search for: microbenchmark

...very helpful in loops. fun0 <- function(x) sapply(x, paste, collapse="+") fun1 <- function(x) { paste <- selectMethod(paste, class(x[[1]])) sapply(x, paste, collapse="+") } lst <- split(rep(LETTERS, 100), rep(1:1300, 2)) library(microbenchmark) microbenchmark(fun0(lst), times=10) ## Unit: milliseconds ## expr min lq median uq max neval ## fun0(lst) 4.153287 4.180659 4.513539 5.19261 5.280481 10 setGeneric("paste") microbenchmark(fun0(lst), fun1(lst), times=10) ## > m...

...R and tried several ways to test if load(file.Rdata) or readRDS(file.rds) is faster. The files file.Rdata and file.rds contain the same data, the first created with save(d, ' file.Rdata', compress=F) and the second with saveRDS(d, ' file.rds', compress=F). First I used the function microbenchmark() and was a astonished about the max value of the output. FIRST TEST: > library(microbenchmark) > microbenchmark( + n <- readRDS('file.rds'), + load('file.Rdata') + ) Unit: milliseconds expr min lq...

Hello All, Need to coalesce some columns using R. Looked online to see how this is done. One approach appears to be to use ifelse. Also uncovered a coalesce function in the BBmisc, emoa, and microbenchmark packages. Trouble is I can't seem to get it to work in any of these packages. Or perhaps I misunderstand what it's intended to do. The documentation is generally pretty scant. Working with two columns: Date of Death (DOD) and Last Known Date Alive (LKDA). One or the other column is popula...

Hi all, I think there's an unnecessary line in [.Date which has a considerable impact on performance when subsetting large dates: x <- Sys.Date() + 1:1e6 microbenchmark::microbenchmark(x[1]) #> Unit: microseconds #> expr min lq mean median uq max neval #> x[1] 920.651 1039.346 3624.833 2294.404 3786.881 41176.38 100 `[.Date` <- function(x, ..., drop = TRUE) { cl <- oldClass(x) # class(x) <- NULL val &lt...

FYI, in R devel (to become 3.5.0), there's isFALSE() which will cut some corners compared to identical(): > microbenchmark::microbenchmark(identical(FALSE, FALSE), isFALSE(FALSE)) Unit: nanoseconds expr min lq mean median uq max neval identical(FALSE, FALSE) 984 1138 1694.13 1218.0 1337.5 13584 100 isFALSE(FALSE) 713 761 1133.53 809.5 871.5 18619 100 > microbenchmark...

...' is overwritten: parse(text="a <- 5") # Works require(R.utils) require(roxygen) parse(text="a <- 5") # Does not work anymore ################# START A NEW R SESSION BEFORE YOU CONTINUE #################### # Inefficiency of 'namespace::fun()': require(microbenchmark) res.a <- microbenchmark(eval(parse(text="a <- 5"))) res.b <- microbenchmark(eval(base::parse(text="a <- 5"))) median(res.a$time)/median(res.b$time) # Can be made up by explicit assignment: foo <- base::parse res.a <- microbenchmark(eval(parse(text="a &lt...

The large value for maximum time may be due to garbage collection, which happens periodically. E.g., try the following, where the unlist(as.list()) creates a lot of garbage. I get a very large time every 102 or 51 iterations and a moderately large time more often mb <- microbenchmark::microbenchmark({ x <- as.list(sin(1:5e5)); x <- unlist(x) / cos(1:5e5) ; sum(x) }, times=1000) plot(mb$time) quantile(mb$time * 1e-6, c(0, .5, .75, .90, .95, .99, 1)) # 0% 50% 75% 90% 95% 99% 100% # 59.04446 82.15453 102.17522 180.36986 187.52667 233...

Note that if you force a garbage collection each iteration the times are more stable. However, on the average it is faster to let the garbage collector decide when to leap into action. mb_gc <- microbenchmark::microbenchmark(gc(), { x <- as.list(sin(1:5e5)); x <- unlist(x) / cos(1:5e5) ; sum(x) }, times=1000, control=list(order="inorder")) with(mb_gc, plot(time[expr!="gc()"])) with(mb_gc, quantile(1e-6*time[expr!="gc()"], c(0, .5, .75, .9, .95, .99, 1))) # 0%...

...wrote: > > You don't appear to have addressed my suggestion to not require a perfect > > external world, instead to measure the overhead of an imperfect world (by > > using an empty benchmark) and subtracting that from the measured > benchmark > > score. > > In microbenchmarks, performance is not additive. You can't compose > two pieces of code and predict that the benchmark results will be the > sum of the individual measurements. > This sounds like an argument against microbenchmarks in general, rather than against James's point. James' point as...

...if (n <= 1) return(acc) else { rlang::env_bind(.tailr_env, n = n - 1, acc = acc * n) next } } } This reduces the number of additional variables I need to one, but is a couple of orders of magnitude slower than the first version. > microbenchmark::microbenchmark(factorial(100), + factorial_tr_1(100), + factorial_tr_2(100)) Unit: microseconds expr min lq mean median uq max neval factorial(100) 53.978 60.543 77.76203...

