design/35112-scaling-the-page-allocator: add proposal
This change adds a new proposal to the repository describing an
improvement to the current page allocator in the runtime, with a focus
Reviewed-by: Austin Clements <email@example.com>
diff --git a/design/35112-scaling-the-page-allocator.md b/design/35112-scaling-the-page-allocator.md
new file mode 100644
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+# Proposal: Scaling the Go page allocator
+Author(s): Michael Knyszek, Austin Clements
+Last updated: 2019-10-18
+The Go runtime's page allocator (i.e. `(*mheap).alloc`) has scalability
+In applications with a high rate of heap allocation and a high GOMAXPROCS,
+small regressions in the allocator can quickly become big problems.
+Based on ideas from Austin about making P-specific land-grabs to reduce lock
+contention, and with evidence that most span allocations are one page in size
+and are for small objects (<=32 KiB in size), I propose we:
+ 1. Remove the concept of spans for free memory and track free memory with a
+ 1. Allow a P to cache free pages for uncontended allocation.
+Point (1) simplifies the allocator, reduces some constant overheads, and more
+importantly enables (2), which tackles lock contention directly.
+The Go runtime's page allocator (i.e. `(*mheap).alloc`) has serious scalability
+These were discovered when working through
+which were both filed during or after the Go 1.12 release.
+The common thread between each of these scenarios is a high rate of allocation
+and a high level of parallelism (in the Go world, a relatively high GOMAXPROCS
+value, such as 32).
+As it turned out, adding some extra work for a small subset of allocations in Go
+1.12 and removing a fast-path data structure in the page heap caused significant
+regressions in both throughput and tail latency.
+The fundamental issue is the heap lock: all operations in the page heap
+(`mheap`) are protected by the heap lock (`mheap.lock`).
+A high allocation rate combined with a high degree of parallelism leads to
+significant contention on this lock, even though page heap allocations are
+relatively cheap and infrequent.
+For instance, if the most popular allocation size is ~1 KiB, as seen with the
+Kubernetes scalability test, then the runtime accesses the page heap every 10th
+allocation or so.
+The proof that this is really a scalability issue in the design and not an
+implementation bug in Go 1.12 is that we were seeing barging behavior on this
+lock in Go 1.11, which indicates that the heap lock was already in a collapsed
+state before the regressions in Go 1.12 were even introduced.
+I believe we can significantly improve the scalability of the page allocator if
+we eliminate as much lock contention in the heap as possible. We can achieve
+this in two ways:
+ 1. Make the allocator faster.
+ The less time spent with the lock held the better the assumptions of e.g.
+ a futex hold up.
+ 1. Come up with a design that avoids grabbing the lock at all in the common
+So, what is this common case? We currently have span allocation data for a
+couple large Go applications which reveal that an incredibly high fraction of
+allocations are for small object spans.
+First, we have data from Kubernetes' 12-hour load test, which indicates that
+99.89% of all span allocations are for small object spans, with 93% being from
+the first 50 size classes, inclusive.
+Next, data from a large Google internal service shows that 95% of its span
+allocations are for small object spans, even though this application is known to
+make very large allocations relatively frequently.
+94% of all of this application's span allocations are from the first 50 size
+Thus, I propose we:
+ * Track free pages in a bitmap that spans the heap's address space.
+ * Allow a P to cache a set of bits from the bitmap.
+The goal is to have most (80%+) small object span allocations allocate quickly,
+and without a lock.
+(1) makes the allocator significantly more cache-friendly, predictable, and
+enables (2), which helps us avoid grabbing the lock in the common case and
+allows us to allocate a small number of pages very quickly.
+Note that this proposal maintains the current first-fit allocation policy and
+highest-address-first scavenging policy.
+### Tracking free memory with bitmaps
+With a first-fit policy, allocation of one page (the common case) amounts to
+finding the first free page in the heap. One promising idea here is to use a
+bitmap because modern microarchitectures are really good at iterating over bits.
