matrixStats.benchmarks
logSumExp() benchmarks on subsetted computation
This report benchmark the performance of logSumExp() on subsetted computation.
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 = "double")
> data <- data[1:4]
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 5344003 285.5 7916910 422.9 7916910 422.9
Vcells 11773401 89.9 36893127 281.5 57430649 438.2
> stats <- microbenchmark(logSumExp_x_S = logSumExp(x_S), `logSumExp(x, idxs)` = logSumExp(x, idxs = idxs),
+ `logSumExp(x[idxs])` = logSumExp(x[idxs]), unit = "ms")
Table: Benchmarking of logSumExp_x_S(), logSumExp(x, idxs)() and logSumExp(x[idxs])() 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_x_S | 0.015992 | 0.0167825 | 0.0173882 | 0.0168795 | 0.0169820 | 0.037199 |
| 3 | logSumExp(x[idxs]) | 0.018401 | 0.0193560 | 0.0214968 | 0.0195630 | 0.0197525 | 0.119437 |
| 2 | logSumExp(x, idxs) | 0.019504 | 0.0203955 | 0.0205936 | 0.0205040 | 0.0206095 | 0.030093 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | logSumExp_x_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.0000000 |
| 3 | logSumExp(x[idxs]) | 1.150638 | 1.153344 | 1.236287 | 1.158980 | 1.163143 | 3.2107584 |
| 2 | logSumExp(x, idxs) | 1.219610 | 1.215284 | 1.184344 | 1.214728 | 1.213609 | 0.8089734 |
Figure: Benchmarking of logSumExp_x_S(), logSumExp(x, idxs)() and logSumExp(x[idxs])() on 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 5340765 285.3 7916910 422.9 7916910 422.9
Vcells 11439488 87.3 36893127 281.5 57430649 438.2
> stats <- microbenchmark(logSumExp_x_S = logSumExp(x_S), `logSumExp(x, idxs)` = logSumExp(x, idxs = idxs),
+ `logSumExp(x[idxs])` = logSumExp(x[idxs]), unit = "ms")
Table: Benchmarking of logSumExp_x_S(), logSumExp(x, idxs)() and logSumExp(x[idxs])() 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_x_S | 0.107942 | 0.1155185 | 0.1326706 | 0.1234485 | 0.148291 | 0.227294 |
| 3 | logSumExp(x[idxs]) | 0.122316 | 0.1346590 | 0.1504823 | 0.1405490 | 0.163293 | 0.267099 |
| 2 | logSumExp(x, idxs) | 0.138869 | 0.1499025 | 0.1718531 | 0.1632120 | 0.190533 | 0.282027 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | logSumExp_x_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 3 | logSumExp(x[idxs]) | 1.133164 | 1.165692 | 1.134256 | 1.138523 | 1.101166 | 1.175126 |
| 2 | logSumExp(x, idxs) | 1.286515 | 1.297649 | 1.295337 | 1.322106 | 1.284859 | 1.240803 |
Figure: Benchmarking of logSumExp_x_S(), logSumExp(x, idxs)() and logSumExp(x[idxs])() on 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 5340837 285.3 7916910 422.9 7916910 422.9
Vcells 11534548 88.1 36893127 281.5 57430649 438.2
> stats <- microbenchmark(logSumExp_x_S = logSumExp(x_S), `logSumExp(x, idxs)` = logSumExp(x, idxs = idxs),
+ `logSumExp(x[idxs])` = logSumExp(x[idxs]), unit = "ms")
Table: Benchmarking of logSumExp_x_S(), logSumExp(x, idxs)() and logSumExp(x[idxs])() 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_x_S | 0.905993 | 0.9066275 | 0.9577668 | 0.9084235 | 0.921494 | 1.597944 |
| 3 | logSumExp(x[idxs]) | 1.085253 | 1.0978600 | 1.1642811 | 1.1007840 | 1.109797 | 1.930893 |
| 2 | logSumExp(x, idxs) | 1.463108 | 1.4638735 | 1.5068310 | 1.4664320 | 1.468910 | 2.205679 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | logSumExp_x_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 3 | logSumExp(x[idxs]) | 1.197860 | 1.210927 | 1.215621 | 1.211752 | 1.204346 | 1.208361 |
| 2 | logSumExp(x, idxs) | 1.614922 | 1.614636 | 1.573276 | 1.614260 | 1.594053 | 1.380323 |
Figure: Benchmarking of logSumExp_x_S(), logSumExp(x, idxs)() and logSumExp(x[idxs])() on 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 5340909 285.3 7916910 422.9 7916910 422.9
Vcells 12479597 95.3 36893127 281.5 57430649 438.2
> stats <- microbenchmark(logSumExp_x_S = logSumExp(x_S), `logSumExp(x, idxs)` = logSumExp(x, idxs = idxs),
+ `logSumExp(x[idxs])` = logSumExp(x[idxs]), unit = "ms")
Table: Benchmarking of logSumExp_x_S(), logSumExp(x, idxs)() and logSumExp(x[idxs])() 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_x_S | 9.222615 | 11.48087 | 12.64326 | 12.42652 | 13.75433 | 16.48410 |
| 3 | logSumExp(x[idxs]) | 18.627014 | 21.55948 | 23.33542 | 22.88887 | 24.69123 | 38.75325 |
| 2 | logSumExp(x, idxs) | 34.404055 | 40.45209 | 46.56888 | 46.82454 | 51.61983 | 66.48265 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | logSumExp_x_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 3 | logSumExp(x[idxs]) | 2.019711 | 1.877861 | 1.845681 | 1.841938 | 1.795161 | 2.350947 |
| 2 | logSumExp(x, idxs) | 3.730401 | 3.523433 | 3.683296 | 3.768114 | 3.752988 | 4.033137 |
Figure: Benchmarking of logSumExp_x_S(), logSumExp(x, idxs)() and logSumExp(x[idxs])() on n = 1000000 data. Outliers are displayed as crosses. Times are in milliseconds.
