- microbenchmark(rowWeightedMeans_X_w_S = rowWeightedMeans(X_S, w = w_S, na.rm = FALSE),
+ `rowWeightedMeans(X, w, cols, rows)` = rowWeightedMeans(X, w = w, rows = cols, cols = rows, na.rm = FALSE),
+ `rowWeightedMeans(X[cols, rows], w[rows])` = rowWeightedMeans(X[cols, rows], w = w[rows], na.rm = FALSE),
+ unit = "ms")
Table: Benchmarking of colWeightedMeans_X_w_S(), colWeightedMeans(X, w, rows, cols)() and colWeightedMeans(X[rows, cols], w[rows])() on 100x100 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | colWeightedMeans_X_w_S | 0.029270 | 0.0316210 | 0.0329605 | 0.0321040 | 0.033290 | 0.053265 |
| 3 | colWeightedMeans(X[rows, cols], w[rows]) | 0.046053 | 0.0494960 | 0.0507809 | 0.0501865 | 0.051381 | 0.073676 |
| 2 | colWeightedMeans(X, w, rows, cols) | 0.061366 | 0.0665215 | 0.0686374 | 0.0674395 | 0.069121 | 0.131850 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | colWeightedMeans_X_w_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 3 | colWeightedMeans(X[rows, cols], w[rows]) | 1.573386 | 1.565289 | 1.540660 | 1.563248 | 1.543437 | 1.383197 |
| 2 | colWeightedMeans(X, w, rows, cols) | 2.096549 | 2.103713 | 2.082418 | 2.100657 | 2.076329 | 2.475359 |
Table: Benchmarking of rowWeightedMeans_X_w_S(), rowWeightedMeans(X, w, cols, rows)() and rowWeightedMeans(X[cols, rows], w[rows])() on 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 | rowWeightedMeans_X_w_S | 0.074601 | 0.0780525 | 0.0887749 | 0.086913 | 0.098268 | 0.116026 |
| 3 | rowWeightedMeans(X[cols, rows], w[rows]) | 0.088196 | 0.0930250 | 0.1051482 | 0.102477 | 0.113152 | 0.204930 |
| 2 | rowWeightedMeans(X, w, cols, rows) | 0.102206 | 0.1074345 | 0.1196216 | 0.116328 | 0.130697 | 0.159276 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | rowWeightedMeans_X_w_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 3 | rowWeightedMeans(X[cols, rows], w[rows]) | 1.182236 | 1.191826 | 1.184436 | 1.179076 | 1.151463 | 1.766242 |
| 2 | rowWeightedMeans(X, w, cols, rows) | 1.370035 | 1.376439 | 1.347472 | 1.338442 | 1.330006 | 1.372761 |
Figure: Benchmarking of colWeightedMeans_X_w_S(), colWeightedMeans(X, w, rows, cols)() and colWeightedMeans(X[rows, cols], w[rows])() on 100x100 data as well as rowWeightedMeans_X_w_S(), rowWeightedMeans(X, w, cols, rows)() and rowWeightedMeans(X[cols, rows], w[rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.
Table: Benchmarking of colWeightedMeans_X_w_S() and rowWeightedMeans_X_w_S() on 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 | colWeightedMeans_X_w_S | 29.270 | 31.6210 | 32.96046 | 32.104 | 33.290 | 53.265 |
| 2 | rowWeightedMeans_X_w_S | 74.601 | 78.0525 | 88.77487 | 86.913 | 98.268 | 116.026 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | colWeightedMeans_X_w_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 2 | rowWeightedMeans_X_w_S | 2.548719 | 2.468375 | 2.693375 | 2.707233 | 2.951877 | 2.178278 |
Figure: Benchmarking of colWeightedMeans_X_w_S() and rowWeightedMeans_X_w_S() on 100x100 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.
