Re: [Rd] Recycling memory with a small free list
On Wed, 18 Feb 2015, Nathan Kurz wrote:
On Wed, Feb 18, 2015 at 7:19 AM, Radford Neal wrote:
... with assignments inside of loops like this:
reweight = function(iter, w, Q) {
for (i in 1:iter) {
wT = w * Q
}
}
... before the RHS is executed, the LHS allocation would be added
to a small fixed length list of available space which is checked
before future allocations. If the same size is requested before the
next garbage collection, the allocation is short-circuited and the
allocation is reused. This list could be very small, possibly even
only a single entry. Entries would only be put on the list if they
have no other references.
Here's an article about the benefits of this approach in Go that might
explain better than I was able:
https://blog.cloudflare.com/recycling-memory-buffers-in-go/
Their charts explain the goal very clearly: stabilize at a smaller
amount of memory to reduce churn, which improves performance in a
myriad of ways.
Thanks -- will have a look.
Reusing the LHS storage immediately isn't possible in general, because
evaluation of the RHS might produce an error, in which case the LHS
variable is supposed to be unchanged.
What's the guarantee R actually makes? What's an example of the use
case where this behaviour would be required? More generally, can one
not assume "a = NULL; a = func()" is equivalent to "a = func()" unless
func() references 'a' or has it as an argument? Or is the difficulty
that there is no way to know in advance it if will be referenced?
Detecting special cases where
there is guaranteed to be no error, or at least no error after the
first modification to newly allocated memory, might be too
complicated.
Yes, if required, the complexity of guaranteeing this might well rule
out the approach I suggested.
Putting the LHS storage on a small free list for later reuse (only
after the old value of the variable will definitely be replaced) seems
more promising (then one would need only two copies for examples such
as above, with them being used in alternate iterations).
OK, let's consider that potentially easier option instead: do nothing
immediately, but add a small queue for recycling from which the
temporary might be drawn. It has slightly worse cache behavior, but
should handle most of the issues with memory churn.
However,
there's a danger of getting carried away and essentially rewriting
malloc. To avoid this, one might try just calling "free" on the
no-longer-needed object, letting "malloc" then figure out when it can
be re-used.
Yes, I think that's what I was anticipating: add a free() equivalent
that does nothing if the object has multiple references/names, but
adds the object to small fixed size "free list" if it does not.
Perhaps this is only for certain types or for objects above a certain
size.
When requesting memory, allocvector() or perhaps R_alloc() does a
quick check of that "free list" to see if it has anything of the exact
requested size. If it does, it short circuits and recycles it. If it
doesn't, normal allocation takes place.
The "free list" is stored as two small fixed size arrays containing
size/address pairs. Searching is done linearly using code that
optimizes to SIMD comparisons. For 4/8/16 slots overhead of the
search should be unmeasurably fast.
The key to the approach would be keeping it simple, and realizing that
the goal is only to get the lowest hanging fruit: repeated
assignments of large arrays used in a loop. If it's complex, skip it
--- the behavior will be no worse than current.
By the way, what's happening with Luke's refcnt patches? From the
outside, they seem like a great improvement.
http://homepage.stat.uiowa.edu/~luke/talks/dsc2014.pdf
http://developer.r-project.org/Refcnt.html
Are they slated to become the standard approach? Are they going to be dropped?
Will both approaches be kept in parallel?
The approach can be enabled in R-devel by defining a preprocessor
variable. It's about 90% of where it needs to be to become the
default. I had to put work on hold for a while but will be getting
back to it soon. It's too late to turn on for 3.2.0 due in April, but
I'm hopeful of switching to reference counting in R-devel by August or
so.
Unfortunately, that seems not to be safe, because it's
possible that there is a reference to the no-longer-needed object on
the PROTECT stack, even though no one should actually be looking at
it any more.
Can you explain this case? I don't think I understand it.
In the current version of pqR (see pqR-project.org), modifications are
(often) done in place for statements such as w = w * Q, but not
curretly when the LHS variable does not appear on the RHS.
Yes, I looked at it earlier, and was excited to see that Luke had
ported half of your approach to standard R:
https://github.com/wch/r-source/blob/trunk/src/main/arithmetic.h#L65
But only the RHS temporary variables optimizations made it over. Your
LHS "w = w * Q" optimizations did not,
Re: [Rd] Recycling memory with a small free list
If you link to tcmalloc instead of the default malloc on your system, the
performance of large allocations should improve. On unix machines you
don't even need to recompile -- you can do this with LD_PRELOAD. The
downside is that you'll almost certainly end up with higher average memory
usage.as tcmalloc never returns memory to the OS.
It would also be worth checking what jemalloc does with large allocations.
It may well be worth tweaking the way that large allocations are handled in
R -- most allocation libraries assume that large allocations are infrequent
and that you won't be frequently requesting the same sized memory block.
Those assumptions don't hold in R. On the other hand, I don't see much
benefit to R having it's own logic for handling small allocations, as most
malloc implementations handle those extremely efficiently.
Karl
On Thu, Feb 19, 2015 at 10:15 AM, wrote:
> On Wed, 18 Feb 2015, Nathan Kurz wrote:
>
> On Wed, Feb 18, 2015 at 7:19 AM, Radford Neal
>> wrote:
>>
>>> ... with assignments inside of loops like this:
reweight = function(iter, w, Q) {
for (i in 1:iter) {
wT = w * Q
}
}
... before the RHS is executed, the LHS allocation would be added
to a small fixed length list of available space which is checked
before future allocations. If the same size is requested before the
next garbage collection, the allocation is short-circuited and the
allocation is reused. This list could be very small, possibly even
only a single entry. Entries would only be put on the list if they
have no other references.