+Each bit in the bitmap represents a single runtime page (8 KiB as of this
+writing), where 1 means in-use and 0 means free. "In-use" in the context of the
+new page allocator is now synonymous with "owned by a span".
+The concept of a free span isn't useful here.
+I propose that the bitmap be divided up into shards each representing a single
+Each shard would then live in its corresponding `heapArena` instance.
+For a 64 MiB arena and 8 KiB page size, this means 8192 bits (1 KiB) per arena.
+The main reason for sharding it in this way is for convenience in
+An alternative could be to maintain one large global bitmap and to map it in as
+Simply iterating over one large bitmap is still fairly inefficient, especially
+for dense heaps.
+We want to be able to quickly skip over completely in-use sections of the heap.
+Therefore, I first propose we subdivide our shards further into chunks.
+Next, instead of running over each chunk directly, I propose we attach summary
+information to each chunk such that it's much faster to filter out chunks which
+couldn't possibly satisfy the allocation.
+We subdivide the shards further rather than just attaching summary information
+to shards directly in order to keep bitmap iteration work consistent across
+platforms (i.e. independent of arena size).
+What should this summary information contain? I propose we augment each chunk
+with three fields: `start, max, end uintptr`.
+`start` represents the number of contiguous 0 bits at the start of this bitmap
+Similarly, `end` represents the number of contiguous 0 bits at the end of the
+Finally, `max` represents the largest contiguous section of 0 bits in the bitmap
+The diagram below illustrates an example summary for a bitmap chunk.
+The arrow indicates which direction addresses go (lower to higher).
+The bitmap contains 3 zero bits at its lowest edge and 7 zero bits at its
+Within the summary, there are 10 contiguous zero bits, which `max` reflects.
+![Diagram of a summary.](35112/summary-diagram.png)
+With these three fields, we can determine whether we'll be able to find a
+sufficiently large contiguous free section of memory in a given arena or
+contiguous set of arenas with a simple state machine.
+Computing this summary information for an arena is less trivial to make fast,
+and effectively amounts to a combination of a table to get per-byte summaries
+and a state machine to merge them until we have a summary which represents the
+The state machine for `start` and `end` is mostly trivial.
+`max` is only a little more complex: by knowing `start`, `max`, and `end` for
+adjacent summaries, we can merge the summaries by picking the maximum of each
+summary's `max` value and the sum of their `start` and `end` values.
+I propose we update these summary values eagerly as spans are allocated and
+For large allocations that span multiple arenas, we can zero out summary
+information very quickly, and we really only need to do the full computation of
+summary information for the ends of the allocation.
+There's a problem in this design so far wherein subsequent allocations may end
+up treading the same path over and over.
+Unfortunately, this retreading behavior's time complexity is `O(heap * allocs)`.
+We propose a simple solution to this problem: maintain a hint address.
+A hint address represents an address before which there are definitely no free
+pages in the heap.
+There may not be free pages for some distance after it, hence why it is just a
+hint, but we know for a fact we can prune from the search everything before that
+In the steady-state, as we allocate from the lower addresses in the heap, we can
+bump the hint forward with every search, effectively eliminating the search
+space until new memory is freed.
+Most allocations are expected to allocate not far from the hint.
+There's still an inherent scalability issue with this design: larger allocations
+may require iterating over the whole heap, even with the hint address.
+This scalability issue arises from the fact that we now have an allocation
+algorithm with a time complexity linear in the size of the heap.
+Modern microarchitectures are good, but not quite good enough to just go with
+Therefore, I propose we take this notion of a summary-per-chunk and extend it:
+we can build a tree around this, wherein a given entry at some level of the
+radix tree represents the merge of some number of summaries in the next level.
+The leaf level in this case contains the per-chunk summaries, while each entry
+in the previous levels may reflect 8 chunks, and so on.
+This tree would be constructed from a finite number of arrays of summaries, with
+lower layers being smaller in size than following layers, since each entry
+reflects a larger portion of the address space.
