Last updated: 2019-01-28

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Introduction

I benchmark the rewritten my_etruncnorm and my_vtruncnorm against their counterparts in package truncnorm. I expect truncnorm to be a bit faster since it calls into C, but my hope is that the difference isn’t too noticeable.

devtools::load_all("~/Github/ashr")
Loading ashr
library(truncnorm)

do_benchmark <- function(ns, FUN1, FUN2) {
  means <- matrix(0, nrow = 2, ncol = length(ns))
  medians <- matrix(0, nrow = 2, ncol = length(ns))
  for (i in 1:length(ns)) {
    times = floor(10000000 / ns[i])
    res <- microbenchmark::microbenchmark(FUN1(a, b), 
                                          FUN2(a, b), 
                                          setup = {
                                            a = -abs(rnorm(ns[i]))
                                            b = abs(rnorm(ns[i]))
                                          }, 
                                          times = times,
                                          unit = "ms")
    res <- summary(res)
    means[, i] <- res$mean
    medians[, i] <- res$median
  }
  return(list(means = means, medians = medians))
}

etruncnorm

ns <- 10^seq(1, 6, by = 0.5)
res <- do_benchmark(ns, etruncnorm, my_etruncnorm)
ymin <- log10(min(c(res$means, res$medians)))
ymax <- log10(max(c(res$means, res$medians)))
plot(log10(ns), log10(res$means[2, ]), type = 'l', lty = 1, col = "red",
     xlab = "log10(n)", ylab = "log10(ms)", ylim = c(ymin, ymax))
lines(log10(ns), log10(res$medians[2, ]), col = "red", lty = 2)
lines(log10(ns), log10(res$means[1, ]), col = "black", lty = 1)
lines(log10(ns), log10(res$medians[1, ]), col = "black", lty = 2)
legend("topleft", legend = c("my_etrunc (mean)", "my_etrunc (median)",
                             "etrunc (mean)", "etrunc (median)"), 
       lty = c(1, 2, 1, 2), col = c("red", "red", "black", "black"))

Expand here to see past versions of etruncnorm-1.png:
Version Author Date
5e3a6c1 Jason Willwerscheid 2019-01-26

vtruncnorm

res <- do_benchmark(ns, vtruncnorm, my_vtruncnorm)
ymin <- log10(min(c(res$means, res$medians)))
ymax <- log10(max(c(res$means, res$medians)))
plot(log10(ns), log10(res$means[2, ]), type = 'l', lty = 1, col = "red",
     xlab = "log10(n)", ylab = "log10(ms)", ylim = c(ymin, ymax))
lines(log10(ns), log10(res$medians[2, ]), col = "red", lty = 2)
lines(log10(ns), log10(res$means[1, ]), col = "black", lty = 1)
lines(log10(ns), log10(res$medians[1, ]), col = "black", lty = 2)
legend("topleft", legend = c("my_vtrunc (mean)", "my_vtrunc (median)",
                             "vtrunc (mean)", "vtrunc (median)"), 
       lty = c(1, 2, 1, 2), col = c("red", "red", "black", "black"))

Expand here to see past versions of vtruncnorm-1.png:
Version Author Date
5e3a6c1 Jason Willwerscheid 2019-01-26

Session information

sessionInfo()
R version 3.4.3 (2017-11-30)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS High Sierra 10.13.6

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] truncnorm_1.0-8 ashr_2.2-27    

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.0           compiler_3.4.3       git2r_0.21.0        
 [4] workflowr_1.0.1      R.methodsS3_1.7.1    R.utils_2.6.0       
 [7] iterators_1.0.10     tools_3.4.3          testthat_2.0.1      
[10] digest_0.6.18        etrunct_0.1          evaluate_0.12       
[13] memoise_1.1.0        lattice_0.20-35      rlang_0.3.0.1       
[16] Matrix_1.2-14        foreach_1.4.4        microbenchmark_1.4-6
[19] commonmark_1.4       yaml_2.2.0           parallel_3.4.3      
[22] mvtnorm_1.0-7        xfun_0.4             withr_2.1.2.9000    
[25] stringr_1.3.1        roxygen2_6.0.1.9000  xml2_1.2.0          
[28] knitr_1.21.6         devtools_1.13.4      rprojroot_1.3-2     
[31] grid_3.4.3           R6_2.3.0             survival_2.41-3     
[34] rmarkdown_1.11       multcomp_1.4-8       mixsqp_0.1-93       
[37] TH.data_1.0-8        magrittr_1.5         whisker_0.3-2       
[40] splines_3.4.3        backports_1.1.2      codetools_0.2-15    
[43] htmltools_0.3.6      MASS_7.3-48          assertthat_0.2.0    
[46] sandwich_2.4-0       stringi_1.2.4        doParallel_1.0.14   
[49] pscl_1.5.2           SQUAREM_2017.10-1    zoo_1.8-1           
[52] R.oo_1.21.0

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