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@ -367,257 +367,6 @@ how the design of _tbb_ avoids the false cache line sharing.
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## Tested Allocators
We tested _mimalloc_ with 9 leading allocators over 12 benchmarks
and the SpecMark benchmarks. The tested allocators are:
- mi: The _mimalloc_ allocator, using version tag `v1.0.0`.
We also test a secure version of _mimalloc_ as smi which uses
the techniques described in Section [#sec-secure].
- tc: The [_tcmalloc_](https://github.com/gperftools/gperftools)
allocator which comes as part of
the Google performance tools and is used in the Chrome browser.
Installed as package `libgoogle-perftools-dev` version
`2.5-2.2ubuntu3`.
- je: The [_jemalloc_](https://github.com/jemalloc/jemalloc)
allocator by Jason Evans is developed at Facebook
and widely used in practice, for example in FreeBSD and Firefox.
Using version tag 5.2.0.
- sn: The [_snmalloc_](https://github.com/microsoft/snmalloc) allocator
is a recent concurrent message passing
allocator by Liétar et al. \[8]. Using `git-0b64536b`.
- rp: The [_rpmalloc_](https://github.com/rampantpixels/rpmalloc) allocator
uses 32-byte aligned allocations and is developed by Mattias Jansson at Rampant Pixels.
Using version tag 1.3.1.
- hd: The [_Hoard_](https://github.com/emeryberger/Hoard) allocator by
Emery Berger \[1]. This is one of the first
multi-thread scalable allocators. Using version tag 3.13.
- glibc: The system allocator. Here we use the _glibc_ allocator (which is originally based on
_Ptmalloc2_), using version 2.27.0. Note that version 2.26 significantly improved scalability over
earlier versions.
- sm: The [_Supermalloc_](https://github.com/kuszmaul/SuperMalloc) allocator by
Bradley Kuszmaul uses hardware transactional memory
to speed up parallel operations. Using version `git-709663fb`.
- tbb: The Intel [TBB](https://github.com/intel/tbb) allocator that comes with
the Thread Building Blocks (TBB) library \[7].
Installed as package `libtbb-dev`, version `2017~U7-8`.
All allocators run exactly the same benchmark programs on Ubuntu 18.04.1
and use `LD_PRELOAD` to override the default allocator. The wall-clock
elapsed time and peak resident memory (_rss_) are measured with the
`time` program. The average scores over 5 runs are used. Performance is
reported relative to _mimalloc_, e.g. a time of 1.5&times; means that
the program took 1.5&times; longer than _mimalloc_.
[_snmalloc_]: https://github.com/Microsoft/_snmalloc_
[_rpmalloc_]: https://github.com/rampantpixels/_rpmalloc_
## Benchmarks
The first set of benchmarks are real world programs and consist of:
- __cfrac__: by Dave Barrett, implementation of continued fraction factorization which
uses many small short-lived allocations -- exactly the workload
we are targeting for Koka and Lean.
- __espresso__: a programmable logic array analyzer, described by
Grunwald, Zorn, and Henderson \[3]. in the context of cache aware memory allocation.
- __barnes__: a hierarchical n-body particle solver \[4] which uses relatively few
allocations compared to `cfrac` and `espresso`. Simulates the gravitational forces
between 163840 particles.
- __leanN__: The [Lean](https://github.com/leanprover/lean) compiler by
de Moura _et al_, version 3.4.1,
compiling its own standard library concurrently using N threads
(`./lean --make -j N`). Big real-world workload with intensive
allocation.
- __redis__: running the [redis](https://redis.io/) 5.0.3 server on
1 million requests pushing 10 new list elements and then requesting the
head 10 elements. Measures the requests handled per second.
- __larsonN__: by Larson and Krishnan \[2]. Simulates a server workload using 100 separate
threads which each allocate and free many objects but leave some
objects to be freed by other threads. Larson and Krishnan observe this
behavior (which they call _bleeding_) in actual server applications,
and the benchmark simulates this.
The second set of benchmarks are stress tests and consist of:
- __alloc-test__: a modern allocator test developed by
OLogN Technologies AG ([ITHare.com](http://ithare.com/testing-memory-allocators-ptmalloc2-tcmalloc-hoard-jemalloc-while-trying-to-simulate-real-world-loads/))
Simulates intensive allocation workloads with a Pareto size
distribution. The _alloc-testN_ benchmark runs on N cores doing
100&middot;10^6^ allocations per thread with objects up to 1KiB
in size. Using commit `94f6cb`
([master](https://github.com/node-dot-cpp/alloc-test), 2018-07-04)
- __sh6bench__: by [MicroQuill](http://www.microquill.com/) as part of SmartHeap. Stress test
where some of the objects are freed in a
usual last-allocated, first-freed (LIFO) order, but others are freed
in reverse order. Using the
public [source](http://www.microquill.com/smartheap/shbench/bench.zip)
(retrieved 2019-01-02)
- __sh8benchN__: by [MicroQuill](http://www.microquill.com/) as part of SmartHeap. Stress test for
multi-threaded allocation (with N threads) where, just as in _larson_,
some objects are freed by other threads, and some objects freed in
reverse (as in _sh6bench_). Using the
public [source](http://www.microquill.com/smartheap/SH8BENCH.zip)
(retrieved 2019-01-02)
- __xmalloc-testN__: by Lever and Boreham \[5] and Christian Eder. We use the updated
version from the SuperMalloc repository. This is a more
extreme version of the _larson_ benchmark with 100 purely allocating threads,
and 100 purely deallocating threads with objects of various sizes migrating
between them. This asymmetric producer/consumer pattern is usually difficult
to handle by allocators with thread-local caches.
