matrixStats.benchmarks
binMeans() benchmarks on subsetted computation
This report benchmark the performance of binMeans() on subsetted computation.
Results
Non-sorted simulated data
> 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 5157490 275.5 8529671 455.6 8529671 455.6
Vcells 9517473 72.7 31876688 243.2 60562128 462.1
> 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 | 5.347789 | 5.724487 | 5.995978 | 5.835461 | 5.985759 | 11.25192 |
| 3 | binMeans(x[idxs], y[idxs]) | 5.926970 | 6.269387 | 6.673842 | 6.395594 | 6.580064 | 10.82776 |
| 2 | binMeans(x, y, idxs) | 6.222204 | 6.570496 | 6.887507 | 6.705499 | 6.872798 | 11.64922 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | binMeans_x_y_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.0000000 |
| 3 | binMeans(x[idxs], y[idxs]) | 1.108303 | 1.095188 | 1.113053 | 1.095988 | 1.099287 | 0.9623029 |
| 2 | binMeans(x, y, idxs) | 1.163510 | 1.147788 | 1.148688 | 1.149095 | 1.148192 | 1.0353095 |
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.
Sorted simulated data
> x <- sort(x)
> idxs <- sort(idxs)
> x_S <- x[idxs]
> y_S <- y[idxs]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5148369 275.0 8529671 455.6 8529671 455.6
Vcells 9382327 71.6 31876688 243.2 60562128 462.1
> 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 | 1.631542 | 1.747900 | 1.915011 | 1.797554 | 1.839143 | 5.369798 |
| 3 | binMeans(x[idxs], y[idxs]) | 1.939260 | 2.083006 | 2.223110 | 2.129283 | 2.184973 | 5.761303 |
| 2 | binMeans(x, y, idxs) | 2.246772 | 2.413905 | 2.637575 | 2.487077 | 2.547488 | 6.118683 |
| 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]) | 1.188606 | 1.191719 | 1.160886 | 1.184545 | 1.188039 | 1.072909 |
| 2 | binMeans(x, y, idxs) | 1.377085 | 1.381031 | 1.377316 | 1.383589 | 1.385150 | 1.139462 |
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.
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_0.60.1 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] rappdirs_0.3.3 startup_0.15.0 labeling_0.4.2
[67] bitops_1.0-7 base64enc_0.1-3 boot_1.3-28
[70] gtable_0.3.0 DBI_1.1.1 markdown_1.1
[73] R6_2.5.1 lpSolveAPI_5.5.2.0-17.7 rle_0.9.2
[76] dplyr_1.0.7 fastmap_1.1.0 bit_4.0.4
[79] utf8_1.2.2 parallel_4.1.1 Rcpp_1.0.7
[82] vctrs_0.3.8 png_0.1-7 DEoptimR_1.0-9
[85] tidyselect_1.1.1 xfun_0.25 coda_0.19-4
Total processing time was 4.37 secs.
Reproducibility
To reproduce this report, do:
html <- matrixStats:::benchmark('binMeans')
Copyright Dongcan Jiang. Last updated on 2021-08-25 18:49:05 (+0200 UTC). Powered by RSP.