product_subset
matrixStats: Benchmark report
This report benchmark the performance of product() on subsetted computation.
> 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]> 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 3241875 173.2 5709258 305.0 5709258 305.0
Vcells 22502024 171.7 45062178 343.8 87357391 666.5
> 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 | 0.013097 | 0.0134985 | 0.0136697 | 0.0136450 | 0.0138260 | 0.014607 |
| 2 | product(x, idxs) | 0.013833 | 0.0143995 | 0.0147004 | 0.0146490 | 0.0148715 | 0.018537 |
| 3 | product(x[idxs]) | 0.014758 | 0.0151945 | 0.0157799 | 0.0153405 | 0.0155625 | 0.046348 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | product_x_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 2 | product(x, idxs) | 1.056196 | 1.066748 | 1.075403 | 1.073580 | 1.075618 | 1.269049 |
| 3 | product(x[idxs]) | 1.126823 | 1.125644 | 1.154374 | 1.124258 | 1.125597 | 3.172999 |
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.
> 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 3238002 173.0 5709258 305.0 5709258 305.0
Vcells 7368594 56.3 36049743 275.1 87357391 666.5
> 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 | 0.151525 | 0.1558065 | 0.1609891 | 0.1607430 | 0.1647510 | 0.181187 |
| 3 | product(x[idxs]) | 0.165618 | 0.1678545 | 0.1752373 | 0.1753845 | 0.1797005 | 0.204904 |
| 2 | product(x, idxs) | 0.174239 | 0.1789610 | 0.1853582 | 0.1848830 | 0.1897760 | 0.207322 |
| 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]) | 1.093008 | 1.077327 | 1.088504 | 1.091086 | 1.090740 | 1.130898 |
| 2 | product(x, idxs) | 1.149903 | 1.148611 | 1.151371 | 1.150178 | 1.151896 | 1.144243 |
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.
> 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 3238074 173 5709258 305.0 5709258 305.0
Vcells 7463654 57 28839795 220.1 87357391 666.5
> 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 | 1.494555 | 1.592543 | 1.641614 | 1.614740 | 1.644912 | 2.147136 |
| 3 | product(x[idxs]) | 1.673141 | 1.784554 | 1.844739 | 1.794579 | 1.836298 | 2.663072 |
| 2 | product(x, idxs) | 2.142434 | 2.262939 | 2.327280 | 2.322249 | 2.330865 | 3.082758 |
| 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]) | 1.119491 | 1.120569 | 1.123735 | 1.111373 | 1.116350 | 1.240290 |
| 2 | product(x, idxs) | 1.433493 | 1.420959 | 1.417678 | 1.438156 | 1.417015 | 1.435753 |
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.
> 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 3238146 173.0 5709258 305.0 5709258 305.0
Vcells 8408703 64.2 28839795 220.1 87357391 666.5
> 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 | 15.13279 | 15.91471 | 17.79098 | 17.73090 | 19.10965 | 23.61119 |
| 3 | product(x[idxs]) | 23.34935 | 26.21922 | 27.67434 | 26.89137 | 28.74615 | 43.28268 |
| 2 | product(x, idxs) | 41.12062 | 42.84058 | 47.22270 | 44.96709 | 49.25474 | 67.80077 |
| 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]) | 1.542965 | 1.647483 | 1.555526 | 1.516638 | 1.504274 | 1.833143 |
| 2 | product(x, idxs) | 2.717320 | 2.691885 | 2.654305 | 2.536086 | 2.577480 | 2.871552 |
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.
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 13.9 secs.
To reproduce this report, do:
html <- matrixStats:::benchmark('product_subset')Copyright Dongcan Jiang. Last updated on 2019-09-10 21:09:01 (-0700 UTC). Powered by RSP.
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