logSumExp - HenrikBengtsson/matrixStats GitHub Wiki
matrixStats: Benchmark report
This report benchmark the performance of logSumExp() against alternative methods.
- logSumExp_R()
where
> logSumExp_R <- function(lx, ...) {
+ iMax <- which.max(lx)
+ log1p(sum(exp(lx[-iMax] - lx[iMax]))) + lx[iMax]
+ }> 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 = "double")
> data <- data[1:4]> x <- data[["n = 1000"]]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 3233776 172.8 5709258 305.0 5709258 305.0
Vcells 9537828 72.8 24515964 187.1 57084605 435.6
> stats <- microbenchmark(logSumExp = logSumExp(x), logSumExp_R = logSumExp_R(x), unit = "ms")Table: Benchmarking of logSumExp() and logSumExp_R() on 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 | logSumExp | 0.013519 | 0.0136465 | 0.0140762 | 0.0137110 | 0.0138315 | 0.042578 |
| 2 | logSumExp_R | 0.018184 | 0.0186770 | 0.0195850 | 0.0189745 | 0.0196315 | 0.050636 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | logSumExp | 1.00000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 2 | logSumExp_R | 1.34507 | 1.368629 | 1.391356 | 1.383889 | 1.419333 | 1.189253 |
Figure: Benchmarking of logSumExp() and logSumExp_R() on n = 1000 data. Outliers are displayed as crosses. Times are in milliseconds.
> x <- data[["n = 10000"]]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 3231548 172.6 5709258 305.0 5709258 305.0
Vcells 7352879 56.1 24515964 187.1 57084605 435.6
> stats <- microbenchmark(logSumExp = logSumExp(x), logSumExp_R = logSumExp_R(x), unit = "ms")Table: Benchmarking of logSumExp() and logSumExp_R() on 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 | logSumExp | 0.129478 | 0.1301875 | 0.1335535 | 0.1331955 | 0.1369590 | 0.149231 |
| 2 | logSumExp_R | 0.160116 | 0.1629015 | 0.1673604 | 0.1682595 | 0.1702905 | 0.182990 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | logSumExp | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.00000 |
| 2 | logSumExp_R | 1.236627 | 1.251284 | 1.253133 | 1.263252 | 1.243369 | 1.22622 |
Figure: Benchmarking of logSumExp() and logSumExp_R() on n = 10000 data. Outliers are displayed as crosses. Times are in milliseconds.
> x <- data[["n = 100000"]]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 3231611 172.6 5709258 305.0 5709258 305.0
Vcells 7352921 56.1 24515964 187.1 57084605 435.6
> stats <- microbenchmark(logSumExp = logSumExp(x), logSumExp_R = logSumExp_R(x), unit = "ms")Table: Benchmarking of logSumExp() and logSumExp_R() on 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 | logSumExp | 1.297379 | 1.406465 | 1.426832 | 1.436896 | 1.449534 | 1.599034 |
| 2 | logSumExp_R | 1.566383 | 1.770138 | 2.034313 | 1.826766 | 2.242275 | 7.980019 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | logSumExp | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 2 | logSumExp_R | 1.207344 | 1.258573 | 1.425756 | 1.271328 | 1.546894 | 4.990525 |
Figure: Benchmarking of logSumExp() and logSumExp_R() on n = 100000 data. Outliers are displayed as crosses. Times are in milliseconds.
> x <- data[["n = 1000000"]]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 3231674 172.6 5709258 305.0 5709258 305.0
Vcells 7353476 56.2 24515964 187.1 57084605 435.6
> stats <- microbenchmark(logSumExp = logSumExp(x), logSumExp_R = logSumExp_R(x), unit = "ms")Table: Benchmarking of logSumExp() and logSumExp_R() on 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 | logSumExp | 13.14968 | 14.46200 | 14.53545 | 14.60917 | 14.76055 | 15.22525 |
| 2 | logSumExp_R | 16.84121 | 18.01362 | 19.41297 | 18.21380 | 18.44415 | 31.28326 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | logSumExp | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 2 | logSumExp_R | 1.280731 | 1.245582 | 1.335561 | 1.246738 | 1.249558 | 2.054696 |
Figure: Benchmarking of logSumExp() and logSumExp_R() on n = 1000000 data. Outliers are displayed as crosses. Times are in milliseconds.
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 8.57 secs.
To reproduce this report, do:
html <- matrixStats:::benchmark('logSumExp')Copyright Henrik Bengtsson. Last updated on 2019-09-10 20:58:37 (-0700 UTC). Powered by RSP.
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