1000x10 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]
> w <- runif(nrow(X))
> w_S <- w[rows]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5327927 284.6 7916910 422.9 7916910 422.9
Vcells 10455614 79.8 33191153 253.3 53339345 407.0
> colStats <- microbenchmark(colWeightedMeans_X_w_S = colWeightedMeans(X_S, w = w_S, na.rm = FALSE),
+ `colWeightedMeans(X, w, rows, cols)` = colWeightedMeans(X, w = w, rows = rows, cols = cols, na.rm = FALSE),
+ `colWeightedMeans(X[rows, cols], w[rows])` = colWeightedMeans(X[rows, cols], w = w[rows], na.rm = FALSE),
+ unit = "ms")
> X <- t(X)
> X_S <- t(X_S)
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5327921 284.6 7916910 422.9 7916910 422.9
Vcells 10465697 79.9 33191153 253.3 53339345 407.0
> rowStats <- microbenchmark(rowWeightedMeans_X_w_S = rowWeightedMeans(X_S, w = w_S, na.rm = FALSE),
+ `rowWeightedMeans(X, w, cols, rows)` = rowWeightedMeans(X, w = w, rows = cols, cols = rows, na.rm = FALSE),
+ `rowWeightedMeans(X[cols, rows], w[rows])` = rowWeightedMeans(X[cols, rows], w = w[rows], na.rm = FALSE),
+ unit = "ms")
Table: Benchmarking of colWeightedMeans_X_w_S(), colWeightedMeans(X, w, rows, cols)() and colWeightedMeans(X[rows, cols], w[rows])() on 1000x10 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | colWeightedMeans_X_w_S | 0.038916 | 0.0417615 | 0.0443260 | 0.0440560 | 0.0466310 | 0.058755 |
| 3 | colWeightedMeans(X[rows, cols], w[rows]) | 0.056890 | 0.0600515 | 0.0641121 | 0.0636985 | 0.0675890 | 0.080929 |
| 2 | colWeightedMeans(X, w, rows, cols) | 0.075096 | 0.0796090 | 0.0849957 | 0.0845720 | 0.0891025 | 0.156608 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | colWeightedMeans_X_w_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 3 | colWeightedMeans(X[rows, cols], w[rows]) | 1.461867 | 1.437963 | 1.446378 | 1.445853 | 1.449444 | 1.377398 |
| 2 | colWeightedMeans(X, w, rows, cols) | 1.929695 | 1.906277 | 1.917515 | 1.919648 | 1.910800 | 2.665441 |
Table: Benchmarking of rowWeightedMeans_X_w_S(), rowWeightedMeans(X, w, cols, rows)() and rowWeightedMeans(X[cols, rows], w[rows])() on 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 | rowWeightedMeans_X_w_S | 0.084563 | 0.0883740 | 0.0956230 | 0.0913750 | 0.1061175 | 0.119929 |
| 3 | rowWeightedMeans(X[cols, rows], w[rows]) | 0.102313 | 0.1069370 | 0.1178380 | 0.1142950 | 0.1300995 | 0.140903 |
| 2 | rowWeightedMeans(X, w, cols, rows) | 0.119662 | 0.1268015 | 0.1400416 | 0.1361275 | 0.1521595 | 0.237710 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | rowWeightedMeans_X_w_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 3 | rowWeightedMeans(X[cols, rows], w[rows]) | 1.209903 | 1.210050 | 1.232319 | 1.250835 | 1.225995 | 1.174887 |
| 2 | rowWeightedMeans(X, w, cols, rows) | 1.415063 | 1.434828 | 1.464517 | 1.489767 | 1.433877 | 1.982089 |
Figure: Benchmarking of colWeightedMeans_X_w_S(), colWeightedMeans(X, w, rows, cols)() and colWeightedMeans(X[rows, cols], w[rows])() on 1000x10 data as well as rowWeightedMeans_X_w_S(), rowWeightedMeans(X, w, cols, rows)() and rowWeightedMeans(X[cols, rows], w[rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.
Table: Benchmarking of colWeightedMeans_X_w_S() and rowWeightedMeans_X_w_S() on 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 | colWeightedMeans_X_w_S | 38.916 | 41.7615 | 44.32595 | 44.056 | 46.6310 | 58.755 |
| 2 | rowWeightedMeans_X_w_S | 84.563 | 88.3740 | 95.62305 | 91.375 | 106.1175 | 119.929 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | colWeightedMeans_X_w_S | 1.000000 | 1.00000 | 1.00000 | 1.000000 | 1.000000 | 1.000000 |
| 2 | rowWeightedMeans_X_w_S | 2.172962 | 2.11616 | 2.15727 | 2.074065 | 2.275686 | 2.041171 |
Figure: Benchmarking of colWeightedMeans_X_w_S() and rowWeightedMeans_X_w_S() on 1000x10 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.