+More specifically, we avoid having an "explicit" pointer-based structure (think
+"implicit" vs. "explicit" when it comes to min-heap structures: the former tends
+to be an array, while the latter tends to be pointer-based).
+Below is a diagram of the complete proposed structure.
+![Diagram of the proposed radix tree.](35112/radix-tree-diagram.png)
+The bottom two boxes are the arenas and summaries representing the full address
+Each red line represents a summary, and each set of dotted lines from a summary
+into the next layer reflects which part of that next layer that summary refers
+In essence, because this tree reflects our address space, it is in fact a radix
+tree over addresses.
+By left shifting a memory address by different amounts, we can find the exact
+summary which contains that address in each level.
+On allocation, this tree may be searched by looking at `start`, `max`, and `end`
+at each level: if we see that `max` is large enough, we continue searching in
+the next, more granular, level.
+If `max` is too small, then we look to see if there's free space spanning two
+adjacent summaries' memory regions by looking at the first's `end` value and the
+second's `start` value.
+Larger allocations are therefore more likely to cross larger boundaries of the
+address space are more likely to get satisfied by levels in the tree which are
+closer to the root.
+Note that if the heap has been exhausted, then we will simply iterate over the
+root level, find all zeros, and return.
+#### Implementation details
+A number of details were omitted from the previous section for brevity, but
+these details are key for an efficient implementation.
+Firstly, note that `start, max, end uintptr` is an awkward structure in size,
+requiring either 12 bytes or 24 bytes to store naively, neither of which fits
+a small multiple of a cache line comfortably.
+To make this structure more cache-friendly, we can pack them tightly into
+64-bits if we constrain the height of the radix tree.
+The packing is straight-forward: we may dedicate 21 bits to each of these
+three numbers and pack them into 63 bits.
+A small quirk with this scheme is that each of `start`, `max`, and `end` are
+counts, and so we need to represent zero as well as the maximum value (`2^21`),
+which at first glance requires an extra bit per field.
+Luckily, in that case (i.e. when `max == 2^21`), then `start == max && max ==
+We may use the last remaining bit to represent this case. A summary representing
+a completely full region is also conveniently `uint64(0)` in this
+representation, which enables us to very quickly skip over parts of the address
+space we don't care about with just one load and branch.
+As mentioned before, a consequence of this packing is that we need to place a
+restriction on our structure: each entry in the root level of the radix tree may
+only represent at most `2^21` 8 KiB pages, or 16 GiB, because we cannot
+represent any more in a single summary.
+From this constraint, it follows that the root level will always be
+`2^(heapAddrBits - 21 - log2(pageSize))` in size in entries.
+Should we need to support much larger heaps, we may easily remedy this by
+representing a summary as `start, max, end uint32`, though at the cost of cache
+line alignment and 1.5x metadata overhead.
+We may also consider packing the three values into two `uint64` values, though
+at the cost of twice as much metadata overhead.
+Note that this concern is irrelevant on 32-bit architectures: we can easily
+represent the whole address space with a tree and 21 bits per summary field.
+Unfortunately, we cannot pack it more tightly on 32-bit architectures since at
+least 14 bits are required per summary field.
+Now that we've limited the size of the root level, we need to pick the sizes of
+the subsequent levels.
+Each entry in the root level must reflect some number of entries in the
+following level, which gives us our fanout.
+In order to stay cache-friendly, I propose trying to keep the fanout close to
+the size of an L1 cache line or some multiple thereof.
+64 bytes per line is generally a safe bet, and our summaries are 8 bytes wide,
+so that gives us a fanout of 8.