Appendix
Session information
R version 4.1.1 Patched (2021-08-10 r80727)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.5 LTS
Matrix products: default
BLAS: /home/hb/software/R-devel/R-4-1-branch/lib/R/lib/libRblas.so
LAPACK: /home/hb/software/R-devel/R-4-1-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_1.4-7 matrixStats_0.60.0 ggplot2_3.3.5
[4] knitr_1.33 R.devices_2.17.0 R.utils_2.10.1
[7] R.oo_1.24.0 R.methodsS3_1.8.1-9001 history_0.0.1-9000
loaded via a namespace (and not attached):
[1] Biobase_2.52.0 httr_1.4.2 splines_4.1.1
[4] bit64_4.0.5 network_1.17.1 assertthat_0.2.1
[7] highr_0.9 stats4_4.1.1 blob_1.2.2
[10] GenomeInfoDbData_1.2.6 robustbase_0.93-8 pillar_1.6.2
[13] RSQLite_2.2.8 lattice_0.20-44 glue_1.4.2
[16] digest_0.6.27 XVector_0.32.0 colorspace_2.0-2
[19] Matrix_1.3-4 XML_3.99-0.7 pkgconfig_2.0.3
[22] zlibbioc_1.38.0 genefilter_1.74.0 purrr_0.3.4
[25] ergm_4.1.2 xtable_1.8-4 scales_1.1.1
[28] tibble_3.1.4 annotate_1.70.0 KEGGREST_1.32.0
[31] farver_2.1.0 generics_0.1.0 IRanges_2.26.0
[34] ellipsis_0.3.2 cachem_1.0.6 withr_2.4.2
[37] BiocGenerics_0.38.0 mime_0.11 survival_3.2-13
[40] magrittr_2.0.1 crayon_1.4.1 statnet.common_4.5.0
[43] memoise_2.0.0 laeken_0.5.1 fansi_0.5.0
[46] R.cache_0.15.0 MASS_7.3-54 R.rsp_0.44.0
[49] progressr_0.8.0 tools_4.1.1 lifecycle_1.0.0
[52] S4Vectors_0.30.0 trust_0.1-8 munsell_0.5.0
[55] tabby_0.0.1-9001 AnnotationDbi_1.54.1 Biostrings_2.60.2
[58] compiler_4.1.1 GenomeInfoDb_1.28.1 rlang_0.4.11
[61] grid_4.1.1 RCurl_1.98-1.4 cwhmisc_6.6
[64] rstudioapi_0.13 rappdirs_0.3.3 startup_0.15.0-9000
[67] labeling_0.4.2 bitops_1.0-7 base64enc_0.1-3
[70] boot_1.3-28 gtable_0.3.0 DBI_1.1.1
[73] markdown_1.1 R6_2.5.1 lpSolveAPI_5.5.2.0-17.7
[76] rle_0.9.2 dplyr_1.0.7 fastmap_1.1.0
[79] bit_4.0.4 utf8_1.2.2 parallel_4.1.1
[82] Rcpp_1.0.7 vctrs_0.3.8 png_0.1-7
[85] DEoptimR_1.0-9 tidyselect_1.1.1 xfun_0.25
[88] coda_0.19-4
Total processing time was 12.95 secs.
Reproducibility
To reproduce this report, do:
html <- matrixStats:::benchmark('logSumExp_subset')
Copyright Dongcan Jiang. Last updated on 2021-08-25 22:35:17 (+0200 UTC). Powered by RSP.