count - HenrikBengtsson/matrixStats GitHub Wiki

matrixStats: Benchmark report

count() benchmarks

This report benchmark the performance of count() against alternative methods.

Alternative methods

  • sum(x == value)

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"]]
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  3231175 172.6    5709258 305.0  5709258 305.0
Vcells 33430019 255.1   59837990 456.6 57084605 435.6
> stats <- microbenchmark(count = count(x, value), `sum(x == value)` = sum(x == value), unit = "ms")

Table: Benchmarking of count() and sum(x == value)() 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 count 0.001668 0.001766 0.0020640 0.0018505 0.0019250 0.021897
2 sum(x == value) 0.002571 0.002667 0.0028124 0.0027235 0.0027855 0.011185
expr min lq mean median uq max
1 count 1.000000 1.000000 1.000000 1.000000 1.000000 1.0000000
2 sum(x == value) 1.541367 1.510193 1.362586 1.471764 1.447013 0.5108006

Figure: Benchmarking of count() and sum(x == value)() on integer+n = 1000 data. Outliers are displayed as crosses. Times are in milliseconds.

n = 10000 vector

> x <- data[["n = 10000"]]
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  3228942 172.5    5709258 305.0  5709258 305.0
Vcells 11794763  90.0   38296314 292.2 57084605 435.6
> stats <- microbenchmark(count = count(x, value), `sum(x == value)` = sum(x == value), unit = "ms")

Table: Benchmarking of count() and sum(x == value)() 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 count 0.001685 0.001856 0.0022827 0.0020615 0.0023060 0.020644
2 sum(x == value) 0.022238 0.022466 0.0229967 0.0225655 0.0227155 0.031721
expr min lq mean median uq max
1 count 1.00000 1.00000 1.00000 1.00000 1.000000 1.000000
2 sum(x == value) 13.19763 12.10453 10.07446 10.94616 9.850607 1.536572

Figure: Benchmarking of count() and sum(x == value)() on integer+n = 10000 data. Outliers are displayed as crosses. Times are in milliseconds.

n = 100000 vector

> x <- data[["n = 100000"]]
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  3229005 172.5    5709258 305.0  5709258 305.0
Vcells 11794805  90.0   38296314 292.2 57084605 435.6
> stats <- microbenchmark(count = count(x, value), `sum(x == value)` = sum(x == value), unit = "ms")

Table: Benchmarking of count() and sum(x == value)() 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 count 0.001679 0.0019050 0.0026980 0.002224 0.0028375 0.029385
2 sum(x == value) 0.206338 0.2172605 0.2189826 0.218022 0.2197080 0.244302
expr min lq mean median uq max
1 count 1.0000 1.0000 1.00000 1.00000 1.00000 1.000000
2 sum(x == value) 122.8934 114.0475 81.16329 98.03147 77.43013 8.313834

Figure: Benchmarking of count() and sum(x == value)() on integer+n = 100000 data. Outliers are displayed as crosses. Times are in milliseconds.

n = 1000000 vector

> x <- data[["n = 1000000"]]
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  3229068 172.5    5709258 305.0  5709258 305.0
Vcells 11795360  90.0   38296314 292.2 57084605 435.6
> stats <- microbenchmark(count = count(x, value), `sum(x == value)` = sum(x == value), unit = "ms")

Table: Benchmarking of count() and sum(x == value)() 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 count 0.001668 0.002062 0.0092437 0.0088085 0.016791 0.03742
2 sum(x == value) 2.738796 3.213318 3.4531879 3.2412525 3.285109 15.69834
expr min lq mean median uq max
1 count 1.000 1.00 1.0000 1.0000 1.000 1.0000
2 sum(x == value) 1641.964 1558.35 373.5716 367.9687 195.647 419.5172

Figure: Benchmarking of count() and sum(x == value)() on integer+n = 1000000 data. Outliers are displayed as crosses. Times are in milliseconds.

n = 10000000 vector

> x <- data[["n = 10000000"]]
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  3229131 172.5    5709258 305.0  5709258 305.0
Vcells 11795402  90.0   38296314 292.2 57084605 435.6
> stats <- microbenchmark(count = count(x, value), `sum(x == value)` = sum(x == value), unit = "ms")

Table: Benchmarking of count() and sum(x == value)() 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 count 0.001711 0.002137 0.0126709 0.0034835 0.0227455 0.052068
2 sum(x == value) 31.990870 32.284979 36.0097863 32.8202760 34.2459520 55.251011
expr min lq mean median uq max
1 count 1.00 1.00 1.000 1.000 1.000 1.000
2 sum(x == value) 18697.18 15107.62 2841.939 9421.638 1505.614 1061.132

