Thanks for the background and suggestions. Valerie
On 07/02/2013 08:41 AM, John Chambers wrote:
It's hard to see how repeated dispatch on the same classes can be that slow, _if_ the function being called each time is itself doing some substantial work. The first call (in a session) with a particular signature searches for inherited methods and stores the method found in a table. The following calls with that signature should do a single lookup in a hash table. Caching the last signature is unlikely to be dramatically faster, but we can experiment and see. What is substantially different is calling a generic function vs calling a primitive or internal. If the local paste you constructed is the default, base::paste, that is a .Internal. Not going through the R generic function several thousand times would make a difference. It's a fundamental point about R that function calls do enough work that they add significant time to a "trivial" computation, such as a primitive call. There are various efforts going on these days to provide more efficient alternatives. They're all helpful; my personal favorite when the game is worth it is to consider doing key computations in a seriously faster language, like C++ via Rcpp. John On 7/1/13 10:04 PM, Valerie Obenchain wrote:Hi, S4 method dispatch can be very slow. Would it be reasonable to cache the most recent dispatch, anticipating the next invocation will be on the same type? This would be 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) ## > microbenchmark(fun0(lst), fun1(lst), times=10) ## Unit: milliseconds ## expr min lq median uq max neval ## fun0(lst) 21.093180 21.27616 21.453174 21.833686 24.758791 10 ## fun1(lst) 4.517808 4.53067 4.582641 4.682235 5.121856 10 Dispatch seems to be especially slow when packages are involved, e.g., with the Bioconductor IRanges package (http://bioconductor.org/packages/release/bioc/html/IRanges.html) removeGeneric("paste") library(IRanges) showMethods(paste) ## Function: paste (package BiocGenerics) ## ...="ANY" ## ...="Rle" selectMethod(paste, "ANY") ## Method Definition (Class "derivedDefaultMethod"): ## ## function (..., sep = " ", collapse = NULL) ## .Internal(paste(list(...), sep, collapse)) ## <environment: namespace:base> ## ## Signatures: ## ... ## target "ANY" ## defined "ANY" microbenchmark(fun0(lst), fun1(lst), times=10) ## Unit: milliseconds ## expr min lq median uq max neval ## fun0(lst) 233.539585 234.592491 236.311209 237.268506 243.181123 10 ## fun1(lst) 4.564914 4.592996 4.642898 4.729009 5.492706 10 sessionInfo() ## R version 3.0.0 Patched (2013-04-04 r62492) ## Platform: x86_64-unknown-linux-gnu (64-bit) ## ## 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=C LC_NAME=C ## [9] LC_ADDRESS=C LC_TELEPHONE=C ## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C ## ## attached base packages: ## [1] parallel stats graphics grDevices utils datasets methods ## [8] base ## ## other attached packages: ## [1] IRanges_1.19.15 BiocGenerics_0.7.2 microbenchmark_1.3-0 ## ## loaded via a namespace (and not attached): ## [1] stats4_3.0.0 Thanks, Valerie ______________________________________________ R-devel@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-devel
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