[R] memory use of copies
Philippe GROSJEAN
Philippe.GROSJEAN at umons.ac.be
Wed Jan 29 10:29:53 CET 2014
For the last case with the list:
> x <- 1:2; y = list(x)[rep(1, 4)]
> .Internal(inspect(y))
@102bbe090 19 VECSXP g0c3 [MARK,NAM(2)] (len=4, tl=0)
@106119628 13 INTSXP g0c1 [MARK] (len=2, tl=0) 1,2
@106119628 13 INTSXP g0c1 [MARK] (len=2, tl=0) 1,2
@106119628 13 INTSXP g0c1 [MARK] (len=2, tl=0) 1,2
@106119628 13 INTSXP g0c1 [MARK] (len=2, tl=0) 1,2
> y[[1]][1] <- 2L # everybody copied
> .Internal(inspect(y))
@102fca698 19 VECSXP g0c3 [NAM(1)] (len=4, tl=0)
@1061196b8 13 INTSXP g0c1 [] (len=2, tl=0) 2,2
@106119688 13 INTSXP g0c1 [] (len=2, tl=0) 1,2
@106119658 13 INTSXP g0c1 [] (len=2, tl=0) 1,2
@106119718 13 INTSXP g0c1 [] (len=2, tl=0) 1,2
> y1 <- y[[1]]; y1[1] <- 3L; y[[1]] <- y1 # only one copied
> .Internal(inspect(y))
@102fca698 19 VECSXP g0c3 [MARK,NAM(1)] (len=4, tl=0)
@10610b7a8 13 INTSXP g0c1 [MARK] (len=2, tl=0) 3,2
@106119688 13 INTSXP g0c1 [MARK] (len=2, tl=0) 1,2
@106119658 13 INTSXP g0c1 [MARK] (len=2, tl=0) 1,2
@106119718 13 INTSXP g0c1 [MARK] (len=2, tl=0) 1,2
Assignment to "double subset" of a list seems to trigger full copy of the list, but `[[<-` alone appears smart enough to avoid copying the other elements of the list.
Best,
Philippe
..............................................<°}))><........
) ) ) ) )
( ( ( ( ( Prof. Philippe Grosjean
) ) ) ) )
( ( ( ( ( Numerical Ecology of Aquatic Systems
) ) ) ) ) Mons University, Belgium
( ( ( ( (
..............................................................
On 29 Jan 2014, at 00:53, Ross Boylan <ross at biostat.ucsf.edu> wrote:
> 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 at 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
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>>
>>
>
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