The not-so-simple task of comparing two memory allocators

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(Portray by Theresa Bloise)

Since version 4.10, OCaml provides a brand novel simplest-match memory allocator
alongside its existing default, the next-match allocator. At Jane
Road, we’ve viewed a colossal enchancment after switching over to the novel
allocator.

This post isn’t about how the novel allocator works. For that, basically the most efficient
provide is these notes from a discuss by its
author
.

As a replace, this post is set true how difficult it is to examine two
allocators in an realistic ability, especially for a rubbish-silent
system.

Benchmarks

One of many benchmarks we looked at in fact slowed down after we switched
allocator, going from this (next-match):

$ time ./bench.exe 50000
trusty    0m34.282s

to this (simplest-match):

$ time ./bench.exe 50000
trusty    0m36.115s

But that’s now not your complete memoir. Essentially the most easy-match memory allocator reduces
fragmentation, packing allocations together extra tightly. If we
measure both time spent and memory old, we survey there’s a substitute-off
here, with simplest-match working pretty slower nonetheless the utilization of less memory:

single datapoints of time and space usage

But that’s now not your complete memoir either. It would be, in a language with
handbook memory management, where the allocator has to take care of a
sequence of malloc and free calls certain by this technique. On
the tons of hand, in a language with rubbish series, the GC gets to
win when memory is freed. By collecting extra slowly, we free later,
the utilization of additional memory and no more time. Adjusting the GC price trades home
and time.

So, in a GC’d language the performance of a program is now not described
by a single (home, time) pair, nonetheless by a curve of (home, time) pairs
describing the accessible tradeoff. The ability to invent this tradeoff in
OCaml is to regulate the space_overhead parameter from its default
of 80. We ran the same benchmark with space_overhead varying from 20
to 320 (that is, from 1/4 to 4x its default payment), giving us a extra
complete home/time curve for this benchmark. The benchmark is
pretty noisy, nonetheless we can aloof survey a separation between simplest-match
and next-match:

whole curves of time and space usage

Right here, simplest-match handily beats next match, whether or now not optimising for time or
home. Conceal that for every blue level there may be an orange level below
and left of it, doubtless with a certain space_overhead payment. (Furthermore
display cover that these numbers come from one amongst the benchmarks that simplest-match
performed the worst on.)

In the default configuration, simplest-match picks a degree on the curve
that’s pretty extra to the true than next-match: it’s optimising extra
aggressively for home use than time spent. In hindsight, here’s to
be expected: internally, the space_overhead measure doesn’t win
into story fragmentation, so for a given space_overhead payment
simplest-match will use less home than next-match, because it fragments less.

That’s practically your complete memoir. There are true two questions left:
what exactly form point out point out by “memory use” and where did the curves
come from?

Measuring memory use

The y axis above is marked “memory use”. There are suprisingly many
suggestions to measure memory use, and selecting the scandalous one may possibly moreover be
misleading. Essentially the obvious candidate is OCaml’s top_heap_size,
accessible from Gc.stat. It goes to mislead for 2
causes:

  • It’s quantized: OCaml grows the heap in orderly chunks, so a minor
    enchancment in memory use (e.g. 5-10%) on the full acquired’t affect
    top_heap_size in any appreciate.

  • It’s an overestimate: Incessantly, now not the full
    heap is old. The stage to which this happens depends on the
    allocator.

As a replace, the memory axis above reveals Linux’s measurements of RSS (this
is printed by /usr/bin/time, and is one amongst the columns in top). RSS is
“resident space dimension”, the amount of trusty bodily RAM in use by the
program. That is all the time lower than the amount distributed: Linux waits
till memory is old sooner than allocating RAM to it, so as that the RAM can
be old extra usefully (e.g. as disk cache) for the time being. (This
behaviour is now not the same thing as VM overcommit: Linux allocates RAM
lazily whatever the overcommit setting. If overcommit is disabled, it
will most inviting allow allocations if there’s adequate RAM+swap to take care of all
of them being old concurrently, nonetheless even on this case this can
populate them lazily, preferring to utilize RAM as cache in the
intervening time).

The relationship between top_heap_size and RSS differs between allocators:

allocator rss usage comparison

This graph reveals the same benchmark trudge with tons of iteration
counts. Each and every datapoint is a separate trudge of this technique, whose memory
use is elevated with elevated iteration counts. The RSS strains are shifted
vertically to align on the left: with out this substitute, the RSS strains
are elevated than the heap dimension because they moreover embody binary
dimension. The shifted RSS strains pretty exceed top heap dimension on the true
of the graph, since now not pretty the full memory distributed is heap
(this happens on both nonetheless is extra obvious on next-match).

Witness that the following-match allocator on the full makes use of the full memory it
allocates: when this allocator finds the orderly empty block on the finish
of the heap, it latches on and allocates from it till it’s empty and
the full distributed heap has been old. Most inviting-match, by difference,
manages to suit novel allocations into holes in the heap, and most inviting draws
from the orderly empty block when it needs to. This implies that the
memory use is lower: even when the heap expands, simplest-match doesn’t
space off extra RAM to be old till it’s wanted. In tons of words, there may be
a further home enchancment of switching to simplest-match that doesn’t
display cover up in measurements of top_heap_size, which is why the major graph
above plots memory as measured by RSS.

Modelling the major GC

The curves in the major graph above are derived by fitting a
three-parameter mannequin to the runtime and home utilization files
components. Right here’s how that mannequin is derived, and roughly what the
parameters point out.

The time taken by essential series, below the fashioned nonetheless
now not-specifically-cheap assumption that all and sundry cycles are the same,
is (mark_time + sweep_time) #cycles. Stamp time is proportional to
the scale of live heap (a property of this technique itself, fair
of GC settings like space_overhead), and sweep time is proportional
to the scale of the live heap + G, the amount of rubbish silent
per cycle. This quantity G is roughly the amount distributed, so the
series of cycles is roughly the full allocations (one other property of
this technique itself) divided by G.

The finish result is that the full runtime is roughly some affine linear
characteristic of (1/G), and the full heap dimension is roughly G plus a
constant. Which manner that heap dimension is a characteristic of runtime as
follows:

for three constants a, b, c. Becoming this 3-parameter mannequin
provides the curves in the customary graph.

The parameters a, b and c have easy
interpretations. a is the vertical asymptote, which is the minimal
quantity of time this technique can win if it does no series at
all. This contains this technique code plus the allocator, so simplest-match
improves a by being quicker to allocate. c is the horizontal
asymptote, the minimal quantity of home this technique can use if it
collects repeatedly. This contains the live files plus any home
lost to fragmentation, so simplest-match improves c by fragmenting
less. One blueprint or the other, b determines the form of the curve between the 2
asymptotes. That is broadly identical between the 2 allocators, since
altering the allocator doesn’t strongly affect how instant marking and
sweeping may possibly moreover be completed (even supposing quit tuned here, as there’s some
work in growth on speeding up marking and sweeping with
prefetching).

Conclusion

Switching allocators from next-match to simplest-match has made most capabilities
quicker and smaller, nonetheless it absolutely’s elegant how great work it took to be
in a plan to narrate that confidently!

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