colRowQuantiles_subset - HenrikBengtsson/matrixStats GitHub Wiki

matrixStats: Benchmark report

colQuantiles() and rowQuantiles() benchmarks on subsetted computation

This report benchmark the performance of colQuantiles() and rowQuantiles() 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]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3175014 169.6    5709258 305.0  5709258 305.0
Vcells 6396981  48.9   22343563 170.5 56666022 432.4
> probs <- seq(from = 0, to = 1, by = 0.25)
> colStats <- microbenchmark(colQuantiles_X_S = colQuantiles(X_S, probs = probs, na.rm = FALSE), `colQuantiles(X, rows, cols)` = colQuantiles(X, 
+     rows = rows, cols = cols, probs = probs, na.rm = FALSE), `colQuantiles(X[rows, cols])` = colQuantiles(X[rows, 
+     cols], probs = probs, na.rm = FALSE), unit = "ms")
> X <- t(X)
> X_S <- t(X_S)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3174277 169.6    5709258 305.0  5709258 305.0
Vcells 6394473  48.8   22343563 170.5 56666022 432.4
> rowStats <- microbenchmark(rowQuantiles_X_S = rowQuantiles(X_S, probs = probs, na.rm = FALSE), `rowQuantiles(X, cols, rows)` = rowQuantiles(X, 
+     rows = cols, cols = rows, probs = probs, na.rm = FALSE), `rowQuantiles(X[cols, rows])` = rowQuantiles(X[cols, 
+     rows], probs = probs, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colQuantiles_X_S(), colQuantiles(X, rows, cols)() and colQuantiles(X[rows, cols])() 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 colQuantiles_X_S 0.159063 0.1612190 0.1679418 0.1621265 0.1632945 0.678460
3 colQuantiles(X[rows, cols]) 0.160790 0.1627480 0.1645033 0.1638575 0.1652030 0.179393
2 colQuantiles(X, rows, cols) 0.160587 0.1633335 0.1651102 0.1643465 0.1662740 0.179827
expr min lq mean median uq max
1 colQuantiles_X_S 1.000000 1.000000 1.0000000 1.000000 1.000000 1.0000000
3 colQuantiles(X[rows, cols]) 1.010857 1.009484 0.9795252 1.010677 1.011688 0.2644121
2 colQuantiles(X, rows, cols) 1.009581 1.013116 0.9831392 1.013693 1.018246 0.2650517

Table: Benchmarking of rowQuantiles_X_S(), rowQuantiles(X, cols, rows)() and rowQuantiles(X[cols, 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 rowQuantiles_X_S 0.160542 0.1631545 0.1649297 0.1641865 0.1662660 0.178008
3 rowQuantiles(X[cols, rows]) 0.162827 0.1643365 0.1673207 0.1654305 0.1669935 0.282405
2 rowQuantiles(X, cols, rows) 0.162740 0.1647915 0.1721287 0.1658600 0.1675075 0.673119
expr min lq mean median uq max
1 rowQuantiles_X_S 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
3 rowQuantiles(X[cols, rows]) 1.014233 1.007245 1.014497 1.007577 1.004376 1.586474
2 rowQuantiles(X, cols, rows) 1.013691 1.010033 1.043648 1.010193 1.007467 3.781398

Figure: Benchmarking of colQuantiles_X_S(), colQuantiles(X, rows, cols)() and colQuantiles(X[rows, cols])() on 10x10 data as well as rowQuantiles_X_S(), rowQuantiles(X, cols, rows)() and rowQuantiles(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colQuantiles_X_S() and rowQuantiles_X_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 colQuantiles_X_S 159.063 161.2190 167.9418 162.1265 163.2945 678.460
2 rowQuantiles_X_S 160.542 163.1545 164.9298 164.1865 166.2660 178.008
expr min lq mean median uq max
1 colQuantiles_X_S 1.000000 1.000000 1.0000000 1.000000 1.000000 1.0000000
2 rowQuantiles_X_S 1.009298 1.012005 0.9820646 1.012706 1.018197 0.2623707

