anyMissing_subset - HenrikBengtsson/matrixStats GitHub Wiki

matrixStats: Benchmark report

anyMissing() benchmarks on subsetted computation

This report benchmark the performance of anyMissing() on subsetted computation.

Data type "integer"

Data

> rvector <- function(n, mode = c("logical", "double", "integer"), range = c(-100, +100), na_prob = 0) {
+     mode <- match.arg(mode)
+     if (mode == "logical") {
+         x <- sample(c(FALSE, TRUE), size = n, replace = TRUE)
+     }     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
+     x
+ }
> rvectors <- function(scale = 10, seed = 1, ...) {
+     set.seed(seed)
+     data <- list()
+     data[[1]] <- rvector(n = scale * 100, ...)
+     data[[2]] <- rvector(n = scale * 1000, ...)
+     data[[3]] <- rvector(n = scale * 10000, ...)
+     data[[4]] <- rvector(n = scale * 1e+05, ...)
+     data[[5]] <- rvector(n = scale * 1e+06, ...)
+     names(data) <- sprintf("n = %d", sapply(data, FUN = length))
+     data
+ }
> data <- rvectors(mode = mode)

Results

n = 1000 vector

> x <- data[["n = 1000"]]
> idxs <- sample.int(length(x), size = length(x) * 0.7)
> x_S <- x[idxs]
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  3052231 163.1    5709258 305.0  5709258 305.0
Vcells 21597952 164.8   58462520 446.1 56666022 432.4
> stats <- microbenchmark(anyMissing_x_S = anyMissing(x_S), `anyMissing(x, idxs)` = anyMissing(x, idxs = idxs), 
+     `anyMissing(x[idxs])` = anyMissing(x[idxs]), unit = "ms")

Table: Benchmarking of anyMissing_x_S(), anyMissing(x, idxs)() and anyMissing(x[idxs])() on integer+n = 1000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 anyMissing_x_S 0.000775 0.000788 0.0008502 0.0008205 0.0008380 0.001761
2 anyMissing(x, idxs) 0.001375 0.001420 0.0014706 0.0014380 0.0014770 0.002159
3 anyMissing(x[idxs]) 0.002194 0.002326 0.0034593 0.0023715 0.0024305 0.108270
expr min lq mean median uq max
1 anyMissing_x_S 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
2 anyMissing(x, idxs) 1.774193 1.802031 1.729795 1.752590 1.762530 1.226008
3 anyMissing(x[idxs]) 2.830968 2.951777 4.068963 2.890311 2.900358 61.482112

Figure: Benchmarking of anyMissing_x_S(), anyMissing(x, idxs)() and anyMissing(x[idxs])() on integer+n = 1000 data. Outliers are displayed as crosses. Times are in milliseconds.

n = 10000 vector

> x <- data[["n = 10000"]]
> idxs <- sample.int(length(x), size = length(x) * 0.7)
> x_S <- x[idxs]
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  3049258 162.9    5709258 305.0  5709258 305.0
Vcells 10485544  80.0   46770016 356.9 56666022 432.4
> stats <- microbenchmark(anyMissing_x_S = anyMissing(x_S), `anyMissing(x, idxs)` = anyMissing(x, idxs = idxs), 
+     `anyMissing(x[idxs])` = anyMissing(x[idxs]), unit = "ms")

Table: Benchmarking of anyMissing_x_S(), anyMissing(x, idxs)() and anyMissing(x[idxs])() on integer+n = 10000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 anyMissing_x_S 0.004139 0.0042000 0.0042818 0.0042680 0.0043060 0.005179
2 anyMissing(x, idxs) 0.009833 0.0098955 0.0101174 0.0099445 0.0100370 0.019449
3 anyMissing(x[idxs]) 0.015902 0.0161795 0.0167231 0.0163195 0.0164805 0.049985
expr min lq mean median uq max
1 anyMissing_x_S 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
2 anyMissing(x, idxs) 2.375695 2.356071 2.362919 2.330014 2.330934 3.755358
3 anyMissing(x[idxs]) 3.841991 3.852262 3.905667 3.823688 3.827334 9.651477

Figure: Benchmarking of anyMissing_x_S(), anyMissing(x, idxs)() and anyMissing(x[idxs])() on integer+n = 10000 data. Outliers are displayed as crosses. Times are in milliseconds.