+Taking all this into account, for a 48-bit address space (such as how we treat
+`linux/amd64` in the runtime), I propose the following 5-level array structure:
+ * Level 0: `16384` entries (fanout = 1, root)
+ * Level 1: `16384*8` entries (fanout = 8)
+ * Level 2: `16384*8*8` entries (fanout = 8)
+ * Level 3: `16384*8*8*8` entries (fanout = 8)
+ * Level 4: `16384*8*8*8*8` entries (fanout = 8, leaves)
+Note that level 4 has `2^48 bytes / (512 * 8 KiB)` entries, which is exactly the
+number of chunks in a 48-bit address space.
+Each entry at this level represents a single chunk.
+Similarly, since a chunk represents 512, or 2^9 pages, each entry in the root
+level summarizes a region of `2^21` contiguous pages, as intended.
+This scheme can be trivially applied to any system with a larger address space,
+since we just increase the size of the root level.
+For a 64-bit address space, the root level can get up to 8 GiB in size, but
+that's mostly virtual address space which is fairly cheap since we'll only
+commit to what we use (see below).
+For most heaps, `2^21` contiguous pages or 16 GiB per entry in the root level is
+If we limited ourselves to 8 entries in the root, we would still be able to
+gracefully support up to 128 GiB (and likely double that, thanks to
+Some Go applications may have larger heaps though, but as mentioned before we
+can always change the structure of a summary away from packing into 64 bits and
+then add an additional level to the tree, at the expense of some additional
+Overall this uses between KiB and hundreds of MiB of address space on systems
+with smaller address spaces (~600 MiB for a 48-bit address space, ~12 KiB for a
+32-bit address space).
+For a full 64-bit address space, this layout requires ~37 TiB of reserved
+At first glance, this seems like an enormous amount, but in reality that's an
+extremely small fraction (~0.00022%) of the full address space. Furthermore,
+this address space is very cheap since we'll only commit what we use, and to
+reduce the size of core dumps and eliminate issues with overcommit we will map
+the space as `PROT_NONE` (only `MEM_RESERVE` on Windows) and map it as
+read/write explicitly when we grow the heap (an infrequent operation).
+There are only two known adverse effects of this large mapping on Linux:
+ 1. `ulimit -v`, which restricts even `PROT_NONE` mappings.
+ 1. Programs like top, when they report virtual memory footprint, include
+ `PROT_NONE` mappings.
+In the grand scheme of things, these are relatively minor consequences.
+The former is not used often, and in cases where it is, it's used as an
+inaccurate proxy for limiting a process's physical memory use.
+The latter is mostly cosmetic, though perhaps some monitoring system uses it as
+a proxy for memory use, and will likely result in some harmless questions.
+### Allow a P to cache bits from the bitmap
+I propose adding a free page cache to each P.
+The page cache, in essence, is a base address marking the beginning of a 64-page
+aligned chunk, and a 64-bit bitmap representing free pages in that chunk.
+With 8 KiB pages, this makes it so that at most each P can hold onto 512 KiB of
+The allocation algorithm would thus consist of a P first checking its own cache.
+If it's empty, it would then go into the bitmap and cache the first non-zero
+chunk of 64 bits it sees, noting the base address of those 64 bits.
+It then allocates out of its own cache if able.
+If it's unable to satisfy the allocation from these bits, then it goes back and
+starts searching for contiguous bits, falling back on heap growth if it fails.
+If the allocation request is more than 16 pages in size, then we don't even
+bother checking the cache.
+The probability that `N` consecutive free pages will be available in the page
+cache decreases exponentially as `N` approaches 64, and 16 strikes a good
+balance between being opportunistic and being wasteful.
+Note that allocating the first non-zero chunk of 64 bits is an equivalent
+operation to allocating one page out of the heap: fundamentally we're looking
+for the first free page we can find in both cases.
+This means that we can and should optimize for this case, since we expect that
+it will be extremely common.
+Note also that we can always update the hint address in this case, making all
+subsequent allocations (large and small) faster.
+Finally, there's a little hiccup in doing this and that's that acquiring an
+`mspan` object currently requires acquiring the heap lock, since these objects
+are just taken out of a locked SLAB allocator.
