matrixStats.benchmarks
binCounts() benchmarks on subsetted computation
This report benchmark the performance of binCounts() on subsetted computation.
Data type “integer”
Non-sorted simulated data
> set.seed(48879)
> nx <- 1e+05
> xmax <- 0.01 * nx
> x <- runif(nx, min = 0, max = xmax)
> storage.mode(x) <- mode
> str(x)
int [1:100000] 722 285 591 3 349 509 216 91 150 383 ...
> nb <- 10000
> bx <- seq(from = 0, to = xmax, length.out = nb + 1L)
> bx <- c(-1, bx, xmax + 1)
> idxs <- sample.int(length(x), size = length(x) * 0.7)
Results
> x_S <- x[idxs]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5163985 275.8 7916910 422.9 7916910 422.9
Vcells 20278435 154.8 51861176 395.7 53339345 407.0
> stats <- microbenchmark(binCounts_x_S = binCounts(x_S, bx = bx), `binCounts(x, idxs)` = binCounts(x,
+ idxs = idxs, bx = bx), `binCounts(x[idxs])` = binCounts(x[idxs], bx = bx), unit = "ms")
Table: Benchmarking of binCounts_x_S(), binCounts(x, idxs)() and binCounts(x[idxs])() on integer+unsorted data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | binCounts_x_S | 3.719853 | 3.763632 | 4.208897 | 3.880740 | 4.545130 | 9.346291 |
| 2 | binCounts(x, idxs) | 3.874295 | 3.943125 | 4.437107 | 4.300498 | 4.783243 | 10.224092 |
| 3 | binCounts(x[idxs]) | 3.857082 | 3.923934 | 4.395350 | 4.358210 | 4.763532 | 5.781915 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | binCounts_x_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 2 | binCounts(x, idxs) | 1.041518 | 1.047691 | 1.054221 | 1.108164 | 1.052388 | 1.093920 |
| 3 | binCounts(x[idxs]) | 1.036891 | 1.042592 | 1.044300 | 1.123036 | 1.048052 | 0.618632 |
Figure: Benchmarking of binCounts_x_S(), binCounts(x, idxs)() and binCounts(x[idxs])() on integer+unsorted data. Outliers are displayed as crosses. Times are in milliseconds.
Sorted simulated data
> x <- sort(x)
> idxs <- sort(idxs)
> x_S <- x[idxs]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5149669 275.1 7916910 422.9 7916910 422.9
Vcells 9102809 69.5 41488941 316.6 53339345 407.0
> stats <- microbenchmark(binCounts_x_S = binCounts(x_S, bx = bx), `binCounts(x, idxs)` = binCounts(x,
+ idxs = idxs, bx = bx), `binCounts(x[idxs])` = binCounts(x[idxs], bx = bx), unit = "ms")
Table: Benchmarking of binCounts_x_S(), binCounts(x, idxs)() and binCounts(x[idxs])() on integer+sorted data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | binCounts_x_S | 0.389301 | 0.4274690 | 0.5977605 | 0.4648495 | 0.7413460 | 1.116528 |
| 3 | binCounts(x[idxs]) | 0.525772 | 0.5964815 | 0.7913820 | 0.6167385 | 0.7711025 | 4.826845 |
| 2 | binCounts(x, idxs) | 0.524227 | 0.5948650 | 0.8445269 | 0.6219030 | 0.9775805 | 4.338583 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | binCounts_x_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 3 | binCounts(x[idxs]) | 1.350554 | 1.395379 | 1.323911 | 1.326749 | 1.040139 | 4.323085 |
| 2 | binCounts(x, idxs) | 1.346585 | 1.391598 | 1.412818 | 1.337859 | 1.318656 | 3.885781 |
Figure: Benchmarking of binCounts_x_S(), binCounts(x, idxs)() and binCounts(x[idxs])() on integer+sorted data. Outliers are displayed as crosses. Times are in milliseconds.
