binCounts - HenrikBengtsson/matrixStats GitHub Wiki

matrixStats: Benchmark report


binCounts() benchmarks

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

Alternative methods

  • hist()

as below

> hist <- graphics::hist
> binCounts_hist <- function(x, bx, right = FALSE, ...) {
+     hist(x, breaks = bx, right = right, include.lowest = TRUE, plot = FALSE)$counts
+ }

Data type "integer"

Non-sorted simulated data

> set.seed(48879)
> nx <- 1e+05
> xmax <- 0.01 * nx
> x <- runif(nx, min = 0, max = xmax)
> storage.mode(x) <- mode
> str(x)
 int [1:100000] 722 285 591 3 349 509 216 91 150 383 ...
> nb <- 10000
> bx <- seq(from = 0, to = xmax, length.out = nb + 1L)
> bx <- c(-1, bx, xmax + 1)

Results

> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3054193 163.2    5709258 305.0  5709258 305.0
Vcells 5150348  39.3   22221311 169.6 56666022 432.4
> stats <- microbenchmark(binCounts = binCounts(x, bx = bx), hist = binCounts_hist(x, bx = bx), unit = "ms")

Table: Benchmarking of binCounts() and hist() on integer+unsorted data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 binCounts 4.906438 4.981776 5.203014 5.038877 5.086733 9.428698
2 hist 8.540419 8.707266 8.860796 8.834853 8.920008 13.046789
expr min lq mean median uq max
1 binCounts 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
2 hist 1.740656 1.747824 1.703012 1.753338 1.753583 1.383732

Figure: Benchmarking of binCounts() and hist() on integer+unsorted data. Outliers are displayed as crosses. Times are in milliseconds.

Sorted simulated data

> x <- sort(x)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3053148 163.1    5709258 305.0  5709258 305.0
Vcells 5006313  38.2   22221311 169.6 56666022 432.4
> stats <- microbenchmark(binCounts = binCounts(x, bx = bx), hist = binCounts_hist(x, bx = bx), unit = "ms")

Table: Benchmarking of binCounts() and hist() on integer+sorted data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 binCounts 0.746109 0.787845 0.8582377 0.8051165 0.8251675 4.630643
2 hist 3.649455 3.757657 6.1436541 3.8502230 3.9923265 217.726446
expr min lq mean median uq max
1 binCounts 1.000000 1.000000 1.000000 1.000000 1.000000 1.00000
2 hist 4.891316 4.769538 7.158453 4.782194 4.838201 47.01862

Figure: Benchmarking of binCounts() and hist() on integer+sorted data. Outliers are displayed as crosses. Times are in milliseconds.

Data type "double"

Non-sorted simulated data

> set.seed(48879)
> nx <- 1e+05
> xmax <- 0.01 * nx
> x <- runif(nx, min = 0, max = xmax)
> storage.mode(x) <- mode
> str(x)
 num [1:100000] 722.11 285.54 591.33 3.42 349.14 ...
> nb <- 10000
> bx <- seq(from = 0, to = xmax, length.out = nb + 1L)
> bx <- c(-1, bx, xmax + 1)

Results

> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3053211 163.1    5709258 305.0  5709258 305.0
Vcells 5056355  38.6   22221311 169.6 56666022 432.4
> stats <- microbenchmark(binCounts = binCounts(x, bx = bx), hist = binCounts_hist(x, bx = bx), unit = "ms")

Table: Benchmarking of binCounts() and hist() on double+unsorted data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 binCounts 7.074370 7.339281 7.665371 7.397135 7.530170 12.55017
2 hist 8.758879 9.131600 9.299926 9.230472 9.331357 14.05749
expr min lq mean median uq max
1 binCounts 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
2 hist 1.238114 1.244209 1.213239 1.247844 1.239196 1.120103

Figure: Benchmarking of binCounts() and hist() on double+unsorted data. Outliers are displayed as crosses. Times are in milliseconds.

Sorted simulated data

> x <- sort(x)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3053277 163.1    5709258 305.0  5709258 305.0
Vcells 5056911  38.6   22267496 169.9 56666022 432.4
> stats <- microbenchmark(binCounts = binCounts(x, bx = bx), hist = binCounts_hist(x, bx = bx), unit = "ms")

Table: Benchmarking of binCounts() and hist() on double+sorted data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 binCounts 0.627017 1.858442 2.001282 1.899146 1.933372 5.737497
2 hist 4.172153 4.382178 4.590442 4.465838 4.560886 8.298852
expr min lq mean median uq max
1 binCounts 1.000000 1.000000 1.00000 1.000000 1.000000 1.000000
2 hist 6.653971 2.357984 2.29375 2.351498 2.359031 1.446424

Figure: Benchmarking of binCounts() and hist() on double+sorted 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 7.73 secs.

Reproducibility

To reproduce this report, do:

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

Copyright Henrik Bengtsson. Last updated on 2019-09-10 20:34:10 (-0700 UTC). Powered by RSP.

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