matrixStats.benchmarks
product() benchmarks on subsetted computation
This report benchmark the performance of product() 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 5350371 285.8 7916910 422.9 7916910 422.9
Vcells 26574849 202.8 54892165 418.8 94934136 724.3
> stats <- microbenchmark(product_x_S = product(x_S, na.rm = FALSE), `product(x, idxs)` = product(x,
+ idxs = idxs, na.rm = FALSE), `product(x[idxs])` = product(x[idxs], na.rm = FALSE), unit = "ms")
Table: Benchmarking of product_x_S(), product(x, idxs)() and product(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 | product_x_S | 0.018551 | 0.0192720 | 0.0198941 | 0.0197205 | 0.0202295 | 0.034530 |
| 2 | product(x, idxs) | 0.019637 | 0.0204445 | 0.0213027 | 0.0210520 | 0.0218930 | 0.028677 |
| 3 | product(x[idxs]) | 0.020544 | 0.0216435 | 0.0226630 | 0.0222330 | 0.0228465 | 0.050459 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | product_x_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.0000000 |
| 2 | product(x, idxs) | 1.058541 | 1.060840 | 1.070806 | 1.067519 | 1.082231 | 0.8304952 |
| 3 | product(x[idxs]) | 1.107434 | 1.123054 | 1.139185 | 1.127406 | 1.129366 | 1.4613090 |
Figure: Benchmarking of product_x_S(), product(x, idxs)() and product(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 5347786 285.7 7916910 422.9 7916910 422.9
Vcells 11445402 87.4 43913732 335.1 94934136 724.3
> stats <- microbenchmark(product_x_S = product(x_S, na.rm = FALSE), `product(x, idxs)` = product(x,
+ idxs = idxs, na.rm = FALSE), `product(x[idxs])` = product(x[idxs], na.rm = FALSE), unit = "ms")
Table: Benchmarking of product_x_S(), product(x, idxs)() and product(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 | product_x_S | 0.153597 | 0.1589205 | 0.1889438 | 0.1857690 | 0.2083460 | 0.256036 |
| 3 | product(x[idxs]) | 0.167638 | 0.1726615 | 0.2010916 | 0.1930770 | 0.2192160 | 0.311540 |
| 2 | product(x, idxs) | 0.183204 | 0.1894220 | 0.2252754 | 0.2187645 | 0.2458025 | 0.311680 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | product_x_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 3 | product(x[idxs]) | 1.091414 | 1.086465 | 1.064293 | 1.039339 | 1.052173 | 1.216782 |
| 2 | product(x, idxs) | 1.192758 | 1.191929 | 1.192288 | 1.177616 | 1.179780 | 1.217329 |
Figure: Benchmarking of product_x_S(), product(x, idxs)() and product(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 5347858 285.7 7916910 422.9 7916910 422.9
Vcells 11540462 88.1 35130986 268.1 94934136 724.3
> stats <- microbenchmark(product_x_S = product(x_S, na.rm = FALSE), `product(x, idxs)` = product(x,
+ idxs = idxs, na.rm = FALSE), `product(x[idxs])` = product(x[idxs], na.rm = FALSE), unit = "ms")
Table: Benchmarking of product_x_S(), product(x, idxs)() and product(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 | product_x_S | 1.549952 | 1.688692 | 1.730460 | 1.727593 | 1.733352 | 2.548933 |
| 3 | product(x[idxs]) | 1.738329 | 1.899376 | 1.951108 | 1.935016 | 1.943423 | 2.839429 |
| 2 | product(x, idxs) | 2.248668 | 2.495837 | 2.498071 | 2.513803 | 2.519142 | 2.849638 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | product_x_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 3 | product(x[idxs]) | 1.121537 | 1.124761 | 1.127508 | 1.120065 | 1.121194 | 1.113968 |
| 2 | product(x, idxs) | 1.450799 | 1.477970 | 1.443588 | 1.455089 | 1.453335 | 1.117973 |
Figure: Benchmarking of product_x_S(), product(x, idxs)() and product(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 5347930 285.7 7916910 422.9 7916910 422.9
Vcells 12485511 95.3 35130986 268.1 94934136 724.3
> stats <- microbenchmark(product_x_S = product(x_S, na.rm = FALSE), `product(x, idxs)` = product(x,
+ idxs = idxs, na.rm = FALSE), `product(x[idxs])` = product(x[idxs], na.rm = FALSE), unit = "ms")
Table: Benchmarking of product_x_S(), product(x, idxs)() and product(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 | product_x_S | 15.60263 | 17.33577 | 19.97094 | 19.25093 | 22.90886 | 30.71439 |
| 3 | product(x[idxs]) | 24.81399 | 28.14037 | 30.92797 | 30.11235 | 33.43813 | 47.27077 |
| 2 | product(x, idxs) | 50.91459 | 58.90147 | 65.89198 | 64.07197 | 71.18637 | 95.78670 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | product_x_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 3 | product(x[idxs]) | 1.590372 | 1.623255 | 1.548649 | 1.564203 | 1.459616 | 1.539043 |
| 2 | product(x, idxs) | 3.263205 | 3.397684 | 3.299392 | 3.328254 | 3.107373 | 3.118626 |
Figure: Benchmarking of product_x_S(), product(x, idxs)() and product(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 16.47 secs.
Reproducibility
To reproduce this report, do:
html <- matrixStats:::benchmark('product_subset')
Copyright Dongcan Jiang. Last updated on 2021-08-25 22:46:16 (+0200 UTC). Powered by RSP.