n = 100000 vector

> x <- data[["n = 100000"]]
> idxs <- sample.int(length(x), size = length(x) * 0.7)
> x_S <- x[idxs]
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  3049330 162.9    5709258 305.0  5709258 305.0
Vcells 10549104  80.5   37416013 285.5 56666022 432.4
> stats <- microbenchmark(anyMissing_x_S = anyMissing(x_S), `anyMissing(x, idxs)` = anyMissing(x, idxs = idxs), 
+     `anyMissing(x[idxs])` = anyMissing(x[idxs]), unit = "ms")

Table: Benchmarking of anyMissing_x_S(), anyMissing(x, idxs)() and anyMissing(x[idxs])() on integer+n = 100000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 anyMissing_x_S 0.040224 0.0443895 0.0533530 0.0466745 0.0573725 0.144915
2 anyMissing(x, idxs) 0.135243 0.1438805 0.1713973 0.1530420 0.1587210 0.451257
3 anyMissing(x[idxs]) 0.203309 0.2190730 0.2791400 0.2292040 0.2465105 0.885805
expr min lq mean median uq max
1 anyMissing_x_S 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
2 anyMissing(x, idxs) 3.362246 3.241318 3.212516 3.278921 2.766500 3.113943
3 anyMissing(x[idxs]) 5.054420 4.935244 5.231949 4.910690 4.296666 6.112583

Figure: Benchmarking of anyMissing_x_S(), anyMissing(x, idxs)() and anyMissing(x[idxs])() on integer+n = 100000 data. Outliers are displayed as crosses. Times are in milliseconds.

n = 1000000 vector

> x <- data[["n = 1000000"]]
> idxs <- sample.int(length(x), size = length(x) * 0.7)
> x_S <- x[idxs]
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  3049402 162.9    5709258 305.0  5709258 305.0
Vcells 11179153  85.3   37416013 285.5 56666022 432.4
> stats <- microbenchmark(anyMissing_x_S = anyMissing(x_S), `anyMissing(x, idxs)` = anyMissing(x, idxs = idxs), 
+     `anyMissing(x[idxs])` = anyMissing(x[idxs]), unit = "ms")

Table: Benchmarking of anyMissing_x_S(), anyMissing(x, idxs)() and anyMissing(x[idxs])() on integer+n = 1000000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 anyMissing_x_S 0.369494 0.4798705 0.5139672 0.486784 0.5081445 1.229963
2 anyMissing(x, idxs) 1.677564 2.2693975 2.7414527 2.416130 2.8599650 5.406851
3 anyMissing(x[idxs]) 2.798661 4.3687570 5.1272277 4.685953 5.0658840 15.620716
expr min lq mean median uq max
1 anyMissing_x_S 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
2 anyMissing(x, idxs) 4.540166 4.729187 5.333907 4.963455 5.628251 4.395946
3 anyMissing(x[idxs]) 7.574307 9.104033 9.975789 9.626350 9.969377 12.700151

Figure: Benchmarking of anyMissing_x_S(), anyMissing(x, idxs)() and anyMissing(x[idxs])() on integer+n = 1000000 data. Outliers are displayed as crosses. Times are in milliseconds.

n = 10000000 vector

> x <- data[["n = 10000000"]]
> idxs <- sample.int(length(x), size = length(x) * 0.7)
> x_S <- x[idxs]
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  3049474 162.9    5709258 305.0  5709258 305.0
Vcells 17479408 133.4   37416013 285.5 56666022 432.4
> stats <- microbenchmark(anyMissing_x_S = anyMissing(x_S), `anyMissing(x, idxs)` = anyMissing(x, idxs = idxs), 
+     `anyMissing(x[idxs])` = anyMissing(x[idxs]), unit = "ms")

Table: Benchmarking of anyMissing_x_S(), anyMissing(x, idxs)() and anyMissing(x[idxs])() on integer+n = 10000000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 anyMissing_x_S 5.684406 7.139483 8.507188 7.543172 10.68328 13.8445
2 anyMissing(x, idxs) 93.497479 112.713793 121.700595 120.517454 130.16848 155.5575
3 anyMissing(x[idxs]) 126.903110 136.767034 146.120593 143.976145 148.79085 363.5733
expr min lq mean median uq max
1 anyMissing_x_S 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000
2 anyMissing(x, idxs) 16.44806 15.78739 14.30562 15.97702 12.18432 11.23604
3 anyMissing(x[idxs]) 22.32478 19.15643 17.17613 19.08695 13.92745 26.26120

Figure: Benchmarking of anyMissing_x_S(), anyMissing(x, idxs)() and anyMissing(x[idxs])() on integer+n = 10000000 data. Outliers are displayed as crosses. Times are in milliseconds.