Figure: Benchmarking of colQuantiles_X_S() and rowQuantiles_X_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]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3173822 169.6    5709258 305.0  5709258 305.0
Vcells 6067332  46.3   22343563 170.5 56666022 432.4
> probs <- seq(from = 0, to = 1, by = 0.25)
> colStats <- microbenchmark(colQuantiles_X_S = colQuantiles(X_S, probs = probs, na.rm = FALSE), `colQuantiles(X, rows, cols)` = colQuantiles(X, 
+     rows = rows, cols = cols, probs = probs, na.rm = FALSE), `colQuantiles(X[rows, cols])` = colQuantiles(X[rows, 
+     cols], probs = probs, na.rm = FALSE), unit = "ms")
> X <- t(X)
> X_S <- t(X_S)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3173813 169.5    5709258 305.0  5709258 305.0
Vcells 6077410  46.4   22343563 170.5 56666022 432.4
> rowStats <- microbenchmark(rowQuantiles_X_S = rowQuantiles(X_S, probs = probs, na.rm = FALSE), `rowQuantiles(X, cols, rows)` = rowQuantiles(X, 
+     rows = cols, cols = rows, probs = probs, na.rm = FALSE), `rowQuantiles(X[cols, rows])` = rowQuantiles(X[cols, 
+     rows], probs = probs, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colQuantiles_X_S(), colQuantiles(X, rows, cols)() and colQuantiles(X[rows, cols])() 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 colQuantiles_X_S 1.105115 1.118742 1.162312 1.129861 1.164305 1.498532
2 colQuantiles(X, rows, cols) 1.122610 1.134019 1.171893 1.141625 1.161945 2.134724
3 colQuantiles(X[rows, cols]) 1.120515 1.135657 1.271988 1.150035 1.175746 10.611200
expr min lq mean median uq max
1 colQuantiles_X_S 1.000000 1.000000 1.000000 1.000000 1.0000000 1.000000
2 colQuantiles(X, rows, cols) 1.015831 1.013656 1.008243 1.010412 0.9979726 1.424543
3 colQuantiles(X[rows, cols]) 1.013935 1.015120 1.094361 1.017856 1.0098256 7.081063

Table: Benchmarking of rowQuantiles_X_S(), rowQuantiles(X, cols, rows)() and rowQuantiles(X[cols, 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 rowQuantiles_X_S 1.127420 1.139021 1.274640 1.150956 1.198005 10.544132
2 rowQuantiles(X, cols, rows) 1.134061 1.148978 1.179518 1.158532 1.176697 1.490784
3 rowQuantiles(X[cols, rows]) 1.134371 1.150945 1.193167 1.160303 1.194454 1.519690
expr min lq mean median uq max
1 rowQuantiles_X_S 1.000000 1.000000 1.0000000 1.000000 1.0000000 1.0000000
2 rowQuantiles(X, cols, rows) 1.005890 1.008742 0.9253735 1.006582 0.9822133 0.1413852
3 rowQuantiles(X[cols, rows]) 1.006165 1.010469 0.9360812 1.008121 0.9970355 0.1441266

Figure: Benchmarking of colQuantiles_X_S(), colQuantiles(X, rows, cols)() and colQuantiles(X[rows, cols])() on 100x100 data as well as rowQuantiles_X_S(), rowQuantiles(X, cols, rows)() and rowQuantiles(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colQuantiles_X_S() and rowQuantiles_X_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 colQuantiles_X_S 1.105115 1.118742 1.162312 1.129861 1.164305 1.498532
2 rowQuantiles_X_S 1.127420 1.139021 1.274640 1.150956 1.198005 10.544132
expr min lq mean median uq max
1 colQuantiles_X_S 1.000000 1.000000 1.000000 1.00000 1.000000 1.000000
2 rowQuantiles_X_S 1.020183 1.018127 1.096642 1.01867 1.028944 7.036308