10x1000 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]
> w <- runif(nrow(X))
> w_S <- w[rows]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5328139 284.6 7916910 422.9 7916910 422.9
Vcells 10454903 79.8 33191153 253.3 53339345 407.0
> colStats <- microbenchmark(colWeightedMeans_X_w_S = colWeightedMeans(X_S, w = w_S, na.rm = FALSE),
+ `colWeightedMeans(X, w, rows, cols)` = colWeightedMeans(X, w = w, rows = rows, cols = cols, na.rm = FALSE),
+ `colWeightedMeans(X[rows, cols], w[rows])` = colWeightedMeans(X[rows, cols], w = w[rows], na.rm = FALSE),
+ unit = "ms")
> X <- t(X)
> X_S <- t(X_S)
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5328133 284.6 7916910 422.9 7916910 422.9
Vcells 10464986 79.9 33191153 253.3 53339345 407.0
> rowStats <- microbenchmark(rowWeightedMeans_X_w_S = rowWeightedMeans(X_S, w = w_S, na.rm = FALSE),
+ `rowWeightedMeans(X, w, cols, rows)` = rowWeightedMeans(X, w = w, rows = cols, cols = rows, na.rm = FALSE),
+ `rowWeightedMeans(X[cols, rows], w[rows])` = rowWeightedMeans(X[cols, rows], w = w[rows], na.rm = FALSE),
+ unit = "ms")
Table: Benchmarking of colWeightedMeans_X_w_S(), colWeightedMeans(X, w, rows, cols)() and colWeightedMeans(X[rows, cols], w[rows])() on 10x1000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | colWeightedMeans_X_w_S | 0.027334 | 0.0291200 | 0.0317376 | 0.0305235 | 0.031303 | 0.134176 |
| 3 | colWeightedMeans(X[rows, cols], w[rows]) | 0.045773 | 0.0486295 | 0.0515463 | 0.0517670 | 0.052761 | 0.074724 |
| 2 | colWeightedMeans(X, w, rows, cols) | 0.063654 | 0.0672375 | 0.0704083 | 0.0716180 | 0.072744 | 0.080886 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | colWeightedMeans_X_w_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.0000000 |
| 3 | colWeightedMeans(X[rows, cols], w[rows]) | 1.674581 | 1.669969 | 1.624137 | 1.695972 | 1.685493 | 0.5569103 |
| 2 | colWeightedMeans(X, w, rows, cols) | 2.328748 | 2.308980 | 2.218446 | 2.346323 | 2.323867 | 0.6028351 |
Table: Benchmarking of rowWeightedMeans_X_w_S(), rowWeightedMeans(X, w, cols, rows)() and rowWeightedMeans(X[cols, rows], w[rows])() on 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 | rowWeightedMeans_X_w_S | 0.074186 | 0.0777535 | 0.0873818 | 0.0880925 | 0.0958035 | 0.109595 |
| 3 | rowWeightedMeans(X[cols, rows], w[rows]) | 0.087743 | 0.0916445 | 0.1014660 | 0.0984410 | 0.1137795 | 0.123370 |
| 2 | rowWeightedMeans(X, w, cols, rows) | 0.101177 | 0.1059545 | 0.1182516 | 0.1131520 | 0.1313915 | 0.207479 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | rowWeightedMeans_X_w_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 3 | rowWeightedMeans(X[cols, rows], w[rows]) | 1.182743 | 1.178654 | 1.161180 | 1.117473 | 1.187634 | 1.125690 |
| 2 | rowWeightedMeans(X, w, cols, rows) | 1.363829 | 1.362697 | 1.353274 | 1.284468 | 1.371469 | 1.893143 |
Figure: Benchmarking of colWeightedMeans_X_w_S(), colWeightedMeans(X, w, rows, cols)() and colWeightedMeans(X[rows, cols], w[rows])() on 10x1000 data as well as rowWeightedMeans_X_w_S(), rowWeightedMeans(X, w, cols, rows)() and rowWeightedMeans(X[cols, rows], w[rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.
Table: Benchmarking of colWeightedMeans_X_w_S() and rowWeightedMeans_X_w_S() on 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 | colWeightedMeans_X_w_S | 27.334 | 29.1200 | 31.73765 | 30.5235 | 31.3030 | 134.176 |
| 2 | rowWeightedMeans_X_w_S | 74.186 | 77.7535 | 87.38180 | 88.0925 | 95.8035 | 109.595 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | colWeightedMeans_X_w_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.0000000 |
| 2 | rowWeightedMeans_X_w_S | 2.714056 | 2.670107 | 2.753254 | 2.886055 | 3.060521 | 0.8168003 |
Figure: Benchmarking of colWeightedMeans_X_w_S() and rowWeightedMeans_X_w_S() on 10x1000 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.