+This means that even if we can perform the allocation uncontended we still need
+the heap lock to get one of these objects.
+We can solve this problem by adding a pool of `mspan` objects to each P, similar
+to the `sudog` cache.
+With the elimination of free spans, scavenging must work a little differently as
+The primary bit of information we're concerned with here is the `scavenged`
+field currently on each span.
+I propose we add a `scavenged` bitmap to each `heapArena` which mirrors the
+allocation bitmap, and represents whether that page has been scavenged or not.
+Allocating any pages would unconditionally clear these bits to avoid adding
+extra work to the allocation path.
+The scavenger's job is now conceptually much simpler.
+It takes bits from both the allocation bitmap as well as the `scavenged` bitmap
+and performs a bitwise-OR operation on the two to determine which pages are
+It then scavenges any contiguous free pages it finds in a single syscall,
+marking the appropriate bits in the `scavenged` bitmap.
+Like the allocator, it would have a hint address to avoid walking over the same
+parts of the heap repeatedly.
+Because this new algorithm effectively requires iterating over the heap
+backwards, there's a slight concern with how much time it could take,
+specifically if it does the scavenge operation with the heap lock held like
+Instead, I propose that the scavenger iterate over the heap without the lock,
+checking the free and scavenged bitmaps optimistically.
+If it finds what appears to be valid set of contiguous scavengable bits, it'll
+acquire the heap lock, verify their validity, and scavenge.
+We're still scavenging with the heap lock held as before, but scaling the
+scavenger is outside the scope of this document (though we certainly have ideas
+#### Huge-page Awareness
+Another piece of the scavenging puzzle is how to deal with the fact that the
+current scavenging policy is huge-page aware.
+There are two dimensions to this huge-page awareness: the runtime counts the
+number of free and unscavenged huge pages for pacing purposes, and the runtime
+scavenges those huge pages first.
+For the first part, the scavenger currently uses an explicit ratio calculated
+whenever the GC trigger is updated to determine the rate at which it should
+scavenge, and it uses the number of free and unscavenged huge pages to determine
+Instead, I propose that the scavenger releases memory one page at a time while
+avoiding breaking huge pages, and it times how long releasing each page takes.
+Given a 1% maximum time spent scavenging for the background scavenger, we may
+then determine the amount of time to sleep, thus effectively letting the
+scavenger set its own rate.
+In some ways this self-pacing is more accurate because we no longer have to make
+order-of-magnitude assumptions about how long it takes to scavenge.
+Also, it represents a significant simplification of the scavenger from an
+engineering perspective; there's much less state we need to keep around in
+The downside to this self-pacing idea is that we must measure time spent
+sleeping and time spent scavenging, which may be funky in the face of OS-related
+context switches and other external anomalies (e.g. someone puts their laptop in
+We can deal with such anomalies by setting bounds on how high or low our
+measurements are allowed to go.
+Furthermore, I propose we manage an EWMA which we feed into the time spent
+sleeping to account for scheduling overheads and try to drive the actual time
+spent scavenging to 1% of the time the goroutine is awake (the same pace as
+As far as scavenging huge pages first goes, I propose we just ignore this aspect
+of the current scavenger simplicity's sake.
+In the original scavenging proposal, the purpose of scavenging huge pages first
+was for throughput: we would get the biggest bang for our buck as soon as
+possible, so huge pages don't get "stuck" behind small pages.
+There's a question as to whether this actually matters in practice: conventional
+wisdom suggests a first-fit policy tends to cause large free fragments to
+congregate at higher addresses.
+By analyzing and simulating scavenging over samples of real Go heaps, I think
+this wisdom mostly holds true.
+The graphs below show a simulation of scavenging these heaps using both
+policies, counting how much of the free heap is scavenged at each moment in
+Ignore the simulated time; the trend is more important.
+![Graph of scavenge throughput.](35112/scavenge-throughput-graph.png)
+With the exception of two applications, the rest all seem to have their free and
+unscavenged huge pages at higher addresses, so the simpler policy leads to a
+similar rate of releasing memory.