Figure: Benchmarking of colQuantiles_X_S() and rowQuantiles_X_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]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3174572 169.6    5709258 305.0  5709258 305.0
Vcells 6071391  46.4   22343563 170.5 56666022 432.4
> probs <- seq(from = 0, to = 1, by = 0.25)
> colStats <- microbenchmark(colQuantiles_X_S = colQuantiles(X_S, probs = probs, na.rm = FALSE), `colQuantiles(X, rows, cols)` = colQuantiles(X, 
+     rows = rows, cols = cols, probs = probs, na.rm = FALSE), `colQuantiles(X[rows, cols])` = colQuantiles(X[rows, 
+     cols], probs = probs, na.rm = FALSE), unit = "ms")
> X <- t(X)
> X_S <- t(X_S)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3174566 169.6    5709258 305.0  5709258 305.0
Vcells 6081474  46.4   22343563 170.5 56666022 432.4
> rowStats <- microbenchmark(rowQuantiles_X_S = rowQuantiles(X_S, probs = probs, na.rm = FALSE), `rowQuantiles(X, cols, rows)` = rowQuantiles(X, 
+     rows = cols, cols = rows, probs = probs, na.rm = FALSE), `rowQuantiles(X[cols, rows])` = rowQuantiles(X[cols, 
+     rows], probs = probs, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colQuantiles_X_S(), colQuantiles(X, rows, cols)() and colQuantiles(X[rows, cols])() 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 colQuantiles_X_S 0.347737 0.3508535 0.3540595 0.3529880 0.3563305 0.379311
2 colQuantiles(X, rows, cols) 0.365316 0.3686585 0.3735868 0.3709845 0.3732405 0.564299
3 colQuantiles(X[rows, cols]) 0.364836 0.3686685 0.3716493 0.3710155 0.3737505 0.388226
expr min lq mean median uq max
1 colQuantiles_X_S 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
2 colQuantiles(X, rows, cols) 1.050553 1.050748 1.055153 1.050983 1.047456 1.487695
3 colQuantiles(X[rows, cols]) 1.049172 1.050776 1.049680 1.051071 1.048887 1.023503

Table: Benchmarking of rowQuantiles_X_S(), rowQuantiles(X, cols, rows)() and rowQuantiles(X[cols, 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 rowQuantiles_X_S 0.361176 0.3635325 0.3689631 0.3663520 0.3693885 0.529790
3 rowQuantiles(X[cols, rows]) 0.372690 0.3751245 0.3778451 0.3769775 0.3795045 0.391524
2 rowQuantiles(X, cols, rows) 0.371522 0.3754940 0.3787500 0.3779925 0.3802120 0.418408
expr min lq mean median uq max
1 rowQuantiles_X_S 1.000000 1.000000 1.000000 1.000000 1.000000 1.0000000
3 rowQuantiles(X[cols, rows]) 1.031879 1.031887 1.024073 1.029003 1.027386 0.7390173
2 rowQuantiles(X, cols, rows) 1.028645 1.032903 1.026525 1.031774 1.029301 0.7897620

Figure: Benchmarking of colQuantiles_X_S(), colQuantiles(X, rows, cols)() and colQuantiles(X[rows, cols])() on 1000x10 data as well as rowQuantiles_X_S(), rowQuantiles(X, cols, rows)() and rowQuantiles(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colQuantiles_X_S() and rowQuantiles_X_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 colQuantiles_X_S 347.737 350.8535 354.0595 352.988 356.3305 379.311
2 rowQuantiles_X_S 361.176 363.5325 368.9631 366.352 369.3885 529.790
expr min lq mean median uq max
1 colQuantiles_X_S 1.000000 1.000000 1.000000 1.00000 1.000000 1.000000
2 rowQuantiles_X_S 1.038647 1.036138 1.042093 1.03786 1.036646 1.396717