100x1000 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]
> w <- runif(nrow(X))
> w_S <- w[rows]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5328347 284.6 7916910 422.9 7916910 422.9
Vcells 10499858 80.2 33191153 253.3 53339345 407.0
> colStats <- microbenchmark(colWeightedMeans_X_w_S = colWeightedMeans(X_S, w = w_S, na.rm = FALSE),
+ `colWeightedMeans(X, w, rows, cols)` = colWeightedMeans(X, w = w, rows = rows, cols = cols, na.rm = FALSE),
+ `colWeightedMeans(X[rows, cols], w[rows])` = colWeightedMeans(X[rows, cols], w = w[rows], na.rm = FALSE),
+ unit = "ms")
> X <- t(X)
> X_S <- t(X_S)
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5328341 284.6 7916910 422.9 7916910 422.9
Vcells 10599941 80.9 33191153 253.3 53339345 407.0
> rowStats <- microbenchmark(rowWeightedMeans_X_w_S = rowWeightedMeans(X_S, w = w_S, na.rm = FALSE),
+ `rowWeightedMeans(X, w, cols, rows)` = rowWeightedMeans(X, w = w, rows = cols, cols = rows, na.rm = FALSE),
+ `rowWeightedMeans(X[cols, rows], w[rows])` = rowWeightedMeans(X[cols, rows], w = w[rows], na.rm = FALSE),
+ unit = "ms")
Table: Benchmarking of colWeightedMeans_X_w_S(), colWeightedMeans(X, w, rows, cols)() and colWeightedMeans(X[rows, cols], w[rows])() on 100x1000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | colWeightedMeans_X_w_S | 0.108428 | 0.1168360 | 0.1528727 | 0.1272540 | 0.2098625 | 0.259869 |
| 3 | colWeightedMeans(X[rows, cols], w[rows]) | 0.227391 | 0.2393220 | 0.3090473 | 0.2617915 | 0.4170575 | 0.500458 |
| 2 | colWeightedMeans(X, w, rows, cols) | 0.343611 | 0.3620755 | 0.5765176 | 0.4073580 | 0.6181440 | 10.841309 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | colWeightedMeans_X_w_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 3 | colWeightedMeans(X[rows, cols], w[rows]) | 2.097161 | 2.048358 | 2.021600 | 2.057236 | 1.987289 | 1.925809 |
| 2 | colWeightedMeans(X, w, rows, cols) | 3.169025 | 3.099006 | 3.771227 | 3.201141 | 2.945471 | 41.718362 |
Table: Benchmarking of rowWeightedMeans_X_w_S(), rowWeightedMeans(X, w, cols, rows)() and rowWeightedMeans(X[cols, rows], w[rows])() on 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 | rowWeightedMeans_X_w_S | 0.467192 | 0.5749695 | 0.7012377 | 0.5907415 | 0.6168725 | 10.128518 |
| 3 | rowWeightedMeans(X[cols, rows], w[rows]) | 0.560519 | 0.7025095 | 0.7616442 | 0.7902015 | 0.8076905 | 1.122994 |
| 2 | rowWeightedMeans(X, w, cols, rows) | 0.655268 | 0.8022210 | 0.9124271 | 0.9097415 | 1.0074615 | 1.574210 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | rowWeightedMeans_X_w_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.0000000 |
| 3 | rowWeightedMeans(X[cols, rows], w[rows]) | 1.199762 | 1.221820 | 1.086143 | 1.337643 | 1.309331 | 0.1108745 |
| 2 | rowWeightedMeans(X, w, cols, rows) | 1.402567 | 1.395241 | 1.301167 | 1.539999 | 1.633176 | 0.1554235 |
Figure: Benchmarking of colWeightedMeans_X_w_S(), colWeightedMeans(X, w, rows, cols)() and colWeightedMeans(X[rows, cols], w[rows])() on 100x1000 data as well as rowWeightedMeans_X_w_S(), rowWeightedMeans(X, w, cols, rows)() and rowWeightedMeans(X[cols, rows], w[rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.