Data type “double”
Non-sorted simulated data
> set.seed(48879)
> nx <- 1e+05
> xmax <- 0.01 * nx
> x <- runif(nx, min = 0, max = xmax)
> storage.mode(x) <- mode
> str(x)
num [1:100000] 722.11 285.54 591.33 3.42 349.14 ...
> nb <- 10000
> bx <- seq(from = 0, to = xmax, length.out = nb + 1L)
> bx <- c(-1, bx, xmax + 1)
> idxs <- sample.int(length(x), size = length(x) * 0.7)
Results
> x_S <- x[idxs]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5149745 275.1 7916910 422.9 7916910 422.9
Vcells 9188378 70.2 33191153 253.3 53339345 407.0
> stats <- microbenchmark(binCounts_x_S = binCounts(x_S, bx = bx), `binCounts(x, idxs)` = binCounts(x,
+ idxs = idxs, bx = bx), `binCounts(x[idxs])` = binCounts(x[idxs], bx = bx), unit = "ms")
Table: Benchmarking of binCounts_x_S(), binCounts(x, idxs)() and binCounts(x[idxs])() on double+unsorted data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | binCounts_x_S | 5.087195 | 5.127521 | 5.369601 | 5.169205 | 5.219648 | 10.126321 |
| 3 | binCounts(x[idxs]) | 5.305073 | 5.358559 | 5.534258 | 5.390321 | 5.502022 | 10.321932 |
| 2 | binCounts(x, idxs) | 5.273486 | 5.362463 | 5.528350 | 5.401738 | 5.526019 | 7.389614 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | binCounts_x_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.0000000 |
| 3 | binCounts(x[idxs]) | 1.042829 | 1.045058 | 1.030665 | 1.042776 | 1.054098 | 1.0193171 |
| 2 | binCounts(x, idxs) | 1.036620 | 1.045820 | 1.029564 | 1.044984 | 1.058696 | 0.7297432 |
Figure: Benchmarking of binCounts_x_S(), binCounts(x, idxs)() and binCounts(x[idxs])() on double+unsorted data. Outliers are displayed as crosses. Times are in milliseconds.
Sorted simulated data
> x <- sort(x)
> idxs <- sort(idxs)
> x_S <- x[idxs]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5149819 275.1 7916910 422.9 7916910 422.9
Vcells 9188427 70.2 33191153 253.3 53339345 407.0
> stats <- microbenchmark(binCounts_x_S = binCounts(x_S, bx = bx), `binCounts(x, idxs)` = binCounts(x,
+ idxs = idxs, bx = bx), `binCounts(x[idxs])` = binCounts(x[idxs], bx = bx), unit = "ms")
Table: Benchmarking of binCounts_x_S(), binCounts(x, idxs)() and binCounts(x[idxs])() on double+sorted data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | binCounts_x_S | 1.087128 | 1.129077 | 1.216472 | 1.161497 | 1.242320 | 1.891193 |
| 3 | binCounts(x[idxs]) | 1.242649 | 1.278549 | 1.428190 | 1.299653 | 1.403714 | 4.931474 |
| 2 | binCounts(x, idxs) | 1.239881 | 1.283872 | 1.407194 | 1.301867 | 1.436963 | 4.894416 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | binCounts_x_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 3 | binCounts(x[idxs]) | 1.143057 | 1.132384 | 1.174042 | 1.118946 | 1.129913 | 2.607600 |
| 2 | binCounts(x, idxs) | 1.140511 | 1.137099 | 1.156783 | 1.120853 | 1.156677 | 2.588004 |
Figure: Benchmarking of binCounts_x_S(), binCounts(x, idxs)() and binCounts(x[idxs])() on double+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_0.60.0 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
[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 7.05 secs.
Reproducibility
To reproduce this report, do:
html <- matrixStats:::benchmark('binCounts_subset')
Copyright Dongcan Jiang. Last updated on 2021-08-25 22:09:14 (+0200 UTC). Powered by RSP.