Thank you for a very thorough analysis. It seems whether or not an operation makes a full copy really depends on the specific operation, and that it is not safe to assume that because I know something is unchanged there will be no copy. For example, in your last case only one element of a list was modified, but all the list elements got new memory.
BTW, one reason I got into this, aside from wanting to save memory, is that I found my code was spending a lot of time in areas that probably involved getting new memory. So it mattered for speed too. Ross On Mon, 2014-01-27 at 06:33 -0800, Martin Morgan wrote: > Hi Ross -- > > On 01/23/2014 05:53 PM, Ross Boylan wrote: > > [Apologies if a duplicate; we are having mail problems.] > > > > I am trying to understand the circumstances under which R makes a copy > > of an object, as opposed to simply referring to it. I'm talking about > > what goes on under the hood, not the user semantics. I'm doing things > > that take a lot of memory, and am trying to minimize my use. > > > > I thought that R was clever so that copies were created lazily. For > > example, if a is matrix, then > > b <- a > > b & a referred to to the same object underneath, so that a complete > > duplicate (deep copy) wasn't made until it was necessary, e.g., > > b[3, 1] <- 4 > > would duplicate the contents of a to b, and then overwrite them. > > Compiling your R with --enable-memory-profiling gives access to the > tracemem() > function, showing that your understanding above is correct > > > b = matrix(0, 3, 2) > > tracemem(b) > [1] "<0x7054020>" > > a = b ## no copy > > b[3, 1] = 2 ## copy > tracemem[0x7054020 -> 0x7053fc8]: > > b = matrix(0, 3, 2) > > tracemem(b) > > tracemem(b) > [1] "<0x680e258>" > > b[3, 1] = 2 ## no copy > > > > The same is apparent using .Internal(inspect()), where the first information > @7053ec0 is the address of the data. The other relevant part is the 'NAM()' > field, which indicates whether there are 0, 1 or (have been) at least 2 > symbols > referring to the data. NAM() increments from 1 (no duplication on modify > required) on original creation to 2 when a = b (duplicate on modify) > > > b = matrix(0, 3, 2) > > .Internal(inspect(b)) > @7053ec0 14 REALSXP g0c4 [NAM(1),ATT] (len=6, tl=0) 0,0,0,0,0,... > ATTRIB: > @7057528 02 LISTSXP g0c0 [] > TAG: @21c5fb8 01 SYMSXP g0c0 [LCK,gp=0x4000] "dim" (has value) > @7056858 13 INTSXP g0c1 [NAM(2)] (len=2, tl=0) 3,2 > > b[3, 1] = 2 > > .Internal(inspect(b)) > @7053ec0 14 REALSXP g0c4 [NAM(1),ATT] (len=6, tl=0) 0,0,2,0,0,... > ATTRIB: > @7057528 02 LISTSXP g0c0 [] > TAG: @21c5fb8 01 SYMSXP g0c0 [LCK,gp=0x4000] "dim" (has value) > @7056858 13 INTSXP g0c1 [NAM(2)] (len=2, tl=0) 3,2 > > a = b > > .Internal(inspect(b)) ## data address unchanced > @7053ec0 14 REALSXP g0c4 [NAM(2),ATT] (len=6, tl=0) 0,0,0,0,0,... > ATTRIB: > @7057528 02 LISTSXP g0c0 [] > TAG: @21c5fb8 01 SYMSXP g0c0 [LCK,gp=0x4000] "dim" (has value) > @7056858 13 INTSXP g0c1 [NAM(2)] (len=2, tl=0) 3,2 > > b[3, 1] = 2 > > .Internal(inspect(b)) ## data address changed > @7232910 14 