matrixStats.benchmarks
product() benchmarks
This report benchmark the performance of product() against alternative methods.
Alternative methods
- product_R()
- prod()
where
> product_R <- function(x, na.rm = FALSE, ...) {
+ if (length(x) == 0L)
+ return(0)
+ if (na.rm) {
+ x <- x[!is.na(x)]
+ }
+ if (is.integer(x) && any(x == 0))
+ return(0)
+ sign <- if (sum(x < 0)%%2 == 0)
+ +1 else -1
+ x <- abs(x)
+ x <- log(x)
+ x <- sum(x, na.rm = FALSE)
+ x <- exp(x)
+ y <- sign * x
+ y
+ }
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"]]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5350780 285.8 7916910 422.9 7916910 422.9
Vcells 13622559 104.0 35130986 268.1 94934136 724.3
> stats <- microbenchmark(product = product(x, na.rm = FALSE), product_R = product_R(x, na.rm = FALSE),
+ prod = prod(x, na.rm = FALSE), unit = "ms")
Table: Benchmarking of product(), product_R() and prod() 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 | |
|---|---|---|---|---|---|---|---|
| 3 | prod | 0.002038 | 0.0021310 | 0.0022307 | 0.0022010 | 0.0022685 | 0.004069 |
| 1 | product | 0.028761 | 0.0297330 | 0.0316324 | 0.0309230 | 0.0323755 | 0.053376 |
| 2 | product_R | 0.032185 | 0.0336955 | 0.1690530 | 0.0344495 | 0.0358210 | 13.334780 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 3 | prod | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.00000 |
| 1 | product | 14.11237 | 13.95260 | 14.18053 | 14.04952 | 14.27177 | 13.11772 |
| 2 | product_R | 15.79244 | 15.81206 | 75.78508 | 15.65175 | 15.79061 | 3277.16392 |
Figure: Benchmarking of product(), product_R() and prod() on n = 1000 data. Outliers are displayed as crosses. Times are in milliseconds.
n = 10000 vector
> x <- data[["n = 10000"]]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5348608 285.7 7916910 422.9 7916910 422.9
Vcells 11438369 87.3 35130986 268.1 94934136 724.3
> stats <- microbenchmark(product = product(x, na.rm = FALSE), product_R = product_R(x, na.rm = FALSE),
+ prod = prod(x, na.rm = FALSE), unit = "ms")
Table: Benchmarking of product(), product_R() and prod() 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 | 0.220377 | 0.221737 | 0.2512977 | 0.2230715 | 0.2759320 | 0.395691 |
| 2 | product_R | 0.222116 | 0.226149 | 0.2527879 | 0.2298840 | 0.2675845 | 0.377672 |
| 3 | prod | 0.634510 | 0.638161 | 0.7171957 | 0.6449205 | 0.7744755 | 1.087187 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | product | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.0000000 |
| 2 | product_R | 1.007891 | 1.019897 | 1.005930 | 1.030539 | 0.969748 | 0.9544619 |
| 3 | prod | 2.879202 | 2.878009 | 2.853968 | 2.891093 | 2.806762 | 2.7475657 |
Figure: Benchmarking of product(), product_R() and prod() on n = 10000 data. Outliers are displayed as crosses. Times are in milliseconds.
n = 100000 vector
> x <- data[["n = 100000"]]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5348680 285.7 7916910 422.9 7916910 422.9
Vcells 11438929 87.3 35130986 268.1 94934136 724.3
> stats <- microbenchmark(product = product(x, na.rm = FALSE), product_R = product_R(x, na.rm = FALSE),
+ prod = prod(x, na.rm = FALSE), unit = "ms")
Table: Benchmarking of product(), product_R() and prod() 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 | 2.185606 | 2.217324 | 2.273853 | 2.222247 | 2.257988 | 3.782774 |
| 2 | product_R | 2.227848 | 2.248203 | 2.695299 | 2.758787 | 2.834966 | 9.495283 |
| 3 | prod | 8.890270 | 8.941182 | 9.107281 | 8.967258 | 9.011705 | 12.723921 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | product | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 2 | product_R | 1.019327 | 1.013926 | 1.185345 | 1.241440 | 1.255527 | 2.510137 |
| 3 | prod | 4.067645 | 4.032420 | 4.005220 | 4.035221 | 3.991033 | 3.363648 |
Figure: Benchmarking of product(), product_R() and prod() on n = 100000 data. Outliers are displayed as crosses. Times are in milliseconds.
n = 1000000 vector
> x <- data[["n = 1000000"]]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5348752 285.7 7916910 422.9 7916910 422.9
Vcells 11438978 87.3 35130986 268.1 94934136 724.3
> stats <- microbenchmark(product = product(x, na.rm = FALSE), product_R = product_R(x, na.rm = FALSE),
+ prod = prod(x, na.rm = FALSE), unit = "ms")
Table: Benchmarking of product(), product_R() and prod() 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 | 22.44630 | 24.55837 | 25.50037 | 25.56487 | 26.26367 | 32.63564 |
| 2 | product_R | 23.68967 | 25.63503 | 27.83175 | 26.16565 | 30.08947 | 49.42671 |
| 3 | prod | 91.94983 | 92.49794 | 95.95973 | 93.17209 | 98.70533 | 111.36217 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | product | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 2 | product_R | 1.055393 | 1.043841 | 1.091425 | 1.023500 | 1.145669 | 1.514501 |
| 3 | prod | 4.096436 | 3.766453 | 3.763072 | 3.644536 | 3.758246 | 3.412288 |
Figure: Benchmarking of product(), product_R() and prod() 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 22.3 secs.
Reproducibility
To reproduce this report, do:
html <- matrixStats:::benchmark('product')
Copyright Henrik Bengtsson. Last updated on 2021-08-25 22:46:39 (+0200 UTC). Powered by RSP.