Table: Benchmarking of colWeightedMeans_X_w_S() and rowWeightedMeans_X_w_S() on 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 | colWeightedMeans_X_w_S | 108.428 | 116.8360 | 152.8727 | 127.2540 | 209.8625 | 259.869 |
| 2 | rowWeightedMeans_X_w_S | 467.192 | 574.9695 | 701.2377 | 590.7415 | 616.8725 | 10128.518 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | colWeightedMeans_X_w_S | 1.000000 | 1.000000 | 1.00000 | 1.000000 | 1.000000 | 1.00000 |
| 2 | rowWeightedMeans_X_w_S | 4.308776 | 4.921167 | 4.58707 | 4.642223 | 2.939413 | 38.97548 |
Figure: Benchmarking of colWeightedMeans_X_w_S() and rowWeightedMeans_X_w_S() on 100x1000 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.
1000x100 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]
> w <- runif(nrow(X))
> w_S <- w[rows]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5328553 284.6 7916910 422.9 7916910 422.9
Vcells 10502170 80.2 33191153 253.3 53339345 407.0
> colStats <- microbenchmark(colWeightedMeans_X_w_S = colWeightedMeans(X_S, w = w_S, na.rm = FALSE),
+ `colWeightedMeans(X, w, rows, cols)` = colWeightedMeans(X, w = w, rows = rows, cols = cols, na.rm = FALSE),
+ `colWeightedMeans(X[rows, cols], w[rows])` = colWeightedMeans(X[rows, cols], w = w[rows], na.rm = FALSE),
+ unit = "ms")
> X <- t(X)
> X_S <- t(X_S)
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5328547 284.6 7916910 422.9 7916910 422.9
Vcells 10602253 80.9 33191153 253.3 53339345 407.0
> rowStats <- microbenchmark(rowWeightedMeans_X_w_S = rowWeightedMeans(X_S, w = w_S, na.rm = FALSE),
+ `rowWeightedMeans(X, w, cols, rows)` = rowWeightedMeans(X, w = w, rows = cols, cols = rows, na.rm = FALSE),
+ `rowWeightedMeans(X[cols, rows], w[rows])` = rowWeightedMeans(X[cols, rows], w = w[rows], na.rm = FALSE),
+ unit = "ms")
Table: Benchmarking of colWeightedMeans_X_w_S(), colWeightedMeans(X, w, rows, cols)() and colWeightedMeans(X[rows, cols], w[rows])() on 1000x100 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | colWeightedMeans_X_w_S | 0.135671 | 0.1487570 | 0.1959958 | 0.171817 | 0.2450590 | 0.324342 |
| 3 | colWeightedMeans(X[rows, cols], w[rows]) | 0.257533 | 0.2677835 | 0.4755548 | 0.315645 | 0.4969675 | 10.618271 |
| 2 | colWeightedMeans(X, w, rows, cols) | 0.380244 | 0.4110905 | 0.5569525 | 0.481159 | 0.7341595 | 0.925653 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | colWeightedMeans_X_w_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 3 | colWeightedMeans(X[rows, cols], w[rows]) | 1.898217 | 1.800140 | 2.426353 | 1.837100 | 2.027950 | 32.737885 |
| 2 | colWeightedMeans(X, w, rows, cols) | 2.802692 | 2.763504 | 2.841656 | 2.800416 | 2.995848 | 2.853941 |
Table: Benchmarking of rowWeightedMeans_X_w_S(), rowWeightedMeans(X, w, cols, rows)() and rowWeightedMeans(X[cols, rows], w[rows])() on 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 | rowWeightedMeans_X_w_S | 0.472588 | 0.5570490 | 0.5858299 | 0.591656 | 0.6018410 | 0.793143 |
| 3 | rowWeightedMeans(X[cols, rows], w[rows]) | 0.570157 | 0.6293985 | 0.7271596 | 0.721855 | 0.8063925 | 1.541236 |
| 2 | rowWeightedMeans(X, w, cols, rows) | 0.668604 | 0.7962930 | 0.9796982 | 0.917700 | 1.0258700 | 7.592066 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | rowWeightedMeans_X_w_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 3 | rowWeightedMeans(X[cols, rows], w[rows]) | 1.206457 | 1.129880 | 1.241247 | 1.220059 | 1.339876 | 1.943201 |
| 2 | rowWeightedMeans(X, w, cols, rows) | 1.414771 | 1.429485 | 1.672325 | 1.551070 | 1.704553 | 9.572128 |
Figure: Benchmarking of colWeightedMeans_X_w_S(), colWeightedMeans(X, w, rows, cols)() and colWeightedMeans(X[rows, cols], w[rows])() on 1000x100 data as well as rowWeightedMeans_X_w_S(), rowWeightedMeans(X, w, cols, rows)() and rowWeightedMeans(X[cols, rows], w[rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.
