Andrew Hunter, Chris Kennelly
Notes on the name1: the french word for “reckless” or “rash” :), and also the name of several large and powerful English warships. So: giant and powerful, but maybe a little dangerous. :)
This is a description of the design of the Hugepage-Aware Allocator.
What do we want out of this redesign?
MADV_DONTNEEDmemory back to the OS, due to internal fragmentation. We can recover a useful fraction of this. In optimal cases, we see savings of over 90%. We do not expect to achieve this generally, but a variety of synthetic loads suggest 50% of pageheap is a reasonable target savings.
ReleaseMemoryToSystemis made without any thought to transparent hugepages, and in practice prevent most fleet RAM from remaining as intact hugepages. Services have seen substantial performance gains from from disabling release (and going to various other lengths to maximize hugepage usage).
reasonable allocation speed. This is really stating a non-goal: speed
PageHeap::New. PageHeap is a relatively light consumer of
cycles. We are willing to accept a speed hit in actual page allocation in
exchange for better hugepage usage and space overhead. This is not free but
we think is well justified. Our goal is more to avoid catastrophic
regressions in speed. We intentionally accept two particular time hits:
The algorithm – as usual here, really, the data structures, which neatly determine our algorithm – are nicely divided into components. Essentially, the path of an allocation goes like this:
Deallocation simply determines which of 1), 2), or 3) happened, and marks the corresponding object we allocated from as free.
We will sketch the purpose and approach of each important part. Note that we
have fairly detailed unit tests for each of these; one consequence on the
implementations is that most components are templated on the
tcmalloc::SystemRelease functions[^2] as we make a strong attempt to be zero
initializable where possible (sadly not everywhere).
Bitmap, its underlying implementation, are helper class
used throughout the components below. They are both quite simple:
Bitmap is a
fixed-size (templated) bitmap with fast operations to set and clear bits and
ranges of bits, with extensive support for searching and iterating. (Search and
iteration support is why
std::bitset is not usable here.)
RangeTracker is essentially a
Bitmap augmented with statistics on usage, in
particular the longest range of contiguous free (false) bits. It provides
methods to do best-fit allocation from free ranges (keeping the statistics
Both of these need to be quite fast as they’re on nearly every
allocation/deallocation path in
HugePageAwareAllocator (in multiple ways)!
They are reasonably optimized but probably still have more headroom.
This is a set of classes that fulfills requests for backed (or unbacked) aligned hugepage ranges. We use this for sufficiently large (or nicely sized) requests, and to provide memory for the other components to break up into smaller chunks.
HugeAllocator is (nearly) trivial: it requests arbitrarily large
hugepage-sized chunks from
SysAllocator, keeps them unbacked, and tracks the
available (unbacked) regions. Note that we do not need to be perfectly space
efficient here: we only pay virtual memory and metadata, since none of the
contents are backed. (We do make our best efforts to be relatively frugal,
however, since there’s no need to inflate VSS by large factors.) Nor do we have
to be particularly fast; this is well off any hot path, and we’re going to incur
non-trivial backing costs as soon as we’re done assigning a range.
The one tricky bit here is that we have to write some fiddly data structures by
hand. We would have liked to implement this by grabbing large (gigabyte+) ranges
from SysAllocator and using bitmaps or the like within them; however, too many
tests have brittle reliance on details of
SysAllocator that break if TCMalloc
consistently requests (any considerable amount) more than the minimum needed to
back current usage. So instead we need to track relatively small ranges. We’ve
implemented a balanced tree that merges adjacent ranges; it is, as we said,
fiddly, but reasonably efficient and not stunningly complicated.
This is a very simple wrapper on top of HugeAllocator. It’s only purpose is to store some number of backed single hugepage ranges as a hot cache (in case we rapidly allocate and deallocate a 2 MiB chunk).
It is not clear whether the cache is necessary, but we have it and it’s not costing us much in complexity, and will help significantly in some potential antagonistic scenarios, so we favor keeping it.
It currently attempts to estimate the optimal cache size based on past behavior. This may not really be needed, but it’s a very minor feature to keep or drop.
HugePageFiller takes small requests (less than a hugepage) and attempts to
pack them efficiently into hugepages. The vast majority of binaries use almost
entirely small allocations[^3], so this is the dominant consumer of space and
the most important component.
Our goal here is to make our live allocations fit within the smallest set of hugepages possible, so that we can afford to keep all used hugepages fully backed (and aggressively free empty ones).
The key challenge is avoiding fragmentation of free space within a hugepage: requests for 1 page are (usually) the most common, but 4, 8, or even 50+ page requests aren’t unheard of. Many 1-page free regions won’t be useful here, and we’ll have to request enormous numbers of new hugepages for anything large.