Figure: Benchmarking of colQuantiles_X_S() and rowQuantiles_X_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]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3174781 169.6    5709258 305.0  5709258 305.0
Vcells 6072385  46.4   22343563 170.5 56666022 432.4
> probs <- seq(from = 0, to = 1, by = 0.25)
> colStats <- microbenchmark(colQuantiles_X_S = colQuantiles(X_S, probs = probs, na.rm = FALSE), `colQuantiles(X, rows, cols)` = colQuantiles(X, 
+     rows = rows, cols = cols, probs = probs, na.rm = FALSE), `colQuantiles(X[rows, cols])` = colQuantiles(X[rows, 
+     cols], probs = probs, na.rm = FALSE), unit = "ms")
> X <- t(X)
> X_S <- t(X_S)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3174772 169.6    5709258 305.0  5709258 305.0
Vcells 6082463  46.5   22343563 170.5 56666022 432.4
> rowStats <- microbenchmark(rowQuantiles_X_S = rowQuantiles(X_S, probs = probs, na.rm = FALSE), `rowQuantiles(X, cols, rows)` = rowQuantiles(X, 
+     rows = cols, cols = rows, probs = probs, na.rm = FALSE), `rowQuantiles(X[cols, rows])` = rowQuantiles(X[cols, 
+     rows], probs = probs, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colQuantiles_X_S(), colQuantiles(X, rows, cols)() and colQuantiles(X[rows, cols])() 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 colQuantiles_X_S 8.012248 8.243809 9.089607 8.443760 9.317666 15.72662
2 colQuantiles(X, rows, cols) 7.978753 8.229190 11.738007 8.474258 9.434287 261.53323
3 colQuantiles(X[rows, cols]) 8.036511 8.270571 9.255518 8.487041 9.350547 16.61493
expr min lq mean median uq max
1 colQuantiles_X_S 1.0000000 1.0000000 1.000000 1.000000 1.000000 1.000000
2 colQuantiles(X, rows, cols) 0.9958195 0.9982267 1.291366 1.003612 1.012516 16.629969
3 colQuantiles(X[rows, cols]) 1.0030282 1.0032464 1.018253 1.005126 1.003529 1.056485

Table: Benchmarking of rowQuantiles_X_S(), rowQuantiles(X, cols, rows)() and rowQuantiles(X[cols, 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
2 rowQuantiles(X, cols, rows) 8.068601 8.283373 8.699466 8.424089 8.919847 15.23229
1 rowQuantiles_X_S 8.072643 8.315941 9.143712 8.467600 9.180913 15.80287
3 rowQuantiles(X[cols, rows]) 8.126022 8.358648 9.244347 8.631372 9.439137 16.19782
expr min lq mean median uq max
2 rowQuantiles(X, cols, rows) 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
1 rowQuantiles_X_S 1.000501 1.003932 1.051066 1.005165 1.029268 1.037459
3 rowQuantiles(X[cols, rows]) 1.007117 1.009087 1.062634 1.024606 1.058217 1.063387

Figure: Benchmarking of colQuantiles_X_S(), colQuantiles(X, rows, cols)() and colQuantiles(X[rows, cols])() on 10x1000 data as well as rowQuantiles_X_S(), rowQuantiles(X, cols, rows)() and rowQuantiles(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colQuantiles_X_S() and rowQuantiles_X_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 colQuantiles_X_S 8.012248 8.243809 9.089607 8.44376 9.317666 15.72662
2 rowQuantiles_X_S 8.072643 8.315941 9.143712 8.46760 9.180913 15.80287
expr min lq mean median uq max
1 colQuantiles_X_S 1.000000 1.00000 1.000000 1.000000 1.0000000 1.000000
2 rowQuantiles_X_S 1.007538 1.00875 1.005952 1.002823 0.9853233 1.004849