...uce this across machines. The example below generates one or two slow runs out of 10000 on my Windows machine. I haven't been able to generate slow runs on my Linux machine, though I've had problems with slow running examples submitted to CRAN that I suspect may be caused by this. library(microbenchmark) (timings <- microbenchmark( normalizePath("\\\\some/network/drive", mustWork = FALSE), times = 1e4, unit = "s" )) boxplot(timings) Please can a few people run this code and see if they can reproduce the issue. It isn't clear to me whether this is a bug in R or a...

I decided to do a direct comparison of transpose and sweep. library(microbenchmark) NN <- matrix(c(1, 2, 3, 4, 5, 6), nrow = 2, byrow = TRUE) # Example matrix lambda <- c(2, 3, 4) # Example vector colNN <- t(NN) microbenchmark( sweep = sweep(NN, 2, lambda, "/"), transpose = t(t(NN)/lambda), colNN = colNN/lambda ) Unit: nanoseconds expr...

...> > perfect > >> > external world, instead to measure the overhead of an imperfect world > >> > (by > >> > using an empty benchmark) and subtracting that from the measured > >> > benchmark > >> > score. > >> > >> In microbenchmarks, performance is not additive. You can't compose > >> two pieces of code and predict that the benchmark results will be the > >> sum of the individual measurements. > > > > > > This sounds like an argument against microbenchmarks in general, rather > than...

...strated a simple function. > >> Dirk > > Indeed impressive, ... and it also works with integer vectors > something also not 100% trivial when working with compiled code. > > When testing that, I've went a step further: > > ##---- now "test": > require(microbenchmark) > i <- 1:10 Note that the relative speed of the algorithms also depends on the size of the input vector. i + i becomes the winner for longer vectors (e.g. i <- 1:1e6), but a proper Rcpp version is still approximately twice as fast. Rcpp::cppFunction("NumericVector doubleThisNum(N...

...indeed. And you, Winston, are right that this new code snippet would be an order of magnitude faster : ##----------------------------------------------------------------------------- f1 <- function(pkg) pkg %in% loadedNamespaces() f2 <- function(pkg) !is.null(.getNamespace(pkg)) require(microbenchmark) pkg <- "foo"; (mbM <- microbenchmark(r1 <- f1(pkg), r2 <- f2(pkg))); stopifnot(identical(r1,r2)); r1 ## Unit: microseconds ## expr min lq mean median uq max neval cld ## r1 <- f1(pkg) 38.516 40.9790 42.35037 41.7245 42.4060 82.922 100 b...

....function(list(...)). Example: make_fn_1 <- function(a, b) eval(call("function", a, b), env = parent.frame()) make_fn_2 <- function(a, b) as.function(c(a, list(b)), env = parent.frame()) a <- as.pairlist(alist(x = , y = )) b <- quote(x + y) library("microbenchmark") microbenchmark(make_fn_1(a, b), make_fn_2(a, b)) # Unit: microseconds # expr min lq mean median uq max neval cld # make_fn_1(a, b) 1.671 1.8855 2.13297 2.039 2.1950 9.852 100 a # make_fn_2(a, b) 3.541 3.7230 4.13400 3.906 4.1055 23.153...

>>>>> Henrik Bengtsson <henrik.bengtsson at gmail.com> >>>>> on Tue, 13 Mar 2018 10:12:55 -0700 writes: > FYI, in R devel (to become 3.5.0), there's isFALSE() which will cut > some corners compared to identical(): > > microbenchmark::microbenchmark(identical(FALSE, FALSE), isFALSE(FALSE)) > Unit: nanoseconds > expr min lq mean median uq max neval > identical(FALSE, FALSE) 984 1138 1694.13 1218.0 1337.5 13584 100 > isFALSE(FALSE) 713 761 1133.53 809.5 871.5 18619 100...

...{ ? ? ? ? ? ? ? ? escape(acc) ? ? ? ? ? ? } else { ? ? ? ? ? ? ? ? .tailr_env$.tailr_n <- n - 1 ? ? ? ? ? ? ? ? .tailr_env$.tailr_acc <- n * acc ? ? ? ? ? ? ? ? .tailr_env$n <- .tailr_env$.tailr_n ? ? ? ? ? ? ? ? .tailr_env$acc <- .tailr_env$.tailr_acc ? ? ? ? ? ? } ? ? ? ? } ? ? }) } microbenchmark::microbenchmark(factorial(1000), ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?factorial_tr_manual(1000), ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?factorial_tr_automatic_1(1000), ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?factorial_tr_automatic_2(1000)) Unit: microseconds ? ? ? ? ? ? ? ? ? ? ? ? ? ?expr ? ? min ? ? ?lq ? ? ?mean ? med...

...he local variables explicitly through the environment, which is what my package currently produces. The difference between supporting non-local returns and not is tiny, but explicitly accessing variables through their environment costs me about a factor of five ? something that surprised me. > microbenchmark::microbenchmark(factorial(1000), + ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?factorial_tr_manual(1000), + ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?factorial_tr_automatic_1(1000), + ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?factorial_tr_automatic_2(1000)) Unit: microseconds ? ? ? ? ? ? ? ? ? ? ? ? ? ?expr ? ? min ? ? ? lq ? ? mean...

Dear all, I am trying to benchmark code that occasionally prints on the screen and I want to suppress the printing. Is there an idiom for this? If I do sink(tempfile) microbenchmark(...) sink() then I'll be also measuring the costs of writing to tempfile. I could also sink to /dev/null, which is probably fast, but that is not portable. Is there a better solution? Is writing to a textConnection() better? Thanks, Best, Gabor