REALSXP g0c4 [NAM(1),ATT] (len=6, tl=0) 0,0,2,0,0,... > ATTRIB: > @7239d28 02 LISTSXP g0c0 [] > TAG: @21c5fb8 01 SYMSXP g0c0 [LCK,gp=0x4000] "dim" (has value) > @7237b48 13 INTSXP g0c1 [NAM(2)] (len=2, tl=0) 3,2 > > > > > > The following log, from R 3.0.1, does not seem to act that way; I get > > the same amount of memory used whether I copy the same object repeatedly > > or create new objects of the same size. > > > > Can anyone explain what is going on? Am I just wrong that copies are > > initially shallow? Or perhaps that behavior only applies for function > > arguments? Or doesn't apply for class slots or reference class > > variables? > > > > > foo <- setRefClass("foo", fields=list(x="ANY")) > > > bar <- setClass("bar", slots=c("x")) > > using the approach above, we can see that creating an S4 or reference object > in > the way you've indicated (validity checks or other initialization might > change > this) does not copy the data although it is marked for duplication > > > x = 1:2; .Internal(inspect(x)) > @7553868 13 INTSXP g0c1 [NAM(1)] (len=2, tl=0) 1,2 > > .Internal(inspect(foo(x=x)$x)) > @7553868 13 INTSXP g0c1 [NAM(2)] (len=2, tl=0) 1,2 > > .Internal(inspect(bar(x=x)@x)) > @7553868 13 INTSXP g0c1 [NAM(2)] (len=2, tl=0) 1,2 > > On the other hand, lapply is creating copies > > > x = 1:2; .Internal(inspect(x)) > @757b5a8 13 INTSXP g0c1 [NAM(1)] (len=2, tl=0) 1,2 > > .Internal(inspect(lapply(1:2, function(i) x))) > @7551f88 19 VECSXP g0c2 [] (len=2, tl=0) > @757b428 13 INTSXP g0c1 [] (len=2, tl=0) 1,2 > @757b3f8 13 INTSXP g0c1 [] (len=2, tl=0) 1,2 > > One can construct a list without copies > > > x = 1:2; .Internal(inspect(x)) > @7677c18 13 INTSXP g0c1 [NAM(1)] (len=2, tl=0) 1,2 > > .Internal(inspect(list(x)[rep(1, 2)])) > @767b080 19 VECSXP g0c2 [NAM(2)] (len=2, tl=0) > @7677c18 13 INTSXP g0c1 [NAM(2)] (len=2, tl=0) 1,2 > @7677c18 13 INTSXP g0c1 [NAM(2)] (len=2, tl=0) 1,2 > > but that (creating a list of identical elements) doesn't seem to be a likely > real-world scenario and the gain is transient > > > x = 1:2; y = list(x)[rep(1, 4)] > > .Internal(inspect(y)) > @507bef8 19 VECSXP g0c3 [NAM(2)] (len=4, tl=0) > @514ff98 13 INTSXP g0c1 [NAM(2)] (len=2, tl=0) 1,2 > @514ff98 13 INTSXP g0c1 [NAM(2)] (len=2, tl=0) 1,2 > @514ff98 13 INTSXP g0c1 [NAM(2)] (len=2, tl=0) 1,2 > @514ff98 13 INTSXP g0c1 [NAM(2)] (len=2, tl=0) 1,2 > > y[[1]][1] = 2L ## everybody copied > > .Internal(inspect(y)) > @507bf40 19 VECSXP g0c3 [NAM(1)] (len=4, tl=0) > @51502c8 13 INTSXP g0c1 [] (len=2, tl=0) 2,2 > @51502f8 13 INTSXP g0c1 [] (len=2, tl=0) 1,2 > @5150328 13 INTSXP g0c1 [] (len=2, tl=0) 1,2 > @5150358 13 INTSXP g0c1 [] (len=2, tl=0) 1,2 > > > Probably it is more helpful to think of reducing the number of times an > object > is _modified_, e.g., representing data as vectors and doing vectorized > updates. > > Martin > > > > mycoef <- list(a=matrix(rnorm(200000), ncol=2000), > > b=array(rnorm(200000), > > dim=c(4, 5, 10000))) > > > gc() > > used (Mb) gc trigger (Mb) max used (Mb) > > Ncells 2650747 141.6 4170209 222.8 4170209 222.8 > > Vcells 