cache most-recent dispatch
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
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