>>>
>> Here's an article about the benefits of this approach in Go that might
>> explain better than I was able:
>> https://blog.cloudflare.com/recycling-memory-buffers-in-go/
>> Their charts explain the goal very clearly: stabilize at a smaller
>> amount of memory to reduce churn, which improves performance in a
>> myriad of ways.
>>
>
> Thanks -- will have a look.
>
>
> Reusing the LHS storage immediately isn't possible in general, because
>>> evaluation of the RHS might produce an error, in which case the LHS
>>> variable is supposed to be unchanged.
>>>
>>
>> What's the guarantee R actually makes? What's an example of the use
>> case where this behaviour would be required? More generally, can one
>> not assume "a = NULL; a = func()" is equivalent to "a = func()" unless
>> func() references 'a' or has it as an argument? Or is the difficulty
>> that there is no way to know in advance it if will be referenced?
>>
>> Detecting special cases where
>>> there is guaranteed to be no error, or at least no error after the
>>> first modification to newly allocated memory, might be too
>>> complicated.
>>>
>>
>> Yes, if required, the complexity of guaranteeing this might well rule
>> out the approach I suggested.
>>
>> Putting the LHS storage on a small free list for later reuse (only
>>> after the old value of the variable will definitely be replaced) seems
>>> more promising (then one would need only two copies for examples such
>>> as above, with them being used in alternate iterations).
>>>
>>
>> OK, let's consider that potentially easier option instead: do nothing
>> immediately, but add a small queue for recycling from which the
>> temporary might be drawn. It has slightly worse cache behavior, but
>> should handle most of the issues with memory churn.
>>
>> However,
>>> there's a danger of getting carried away and essentially rewriting
>>> malloc. To avoid this, one might try just calling "free" on the
>>> no-longer-needed object, letting "malloc" then figure out when it can
>>> be re-used.
>>>
>>
>> Yes, I think that's what I was anticipating: add a free() equivalent
>> that does nothing if the object has multiple references/names, but
>> adds the object to small fixed size "free list" if it does not.
>> Perhaps this is only for certain types or for objects above a certain
>> size.
>>
>> When requesting memory, allocvector() or perhaps R_alloc() does a
>> quick check of that "free list" to see if it has anything of the exact
>> requested size. If it does, it short circuits and recycles it. If it
>> doesn't, normal allocation takes place.
>>
>> The "free list" is stored as two small fixed size arrays containing
>> size/address pairs. Searching is done linearly using code that
>> optimizes to SIMD comparisons. For 4/8/16 slots overhead of the
>> search should be unmeasurably fast.
>>
>> The key to the approach would be keeping it simple, and realizing that
>> the goal is only to get the lowest hanging fruit: repeated
>> assignments of large arrays used in a loop. If it's complex, skip it
>> --- the behavior will be no worse than current.
>>
>> By the way, what's happening with Luke's refcnt patches? From the
>> outside, they seem like a great improvement.
>> http://homepage.stat.uiowa.edu/~luke/talks/dsc2014.pdf
>> http://developer.r-project.org/Refcnt.html
>> Are they slated to beco
[Rd] save.image Doesn't Save Objects When Browsing
The documentation states that "save.image() is just a short-cut for save(list =
ls(all = TRUE), file = ".RData")". However, if I do
Browse[1]> ls(all=TRUE)
[1] "expression" "orderedFeatures""predictParams"
[4] "resubstituteParams" "trainParams""verbose"
Browse[1]> save.image("BROWSE.RData")
load("BROWSE.RData") shows different variables than ls() did. Explicitly typing
Browse[1]> save(list = ls(all = TRUE), file = ".RData")
causes the variables in the current environment to be saved.
Is the documentation of save.image() missing a special case ?
--
Dario Strbenac
PhD Student
University of Sydney
Camperdown NSW 2050
Australia
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R-devel@r-project.org mailing list
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Re: [Rd] save.image Doesn't Save Objects When Browsing
On 20/02/2015 01:00, Dario Strbenac wrote:
The documentation states that "save.image() is just a short-cut for save(list = ls(all =
TRUE), file = ".RData")". However, if I do
Browse[1]> ls(all=TRUE)
[1] "expression" "orderedFeatures""predictParams"
[4] "resubstituteParams" "trainParams""verbose"
Browse[1]> save.image("BROWSE.RData")
load("BROWSE.RData") shows different variables than ls() did. Explicitly typing
Browse[1]> save(list = ls(all = TRUE), file = ".RData")
causes the variables in the current environment to be saved.
Is the documentation of save.image() missing a special case?
You are missing the other arguments. What it actually says is
‘save.image()’ is just a short-cut for ‘save my current
workspace’,
and for that you need to specify envir=.GlobalEnv .
--
Dario Strbenac
PhD Student
University of Sydney
Camperdown NSW 2050
Australia
__
R-devel@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-devel
--
Brian D. Ripley, rip...@stats.ox.ac.uk
Emeritus Professor of Applied Statistics, University of Oxford
1 South Parks Road, Oxford OX1 3TG, UK
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