binMeans_subset - HenrikBengtsson/matrixStats GitHub Wiki
matrixStats: Benchmark report
This report benchmark the performance of binMeans() on subsetted computation.
> nx <- 1e+05
> set.seed(48879)
> x <- runif(nx, min = 0, max = 1)
> y <- runif(nx, min = 0, max = 1)
> nb <- 1000
> bx <- seq(from = 0, to = 1, length.out = nb + 1L)
> bx <- c(-1, bx, 2)
> idxs <- sample.int(length(x), size = length(x) * 0.7)> x_S <- x[idxs]
> y_S <- y[idxs]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 3056266 163.3 5709258 305.0 5709258 305.0
Vcells 5455271 41.7 22267496 169.9 56666022 432.4
> stats <- microbenchmark(binMeans_x_y_S = binMeans(x = x_S, y = y_S, bx = bx, count = TRUE), `binMeans(x, y, idxs)` = binMeans(x = x,
+ y = y, idxs = idxs, bx = bx, count = TRUE), `binMeans(x[idxs], y[idxs])` = binMeans(x = x[idxs],
+ y = y[idxs], bx = bx, count = TRUE), unit = "ms")Table: Benchmarking of binMeans_x_y_S(), binMeans(x, y, idxs)() and binMeans(x[idxs], y[idxs])() on unsorted data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | binMeans_x_y_S | 5.248795 | 5.374193 | 5.601428 | 5.497117 | 5.569229 | 9.767022 |
| 3 | binMeans(x[idxs], y[idxs]) | 5.789024 | 5.950575 | 6.290929 | 6.044293 | 6.149934 | 10.290326 |
| 2 | binMeans(x, y, idxs) | 6.071562 | 6.262751 | 6.562123 | 6.334817 | 6.477821 | 12.609668 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | binMeans_x_y_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 3 | binMeans(x[idxs], y[idxs]) | 1.102924 | 1.107250 | 1.123094 | 1.099539 | 1.104270 | 1.053579 |
| 2 | binMeans(x, y, idxs) | 1.156753 | 1.165338 | 1.171509 | 1.152389 | 1.163145 | 1.291045 |
Figure: Benchmarking of binMeans_x_y_S(), binMeans(x, y, idxs)() and binMeans(x[idxs], y[idxs])() on unsorted data. Outliers are displayed as crosses. Times are in milliseconds.
> x <- sort(x)
> idxs <- sort(idxs)> x_S <- x[idxs]
> y_S <- y[idxs]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 3054150 163.2 5709258 305.0 5709258 305.0
Vcells 5343765 40.8 22267496 169.9 56666022 432.4
> stats <- microbenchmark(binMeans_x_y_S = binMeans(x = x_S, y = y_S, bx = bx, count = TRUE), `binMeans(x, y, idxs)` = binMeans(x = x,
+ y = y, idxs = idxs, bx = bx, count = TRUE), `binMeans(x[idxs], y[idxs])` = binMeans(x = x[idxs],
+ y = y[idxs], bx = bx, count = TRUE), unit = "ms")Table: Benchmarking of binMeans_x_y_S(), binMeans(x, y, idxs)() and binMeans(x[idxs], y[idxs])() on sorted data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | binMeans_x_y_S | 1.510340 | 1.592269 | 1.783127 | 1.627860 | 1.660300 | 5.024760 |
| 3 | binMeans(x[idxs], y[idxs]) | 1.903016 | 1.998981 | 2.184200 | 2.025745 | 2.057967 | 6.912624 |
| 2 | binMeans(x, y, idxs) | 2.190399 | 2.330208 | 2.677876 | 2.357755 | 2.416732 | 5.810211 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | binMeans_x_y_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 3 | binMeans(x[idxs], y[idxs]) | 1.259992 | 1.255429 | 1.224927 | 1.244422 | 1.239515 | 1.375712 |
| 2 | binMeans(x, y, idxs) | 1.450269 | 1.463452 | 1.501787 | 1.448378 | 1.455599 | 1.156316 |
Figure: Benchmarking of binMeans_x_y_S(), binMeans(x, y, idxs)() and binMeans(x[idxs], y[idxs])() on sorted 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 4.18 secs.
To reproduce this report, do:
html <- matrixStats:::benchmark('binMeans')Copyright Dongcan Jiang. Last updated on 2019-09-10 20:34:15 (-0700 UTC). Powered by RSP.
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