799751724 6101.7 1711485496 13057.6 1711485493 13057.6 > > > a <- lapply(1:100, function(i) bar(x=mycoef)) # create 100 objects > > that > > contain copies > > > gc() > > used (Mb) gc trigger (Mb) max used (Mb) > > Ncells 2652156 141.7 4170209 222.8 4170209 222.8 > > Vcells 839752640 6406.9 1711485496 13057.6 1711485493 13057.6 > > # +305 Mb > > > b <- lapply(1:100, function(i) foo(x=mycoef)) # same with a reference > > class > > > gc() > > used (Mb) gc trigger (Mb) max used (Mb) > > Ncells 2654761 141.8 4170209 222.8 4170209 222.8 > > Vcells 879756752 6712.1 1711485496 13057.6 1711485493 13057.6 > > # also + 305 Mb > > > rm("a", "b") > > > gc() > > used (Mb) gc trigger (Mb) max used (Mb) > > Ncells 2650660 141.6 4170209 222.8 4170209 222.8 > > Vcells 799751664 6101.7 1711485496 13057.6 1711485493 13057.6 > > # write to "copy" to see if it uses more memory > > > a <- lapply(1:100, function(i) {r <- bar(x=mycoef); r@x$a[5, 10] <- 33; > > r} ) > > > gc() > > used (Mb) gc trigger (Mb) max used (Mb) > > Ncells 2652174 141.7 4170209 222.8 4170209 222.8 > > Vcells 839752684 6406.9 1711485496 13057.6 1711485493 13057.6 > > # also + 305 Mb > > > rm("a", "b") > > Warning message: > > In rm("a", "b") : object 'b' not found > > > gc() > > used (Mb) gc trigger (Mb) max used (Mb) > > Ncells 2650680 141.6 4170209 222.8 4170209 222.8 > > Vcells 799751684 6101.7 1711485496 13057.6 1711485493 13057.6 > > # now create completely distinct objects > > > a <- lapply(1:100, function(i) {acoef <- list(a=matrix(rnorm(200000), > > ncol=2000), b=array(rnorm(200000), dim=c(4, 5, 10000))) > > !+ bar(x=acoef)}) > > > gc() > > used (Mb) gc trigger (Mb) max used (Mb) > > Ncells 2652191 141.7 4170209 222.8 4170209 222.8 > > Vcells 839752699 6406.9 1711485496 13057.6 1711485493 13057.6 > > # + 305 Mb > > > > Thanks. > > Ross Boylan > > > > P.S. I also tried posting this from a google-managed email account, and > > have got > > back two messages like this: > > Mail Delivery Subsystem mailer-dae...@googlemail.com > > > > > > 5:22 PM (28 minutes ago) > > > > > > to me > > > > This is an automatically generated Delivery Status Notification > > > > THIS IS A WARNING MESSAGE ONLY. > > > > YOU DO NOT NEED TO RESEND YOUR MESSAGE. > > > > Delivery to the following recipient has been delayed: > > > > r-h...@r.project.org <mailto:r-h...@r.project.org> > > > > Message will be retried for 1 more day(s) > > > > Technical details of temporary failure: > > The recipient server did not accept our requests to connect. Learn more at > > http://support.google.com/mail/bin/answer.py?answer=7720 > > <http://support.google.com/mail/bin/answer.py?answer=7720> > > [(0) r.project.org <http://r.project.org> > > . [206.188.192.100]:25: Connection refused] > > > > > > ______________________________________________ > > R-help@r-project.org mailing list > > https://stat.ethz.ch/mailman/listinfo/r-help > > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > > and provide commented, minimal, self-contained, reproducible code. > > ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.