Our solution is to build a heap-ordered data structure on fragmentation, not total amount free, in each hugepage. We use the longest free range (the biggest allocation a hugepage can fulfill!) as a measurement of fragmentation. In other words: if a hugepage has a free range of length 8, we never allocate from it for a smaller request (unless all hugepages available have equally long ranges). This carefully husbands long ranges for the requests that need them, and allows them to grow (as neighboring allocations are freed).
Inside each equal-longest-free-range group, we order our heap by the number of allocations (chunked logarithmically). This helps favor allocating from fuller hugepages (of equal fragmented status). Number of allocations handily outperforms the total number of allocated pages here; our hypothesis is that since allocations of any size are equally likely[^4] to become free at any given time, and we need all allocations on a hugepage to become free to make the hugepage empty, we’re better off hoping for 1 10-page allocation to become free (with some probability P) than 5 1-page allocations (with probability P^5).
HugePageFiller contains support for releasing parts of mostly-empty
hugepages as a last resort.
The actual implementation uses a fixed set of lists and a bitmap for acceleration.
HugeRegion(big but not enormous…)
HugeAllocator covers very large requests and
HugePageFiller tiny ones; what
about the middle? In particular, requests that cannot fit into a hugepage, but
should not be rounded to multiples? (For instance, 2.1 MiB.) These are woefully
In any case, we certainly have to do something with “2.1 MiB”-type allocations, and rounding them to 4 will produce unacceptable slack (see below for what we can do with the filler here; it is wildly insufficient in current binaries which have the majority of their allocation in these large chunks.)
The solution is a much larger “region” that best-fits these chunks into a large range of hugepages (i.e. allows them to cross a hugepage boundary). We keep a set of these regions, and allocate from the most fragmented one (much as with Filler above)! The main difference is that these regions are kept un-backed by default (whereas the Filler deals almost entirely with backed hugepages). We back hugepages on demand when they are used by a request hitting the region (and aggressively _unback _them when they become empty again).
A few important details:
These regions are currently 1 GiB, which is very large!
The reason is this: suppose our entire binary allocates a huge number
requests of size
S that are too big for the filler, but that don’t evenly
divide the region size
M (say, 2.1 MiB :)) How much space will we waste?
Answer: we will allocate about
R = N / (M / S) regions, with each region
floor(M/S) allocations. The tail will be unused. We can unback any
totally untouched hugepages, but suppose that
M/S allocations just barely
touches the last hugepage in the region: we will then waste ~a full hugepage
per region, and thus waste
R hugepages. Conclusion: the larger a region we
use, the less waste (in this case). Originally regions were 32 MiB, and this
effect was very noticable. This also allows us to use very few regions in a
given binary, which means we can be less careful about how we organize the
set of regions.
We don’t make any attempt, when allocating from a given region, to find an already-backed but unused range. Nor do we prefer regions that have such ranges.
This is basically a question of effort. We’d like to do this, but we don’t
see any way to do it without making the data structure more complicated and
cumbersome. So far in tests it hasn’t proved a major problem. (Note that
RangeTracker has a low-address bias, which will help somewhat here by
compacting allocations towards the low end of any region).
Additional details on the design goals/tradeoffs are in the Regions Are Not Optional design doc.
HugePageAwareAllocator(putting it all together…)
This class houses the above components and routes between them, in addition to interfacing with the rest of TCMalloc (the above classes don’t need or use Spans, for instance). This is mostly straightforward; two points are worth discussing.
How do we choose which sub-allocator for a given request?
We use a size-based policy.
The changeover point between 1) and 2) is just a tuning decision (any choice would produce a usable binary). Half a hugepage was picked arbitrarily; this seems to work well.
HugeRegion (some of the time) need to be
backed; so do hugepages that grow the
HugePageFiller. This isn’t free. Page
heap allocation isn’t hugely expensive in practice, but it is under a lock and
contention matters. We currently rely on access by the application to back
memory, and assume returned memory has been backed.
For accounting purposes, we do a bit of tracking whether a given allocation is being fulfilled from previously-unbacked memory.
We do wire that information to the point we drop the pageheap lock; we then back it without producing lock contention. This made a noticable performance difference when explicitly backing memory before returning it to the application.
Also the name of this cutie, the real reason for the choice. [^2]: It will be possible, given recent improvements in constexpr usage, to eliminate this in followups. [^3]: Here we mean “requests to the pageheap as filtered through sampling, the central cache, etc” [^4]: Well, no, this is false in our empirical data, but to first order. ↩