Figure: Benchmarking of count() and sum(x == value)() 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"]]
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  3229194 172.5    5709258 305.0  5709258 305.0
Vcells 17351273 132.4   38296314 292.2 57084605 435.6
> stats <- microbenchmark(count = count(x, value), `sum(x == value)` = sum(x == value), unit = "ms")

Table: Benchmarking of count() and sum(x == value)() 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 count 0.001683 0.0017205 0.0023145 0.0018620 0.0020020 0.043659
2 sum(x == value) 0.002109 0.0022205 0.0024306 0.0022905 0.0023675 0.015180
expr min lq mean median uq max
1 count 1.000000 1.000000 1.000000 1.000000 1.000000 1.0000000
2 sum(x == value) 1.253119 1.290613 1.050184 1.230129 1.182567 0.3476946

Figure: Benchmarking of count() and sum(x == value)() on double+n = 1000 data. Outliers are displayed as crosses. Times are in milliseconds.

n = 10000 vector

> x <- data[["n = 10000"]]
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  3229257 172.5    5709258 305.0  5709258 305.0
Vcells 17351314 132.4   38296314 292.2 57084605 435.6
> stats <- microbenchmark(count = count(x, value), `sum(x == value)` = sum(x == value), unit = "ms")

Table: Benchmarking of count() and sum(x == value)() 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 count 0.001677 0.0019015 0.0025086 0.0021490 0.0023955 0.038174
2 sum(x == value) 0.017422 0.0176605 0.0182435 0.0177825 0.0179710 0.028087
expr min lq mean median uq max
1 count 1.00000 1.000000 1.000000 1.000000 1.000000 1.0000000
2 sum(x == value) 10.38879 9.287668 7.272254 8.274779 7.501983 0.7357626

Figure: Benchmarking of count() and sum(x == value)() on double+n = 10000 data. Outliers are displayed as crosses. Times are in milliseconds.

n = 100000 vector

> x <- data[["n = 100000"]]
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  3229320 172.5    5709258 305.0  5709258 305.0
Vcells 17351639 132.4   38296314 292.2 57084605 435.6
> stats <- microbenchmark(count = count(x, value), `sum(x == value)` = sum(x == value), unit = "ms")

Table: Benchmarking of count() and sum(x == value)() 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 count 0.001681 0.0018845 0.0026881 0.0022020 0.0028000 0.028854
2 sum(x == value) 0.167430 0.1688190 0.1710085 0.1694525 0.1707535 0.205628
expr min lq mean median uq max
1 count 1.00000 1.00000 1.0000 1.00000 1.00000 1.000000
2 sum(x == value) 99.60143 89.58291 63.6164 76.95391 60.98339 7.126499

Figure: Benchmarking of count() and sum(x == value)() on double+n = 100000 data. Outliers are displayed as crosses. Times are in milliseconds.

n = 1000000 vector

> x <- data[["n = 1000000"]]
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  3229383 172.5    5709258 305.0  5709258 305.0
Vcells 17352014 132.4   38296314 292.2 57084605 435.6
> stats <- microbenchmark(count = count(x, value), `sum(x == value)` = sum(x == value), unit = "ms")

Table: Benchmarking of count() and sum(x == value)() 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 count 0.001665 0.0020285 0.0078366 0.007264 0.012295 0.025685
2 sum(x == value) 1.730855 2.7169495 3.0016203 2.744386 2.806246 14.117200
expr min lq mean median uq max
1 count 1.000 1.000 1.0000 1.0000 1.0000 1.0000
2 sum(x == value) 1039.553 1339.388 383.0278 377.8064 228.2429 549.6282

Figure: Benchmarking of count() and sum(x == value)() on double+n = 1000000 data. Outliers are displayed as crosses. Times are in milliseconds.

n = 10000000 vector

> x <- data[["n = 10000000"]]
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  3229446 172.5    5709258 305.0  5709258 305.0
Vcells 17352056 132.4   38296314 292.2 57084605 435.6
> stats <- microbenchmark(count = count(x, value), `sum(x == value)` = sum(x == value), unit = "ms")

Table: Benchmarking of count() and sum(x == value)() 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 count 0.00170 0.0021225 0.0117215 0.0030785 0.0215545 0.036833
2 sum(x == value) 27.04751 27.3330855 30.7959116 27.8625050 28.7366065 44.825225
expr min lq mean median uq max
1 count 1.0 1.00 1.000 1.000 1.000 1.000
2 sum(x == value) 15910.3 12877.78 2627.301 9050.676 1333.207 1216.985

Figure: Benchmarking of count() and sum(x == value)() 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 19.52 secs.

Reproducibility

To reproduce this report, do:

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

Copyright Henrik Bengtsson. Last updated on 2019-09-10 20:57:56 (-0700 UTC). Powered by RSP.

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