+The simulation is based on heap snapshots at the end of program execution, so
+it's a little non-representative since large, long-lived objects, or clusters
+of objects, could have gotten freed just before measurement.
+This misrepresentation actually acts in our favor, however, since it suggests
+an even smaller frequency of huge pages appearing in the middle of the heap.
+The purpose of this proposal is to help the memory allocator scale.
+To reiterate: it's current very easy to put the heap lock in a collapsing state.
+Every page-level allocation must acquire the heap lock, and with 1 KiB objects
+we're already hitting that page on every 10th allocation.
+To give you an idea of what kinds of timings are involved with page-level
+allocations, I took a trace from a 12-hour load test from Kubernetes when I was
+92% of all span allocations were for the first 50 size classes (i.e. up and
+including 8 KiB objects). Each of those, on average, spent 4.0µs in the critical
+section with the heap locked, minus any time spent scavenging.
+The mode of this latency was between 3 and 4µs, with the runner-up being between
+2 and 3µs.
+These numbers were taken with the load test built using Go 1.12.4 and from a
+`linux/amd64` GCE instance.
+Note that these numbers do not include the time it takes to acquire or release
+the heap lock; it is only the time in the critical section.
+I implemented a prototype of this proposal which lives outside of the Go
+runtime, and optimized it over the course of a few days.
+I then took heap samples from large, end-to-end benchmarks to get realistic
+heap layouts for benchmarking the prototype.
+The prototype benchmark then started with these heap samples and allocated out
+of them until the heap was exhausted.
+Without the P cache, allocations took only about 680 ns on average on a similar
+GCE instance to the Kubernetes case, pretty much regardless of heap size.
+This number scaled gracefully relative to allocation size as well.
+To be totally clear, this time includes finding space, marking the space and
+It does not include clearing scavenge bits.
+With the P cache included, that number dropped to 20 ns on average.
+The comparison with the P cache isn't an apples-to-apples comparison since it
+should include heap lock/unlock time on the slow path (and the k8s numbers
+However I believe this only strengthens our case: with the P cache, in theory,
+the lock will be acquired less frequently, so an apples-to-apples comparison
+would be even more favorable to the P cache.
+All of this doesn't even include the cost savings when freeing memory.
+While I do not have numbers regarding the cost of freeing, I do know that the
+free case in the current implementation is a significant source of lock
+Each free currently requires a treap insertion and maybe one or two removals for
+In comparison, freeing memory in this new allocator is faster than allocation
+(without the cache): we know exactly which bits in the bitmap to clear from the
+address, and can quickly index into the arenas array to update them as well as
+While updating the summaries still takes time, we can do even better by freeing
+many pages within the same arena at once, amortizing the cost of this update.
+In fact, the fast page sweeper that Austin added in Go 1.12 already iterates
+over the heap from lowest to highest address, freeing completely free spans.
+It would be straight-forward to batch free operations within the same heap arena
+to achieve this cost amortization.
+In sum, this new page allocator design has the potential to not only solve our
+immediate scalability problem, but also gives us more headroom for future
+optimizations compared to the current treap-based allocator, for which a number
+of various caching strategies, have been designed and/or attempted.
+### Fragmentation Concerns
+The biggest way fragmentation could worsen with this design is as a result of
+the P cache.
+The P cache makes it so that allocation isn't quite exactly a serialized
+single-threaded first-fit, and P may hold onto pages which another P may need
+In practice, given an in-tree prototype, we've seen that this fragmentation
+scales with the number of P's, and we believe this to be a reasonable trade-off:
+more processors generally require more memory to take advantage of parallelism.
+### Prior Art
+As far as prior art is concerned, there hasn't been much work put into bitmap
+allocators in general.
+The reason is likely because most other points in the design space for memory
+management wouldn't really benefit from a bitmap-based page-level allocator.