Figure: Benchmarking of colQuantiles_X_S() and rowQuantiles_X_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]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3174977 169.6    5709258 305.0  5709258 305.0
Vcells 6117111  46.7   22343563 170.5 56666022 432.4
> probs <- seq(from = 0, to = 1, by = 0.25)
> colStats <- microbenchmark(colQuantiles_X_S = colQuantiles(X_S, probs = probs, na.rm = FALSE), `colQuantiles(X, rows, cols)` = colQuantiles(X, 
+     rows = rows, cols = cols, probs = probs, na.rm = FALSE), `colQuantiles(X[rows, cols])` = colQuantiles(X[rows, 
+     cols], probs = probs, na.rm = FALSE), unit = "ms")
> X <- t(X)
> X_S <- t(X_S)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3174971 169.6    5709258 305.0  5709258 305.0
Vcells 6217194  47.5   22343563 170.5 56666022 432.4
> rowStats <- microbenchmark(rowQuantiles_X_S = rowQuantiles(X_S, probs = probs, na.rm = FALSE), `rowQuantiles(X, cols, rows)` = rowQuantiles(X, 
+     rows = cols, cols = rows, probs = probs, na.rm = FALSE), `rowQuantiles(X[cols, rows])` = rowQuantiles(X[cols, 
+     rows], probs = probs, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colQuantiles_X_S(), colQuantiles(X, rows, cols)() and colQuantiles(X[rows, cols])() 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 colQuantiles_X_S 10.18937 10.38272 11.47289 10.68613 11.26622 21.97929
2 colQuantiles(X, rows, cols) 10.40267 10.59949 11.56238 10.83319 11.45710 25.01436
3 colQuantiles(X[rows, cols]) 10.41228 10.58153 11.45298 10.83808 11.35463 22.18076
expr min lq mean median uq max
1 colQuantiles_X_S 1.000000 1.000000 1.0000000 1.000000 1.000000 1.000000
2 colQuantiles(X, rows, cols) 1.020934 1.020878 1.0078004 1.013762 1.016942 1.138088
3 colQuantiles(X[rows, cols]) 1.021876 1.019149 0.9982649 1.014220 1.007847 1.009167

Table: Benchmarking of rowQuantiles_X_S(), rowQuantiles(X, cols, rows)() and rowQuantiles(X[cols, 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 rowQuantiles_X_S 10.50413 10.71388 11.97065 10.97342 11.71819 28.87060
2 rowQuantiles(X, cols, rows) 10.61135 10.82808 11.98220 11.14736 11.66477 24.07509
3 rowQuantiles(X[cols, rows]) 10.62854 10.85966 11.77734 11.14840 11.92261 22.28323
expr min lq mean median uq max
1 rowQuantiles_X_S 1.000000 1.000000 1.0000000 1.000000 1.0000000 1.0000000
2 rowQuantiles(X, cols, rows) 1.010208 1.010659 1.0009654 1.015851 0.9954409 0.8338963
3 rowQuantiles(X[cols, rows]) 1.011844 1.013606 0.9838513 1.015945 1.0174450 0.7718312

Figure: Benchmarking of colQuantiles_X_S(), colQuantiles(X, rows, cols)() and colQuantiles(X[rows, cols])() on 100x1000 data as well as rowQuantiles_X_S(), rowQuantiles(X, cols, rows)() and rowQuantiles(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colQuantiles_X_S() and rowQuantiles_X_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 colQuantiles_X_S 10.18937 10.38272 11.47289 10.68613 11.26622 21.97929
2 rowQuantiles_X_S 10.50413 10.71388 11.97065 10.97342 11.71819 28.87060
expr min lq mean median uq max
1 colQuantiles_X_S 1.00000 1.000000 1.000000 1.000000 1.000000 1.000000
2 rowQuantiles_X_S 1.03089 1.031896 1.043386 1.026885 1.040117 1.313537