Data type "double"

Data

> rvector <- function(n, mode = c("logical", "double", "integer"), range = c(-100, +100), na_prob = 0) {
+     mode <- match.arg(mode)
+     if (mode == "logical") {
+         x <- sample(c(FALSE, TRUE), size = n, replace = TRUE)
+     }     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
+     x
+ }
> rvectors <- function(scale = 10, seed = 1, ...) {
+     set.seed(seed)
+     data <- list()
+     data[[1]] <- rvector(n = scale * 100, ...)
+     data[[2]] <- rvector(n = scale * 1000, ...)
+     data[[3]] <- rvector(n = scale * 10000, ...)
+     data[[4]] <- rvector(n = scale * 1e+05, ...)
+     data[[5]] <- rvector(n = scale * 1e+06, ...)
+     names(data) <- sprintf("n = %d", sapply(data, FUN = length))
+     data
+ }
> data <- rvectors(mode = mode)

Results

n = 1000 vector

> x <- data[["n = 1000"]]
> idxs <- sample.int(length(x), size = length(x) * 0.7)
> x_S <- x[idxs]
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  3049555 162.9    5709258 305.0  5709258 305.0
Vcells 16036274 122.4   37416013 285.5 56666022 432.4
> stats <- microbenchmark(anyMissing_x_S = anyMissing(x_S), `anyMissing(x, idxs)` = anyMissing(x, idxs = idxs), 
+     `anyMissing(x[idxs])` = anyMissing(x[idxs]), unit = "ms")

Table: Benchmarking of anyMissing_x_S(), anyMissing(x, idxs)() and anyMissing(x[idxs])() on double+n = 1000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 anyMissing_x_S 0.000777 0.000803 0.0008492 0.000819 0.0008375 0.001774
2 anyMissing(x, idxs) 0.001413 0.001439 0.0014882 0.001454 0.0014830 0.002522
3 anyMissing(x[idxs]) 0.002303 0.002439 0.0028966 0.002568 0.0027000 0.030612
expr min lq mean median uq max
1 anyMissing_x_S 1.000000 1.00000 1.000000 1.000000 1.000000 1.000000
2 anyMissing(x, idxs) 1.818533 1.79203 1.752553 1.775336 1.770746 1.421646
3 anyMissing(x[idxs]) 2.963964 3.03736 3.411141 3.135531 3.223881 17.255919

Figure: Benchmarking of anyMissing_x_S(), anyMissing(x, idxs)() and anyMissing(x[idxs])() on double+n = 1000 data. Outliers are displayed as crosses. Times are in milliseconds.

n = 10000 vector

> x <- data[["n = 10000"]]
> idxs <- sample.int(length(x), size = length(x) * 0.7)
> x_S <- x[idxs]
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  3049618 162.9    5709258 305.0  5709258 305.0
Vcells 16046063 122.5   37416013 285.5 56666022 432.4
> stats <- microbenchmark(anyMissing_x_S = anyMissing(x_S), `anyMissing(x, idxs)` = anyMissing(x, idxs = idxs), 
+     `anyMissing(x[idxs])` = anyMissing(x[idxs]), unit = "ms")

Table: Benchmarking of anyMissing_x_S(), anyMissing(x, idxs)() and anyMissing(x[idxs])() on double+n = 10000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 anyMissing_x_S 0.004032 0.004152 0.0043787 0.0042080 0.0042875 0.018270
2 anyMissing(x, idxs) 0.009852 0.010059 0.0101976 0.0102005 0.0102715 0.011691
3 anyMissing(x[idxs]) 0.016944 0.018027 0.0185712 0.0183025 0.0185410 0.042988
expr min lq mean median uq max
1 anyMissing_x_S 1.000000 1.000000 1.000000 1.000000 1.000000 1.0000000
2 anyMissing(x, idxs) 2.443452 2.422688 2.328944 2.424073 2.395685 0.6399015
3 anyMissing(x[idxs]) 4.202381 4.341763 4.241298 4.349453 4.324432 2.3529283

Figure: Benchmarking of anyMissing_x_S(), anyMissing(x, idxs)() and anyMissing(x[idxs])() on double+n = 10000 data. Outliers are displayed as crosses. Times are in milliseconds.

n = 100000 vector

> x <- data[["n = 100000"]]
> idxs <- sample.int(length(x), size = length(x) * 0.7)
> x_S <- x[idxs]
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  3049690 162.9    5709258 305.0  5709258 305.0
Vcells 16140976 123.2   37416013 285.5 56666022 432.4
> stats <- microbenchmark(anyMissing_x_S = anyMissing(x_S), `anyMissing(x, idxs)` = anyMissing(x, idxs = idxs), 
+     `anyMissing(x[idxs])` = anyMissing(x[idxs]), unit = "ms")