+Consider Go against other other managed languages with a GC: Go's GC sits in a
+fairly unique point in the design space for GCs because it is a non-moving
+Most allocators in other managed languages (e.g. Java) tend to be bump
+allocators, since they tend to have moving and compacting GCs.
+When considering non-GC'd languages, e.g C/C++, there has been very little work
+in using bitmaps, except for a few niche cases such as [GPU-accelerated
+allocation](https://arxiv.org/abs/1810.11765) and [real-time
+A good point of comparison for Go's current page allocator is TCMalloc, and in
+many ways Go's memory allocator is based on TCMalloc.
+However, there are some key differences that arise as a result of Go's GC.
+Notably, TCMalloc manages its per-CPU caches as arrays of pointers, rather than
+through spans directly like Go does.
+The reason for this, as far as I can tell, is because when a free occurs in
+TCMalloc, that object is immediately available for re-use, whereas with Go,
+object lifetimes are effectively rounded up to a GC cycle.
+As a result of this global, bulk (de)allocation behavior resulting in the lack
+of short-term re-use, I suspect Go tends to ask the page allocator for memory
+more often that TCMalloc does.
+This bulk (de)allocation behavior would thus help explain why page allocator
+scalability hasn't been such a big issue for TCMalloc (again, as far as I'm
+In sum, Go sits in a unique point in the memory management design space, and
+therefore warrants a unique solution.
+The bitmap allocator fits this point in the design space well: bulk allocations
+and frees can be grouped together to amortize the cost of updating the summaries
+thanks to the GC.
+Furthermore, since we don't move objects in the heap, we retain the flexibility
+of dealing with fragments efficiently through the radix tree.
+### Considered Alternatives
+#### Cache spans in a P
+One considered alternative is to keep the current span structure, and instead
+try to cache the spans themselves on a P, splitting them on each allocation
+without acquiring the heap lock.
+While this seems like a good idea in principle, one big limitation is that you
+can only cache contiguous regions of free memory.
+Suppose many heap fragments tend to just be one page in size: one ends up
+having to go back to the page allocator every single time anyway.
+While it is true that one might only cache one page from the heap in the
+proposed design, this case is fairly rare in practice, since it picks up any
+available memory it can find in a given 64-page aligned region.
+The proposed design also tends to have nicer properties: the treap structure
+scales logarithmically (probabilistically) with respect to the number of free
+heap fragments, but even this property doesn't scale too well to very large
+heaps; one might have to chase down 20 pointers in a 20 GiB heap just for an
+allocation, not to mention the additional removals required.
+Small heaps may see allocations as fast as 100 ns, whereas large heaps may see
+page allocation latencies of 4 µs or more on the same hardware.
+On the other hand, the proposed design has a very consistent performance profile
+since the radix tree is effectively perfectly balanced.
+Furthermore, this idea of caching spans only helps the allocation case.
+In most cases the source of contention is not only allocation but also freeing,
+since we always have to do a treap insertion (and maybe one or two removals) on
+the free path.
+In this proposal, the free path is much more efficient in general (no complex
+operations required, just clearing memory), even though it still requires
+acquiring the heap lock.
+Finally, caching spans doesn't really offer much headroom in terms of future
+optimization, whereas switching to a bitmap allocator allows us to make a
+variety of additional optimizations because the design space is mostly
+This proposal changes no public APIs in either syntax or semantics, and is
+therefore Go 1 backwards compatible.
+Michael Knyszek will implement this proposal.
+The implementation will proceed as follows:
+ 1. Change the scavenger to be self-paced to facilitate an easier transition.
+ 1. Graft the prototype (without the P cache) into the runtime.
+ * The plan is to do this as a few large changes which are purely additive
+ and with tests.
+ * The two allocators will live side-by-side, and we'll flip between the two
+ in a single small change.
+ 1. Delete unnecessary code from the old allocator.
+ 1. Create a pool of `mspan` objects for each P.
+ 1. Add a page cache to each P.
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