Figure: Benchmarking of colQuantiles_X_S() and rowQuantiles_X_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]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3175193 169.6    5709258 305.0  5709258 305.0
Vcells 6117939  46.7   22343563 170.5 56666022 432.4
> probs <- seq(from = 0, to = 1, by = 0.25)
> colStats <- microbenchmark(colQuantiles_X_S = colQuantiles(X_S, probs = probs, na.rm = FALSE), `colQuantiles(X, rows, cols)` = colQuantiles(X, 
+     rows = rows, cols = cols, probs = probs, na.rm = FALSE), `colQuantiles(X[rows, cols])` = colQuantiles(X[rows, 
+     cols], probs = probs, na.rm = FALSE), unit = "ms")
> X <- t(X)
> X_S <- t(X_S)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3175187 169.6    5709258 305.0  5709258 305.0
Vcells 6218022  47.5   22343563 170.5 56666022 432.4
> rowStats <- microbenchmark(rowQuantiles_X_S = rowQuantiles(X_S, probs = probs, na.rm = FALSE), `rowQuantiles(X, cols, rows)` = rowQuantiles(X, 
+     rows = cols, cols = rows, probs = probs, na.rm = FALSE), `rowQuantiles(X[cols, rows])` = rowQuantiles(X[cols, 
+     rows], probs = probs, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colQuantiles_X_S(), colQuantiles(X, rows, cols)() and colQuantiles(X[rows, cols])() 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 colQuantiles_X_S 2.663449 2.692945 2.754061 2.705303 2.726732 3.421134
3 colQuantiles(X[rows, cols]) 2.756835 2.780410 3.021965 2.794395 2.823256 11.221324
2 colQuantiles(X, rows, cols) 2.755981 2.778030 2.956400 2.798392 2.831595 11.446626
expr min lq mean median uq max
1 colQuantiles_X_S 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
3 colQuantiles(X[rows, cols]) 1.035062 1.032479 1.097276 1.032932 1.035399 3.280001
2 colQuantiles(X, rows, cols) 1.034741 1.031595 1.073469 1.034410 1.038457 3.345857

Table: Benchmarking of rowQuantiles_X_S(), rowQuantiles(X, cols, rows)() and rowQuantiles(X[cols, 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 rowQuantiles_X_S 2.844951 2.861944 2.993162 2.877883 2.907040 4.646321
2 rowQuantiles(X, cols, rows) 2.937658 2.963849 3.198382 2.983258 3.034340 11.780869
3 rowQuantiles(X[cols, rows]) 2.936644 2.968400 3.265414 2.990714 3.056268 13.859673
expr min lq mean median uq max
1 rowQuantiles_X_S 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
2 rowQuantiles(X, cols, rows) 1.032587 1.035607 1.068563 1.036615 1.043790 2.535526
3 rowQuantiles(X[cols, rows]) 1.032230 1.037197 1.090958 1.039206 1.051333 2.982935

Figure: Benchmarking of colQuantiles_X_S(), colQuantiles(X, rows, cols)() and colQuantiles(X[rows, cols])() on 1000x100 data as well as rowQuantiles_X_S(), rowQuantiles(X, cols, rows)() and rowQuantiles(X[cols, rows])() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colQuantiles_X_S() and rowQuantiles_X_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 colQuantiles_X_S 2.663449 2.692945 2.754061 2.705303 2.726732 3.421134
2 rowQuantiles_X_S 2.844951 2.861944 2.993162 2.877883 2.907040 4.646321
expr min lq mean median uq max
1 colQuantiles_X_S 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
2 rowQuantiles_X_S 1.068145 1.062756 1.086818 1.063793 1.066126 1.358123

Figure: Benchmarking of colQuantiles_X_S() and rowQuantiles_X_S() on 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 26.99 secs.

Reproducibility

To reproduce this report, do:

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

Copyright Dongcan Jiang. Last updated on 2019-09-10 20:49:41 (-0700 UTC). Powered by RSP.

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