Table: Benchmarking of colWeightedMeans_X_w_S() and rowWeightedMeans_X_w_S() on 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 | colWeightedMeans_X_w_S | 135.671 | 148.757 | 195.9958 | 171.817 | 245.059 | 324.342 |
| 2 | rowWeightedMeans_X_w_S | 472.588 | 557.049 | 585.8299 | 591.656 | 601.841 | 793.143 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | colWeightedMeans_X_w_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 2 | rowWeightedMeans_X_w_S | 3.483338 | 3.744691 | 2.988993 | 3.443524 | 2.455903 | 2.445391 |
Figure: Benchmarking of colWeightedMeans_X_w_S() and rowWeightedMeans_X_w_S() on 1000x100 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.
Appendix
Session information
R version 4.1.1 Patched (2021-08-10 r80727)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.5 LTS
Matrix products: default
BLAS: /home/hb/software/R-devel/R-4-1-branch/lib/R/lib/libRblas.so
LAPACK: /home/hb/software/R-devel/R-4-1-branch/lib/R/lib/libRlapack.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] microbenchmark_1.4-7 matrixStats_0.60.0 ggplot2_3.3.5
[4] knitr_1.33 R.devices_2.17.0 R.utils_2.10.1
[7] R.oo_1.24.0 R.methodsS3_1.8.1-9001 history_0.0.1-9000
loaded via a namespace (and not attached):
[1] Biobase_2.52.0 httr_1.4.2 splines_4.1.1
[4] bit64_4.0.5 network_1.17.1 assertthat_0.2.1
[7] highr_0.9 stats4_4.1.1 blob_1.2.2
[10] GenomeInfoDbData_1.2.6 robustbase_0.93-8 pillar_1.6.2
[13] RSQLite_2.2.8 lattice_0.20-44 glue_1.4.2
[16] digest_0.6.27 XVector_0.32.0 colorspace_2.0-2
[19] Matrix_1.3-4 XML_3.99-0.7 pkgconfig_2.0.3
[22] zlibbioc_1.38.0 genefilter_1.74.0 purrr_0.3.4
[25] ergm_4.1.2 xtable_1.8-4 scales_1.1.1
[28] tibble_3.1.4 annotate_1.70.0 KEGGREST_1.32.0
[31] farver_2.1.0 generics_0.1.0 IRanges_2.26.0
[34] ellipsis_0.3.2 cachem_1.0.6 withr_2.4.2
[37] BiocGenerics_0.38.0 mime_0.11 survival_3.2-13
[40] magrittr_2.0.1 crayon_1.4.1 statnet.common_4.5.0
[43] memoise_2.0.0 laeken_0.5.1 fansi_0.5.0
[46] R.cache_0.15.0 MASS_7.3-54 R.rsp_0.44.0
[49] progressr_0.8.0 tools_4.1.1 lifecycle_1.0.0
[52] S4Vectors_0.30.0 trust_0.1-8 munsell_0.5.0
[55] tabby_0.0.1-9001 AnnotationDbi_1.54.1 Biostrings_2.60.2
[58] compiler_4.1.1 GenomeInfoDb_1.28.1 rlang_0.4.11
[61] grid_4.1.1 RCurl_1.98-1.4 cwhmisc_6.6
[64] rstudioapi_0.13 rappdirs_0.3.3 startup_0.15.0
[67] labeling_0.4.2 bitops_1.0-7 base64enc_0.1-3
[70] boot_1.3-28 gtable_0.3.0 DBI_1.1.1
[73] markdown_1.1 R6_2.5.1 lpSolveAPI_5.5.2.0-17.7
[76] rle_0.9.2 dplyr_1.0.7 fastmap_1.1.0
[79] bit_4.0.4 utf8_1.2.2 parallel_4.1.1
[82] Rcpp_1.0.7 vctrs_0.3.8 png_0.1-7
[85] DEoptimR_1.0-9 tidyselect_1.1.1 xfun_0.25
[88] coda_0.19-4
Total processing time was 13.02 secs.
Reproducibility
To reproduce this report, do:
html <- matrixStats:::benchmark('colRowWeightedMeans_subset')
Copyright Dongcan Jiang. Last updated on 2021-08-25 22:31:53 (+0200 UTC). Powered by RSP.