Table: Benchmarking of anyMissing_x_S(), anyMissing(x, idxs)() and anyMissing(x[idxs])() on double+n = 100000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 anyMissing_x_S 0.036983 0.0371180 0.0403236 0.0375025 0.0398020 0.068180
2 anyMissing(x, idxs) 0.138132 0.1385075 0.1411397 0.1386980 0.1388735 0.311077
3 anyMissing(x[idxs]) 0.227166 0.2362010 0.3139094 0.3564825 0.3605265 0.375998
expr min lq mean median uq max
1 anyMissing_x_S 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
2 anyMissing(x, idxs) 3.735013 3.731545 3.500174 3.698367 3.489109 4.562584
3 anyMissing(x[idxs]) 6.142444 6.363516 7.784754 9.505566 9.058000 5.514784

Figure: Benchmarking of anyMissing_x_S(), anyMissing(x, idxs)() and anyMissing(x[idxs])() on double+n = 100000 data. Outliers are displayed as crosses. Times are in milliseconds.

n = 1000000 vector

> x <- data[["n = 1000000"]]
> idxs <- sample.int(length(x), size = length(x) * 0.7)
> x_S <- x[idxs]
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  3049763 162.9    5709258 305.0  5709258 305.0
Vcells 17086033 130.4   37416013 285.5 56666022 432.4
> stats <- microbenchmark(anyMissing_x_S = anyMissing(x_S), `anyMissing(x, idxs)` = anyMissing(x, idxs = idxs), 
+     `anyMissing(x[idxs])` = anyMissing(x[idxs]), unit = "ms")

Table: Benchmarking of anyMissing_x_S(), anyMissing(x, idxs)() and anyMissing(x[idxs])() on double+n = 1000000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 anyMissing_x_S 0.522342 0.6471455 0.6660328 0.666311 0.6850945 0.757113
2 anyMissing(x, idxs) 3.715565 4.5817680 4.7084413 4.678424 4.8384565 5.932567
3 anyMissing(x[idxs]) 6.331259 7.0339720 8.9550346 9.288386 9.5016535 16.537808
expr min lq mean median uq max
1 anyMissing_x_S 1.00000 1.000000 1.000000 1.000000 1.000000 1.000000
2 anyMissing(x, idxs) 7.11328 7.079966 7.069383 7.021381 7.062466 7.835775
3 anyMissing(x[idxs]) 12.12091 10.869228 13.445335 13.940015 13.869114 21.843249

Figure: Benchmarking of anyMissing_x_S(), anyMissing(x, idxs)() and anyMissing(x[idxs])() on double+n = 1000000 data. Outliers are displayed as crosses. Times are in milliseconds.

n = 10000000 vector

> x <- data[["n = 10000000"]]
> idxs <- sample.int(length(x), size = length(x) * 0.7)
> x_S <- x[idxs]
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  3049835 162.9    5709258 305.0  5709258 305.0
Vcells 26536516 202.5   44979215 343.2 56666022 432.4
> stats <- microbenchmark(anyMissing_x_S = anyMissing(x_S), `anyMissing(x, idxs)` = anyMissing(x, idxs = idxs), 
+     `anyMissing(x[idxs])` = anyMissing(x[idxs]), unit = "ms")

Table: Benchmarking of anyMissing_x_S(), anyMissing(x, idxs)() and anyMissing(x[idxs])() on double+n = 10000000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 anyMissing_x_S 5.888141 7.225792 8.73584 7.723072 10.87091 13.93323
2 anyMissing(x, idxs) 103.717287 151.267297 161.63545 163.366152 173.61495 220.41171
3 anyMissing(x[idxs]) 146.006209 173.591400 187.50270 181.606660 189.74241 416.62257
expr min lq mean median uq max
1 anyMissing_x_S 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000
2 anyMissing(x, idxs) 17.61461 20.93435 18.50257 21.15300 15.97060 15.81914
3 anyMissing(x[idxs]) 24.79666 24.02386 21.46362 23.51482 17.45414 29.90136

Figure: Benchmarking of anyMissing_x_S(), anyMissing(x, idxs)() and anyMissing(x[idxs])() on double+n = 10000000 data. 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 1.3 mins.

Reproducibility

To reproduce this report, do:

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

Copyright Dongcan Jiang. Last updated on 2019-09-10 20:33:34 (-0700 UTC). Powered by RSP.

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