- __cache-scratch__: by Emery Berger \[1]. Introduced with the Hoard
allocator to test for _passive-false_ sharing of cache lines: first
some small objects are allocated and given to each thread; the threads
free that object and allocate immediately another one, and access that
repeatedly. If an allocator allocates objects from different threads
close to each other this will lead to cache-line contention.
## On a 16-core AMD EPYC running Linux
Testing on a big Amazon EC2 instance ([r5a.4xlarge](https://aws.amazon.com/ec2/instance-types/))
consisting of a 16-core AMD EPYC 7000 at 2.5GHz
with 128GB ECC memory, running Ubuntu 18.04.1 with LibC 2.27 and GCC 7.3.0.
We excluded SuperMalloc here as it use transactional memory instructions
that are usually not supported in a virtualized environment.
![bench-r5a-1](doc/bench-r5a-1.svg)
![bench-r5a-2](doc/bench-r5a-2.svg)
Memory usage:
![bench-r5a-rss-1](doc/bench-r5a-rss-1.svg)
![bench-r5a-rss-1](doc/bench-r5a-rss-2.svg)
(note: the _xmalloc-testN_ memory usage should be disregarded is it
allocates more the faster the program runs).
In the first five benchmarks we can see _mimalloc_ outperforms the other
allocators moderately, but we also see that all these modern allocators
perform well -- the times of large performance differences in regular
workloads are over. In
_cfrac_ and _espresso_, _mimalloc_ is a tad faster than _tcmalloc_ and
_jemalloc_, but a solid 10\% faster than all other allocators on
_espresso_. The _tbb_ allocator does not do so well here and lags more than
20\% behind _mimalloc_. The _cfrac_ and _espresso_ programs do not use much
memory (~1.5MB) so it does not matter too much, but still _mimalloc_ uses
about half the resident memory of _tcmalloc_.
The _leanN_ program is most interesting as a large realistic and
concurrent workload and there is a 8% speedup over _tcmalloc_. This is
quite significant: if Lean spends 20% of its time in the
allocator that means that _mimalloc_ is 1.3&times; faster than _tcmalloc_
here. This is surprising as that is *not* measured in a pure
allocation benchmark like _alloc-test_. We conjecture that we see this
outsized improvement here because _mimalloc_ has better locality in
the allocation which improves performance for the *other* computations
in a program as well.
The _redis_ benchmark shows more differences between the allocators where
_mimalloc_ is 14\% faster than _jemalloc_. On this benchmark _tbb_ (and _Hoard_) do
not do well and are over 40\% slower.
The _larson_ server workload which allocates and frees objects between
many threads shows even larger differences, where _mimalloc_ is more than
2.5&times; faster than _tcmalloc_ and _jemalloc_ which is quite surprising
for these battle tested allocators -- probably due to the object
migration between different threads. This is a difficult benchmark for
other allocators too where _mimalloc_ is still 48% faster than the next
fastest (_snmalloc_).
The second benchmark set tests specific aspects of the allocators and
shows even more extreme differences between them.
The _alloc-test_ is very allocation intensive doing millions of
allocations in various size classes. The test is scaled such that when an
allocator performs almost identically on _alloc-test1_ as _alloc-testN_ it
means that it scales linearly. Here, _tcmalloc_, _snmalloc_, and
_Hoard_ seem to scale less well and do more than 10% worse on the
multi-core version. Even the best allocators (_tcmalloc_ and _jemalloc_) are
more than 10% slower as _mimalloc_ here.
Also in _sh6bench_ _mimalloc_ does much
better than the others (more than 2&times; faster than _jemalloc_).
We cannot explain this well but believe it is
caused in part by the "reverse" free-ing pattern in _sh6bench_.
Again in _sh8bench_ the _mimalloc_ allocator handles object migration
between threads much better and is over 36% faster than the next best
allocator, _snmalloc_. Whereas _tcmalloc_ did well on _sh6bench_, the
addition of object migration caused it to be almost 3 times slower
than before.
The _xmalloc-testN_ benchmark simulates an asymmetric workload where
some threads only allocate, and others only free. The _snmalloc_
allocator was especially developed to handle this case well as it
often occurs in concurrent message passing systems. Here we see that
the _mimalloc_ technique of having non-contended sharded thread free
lists pays off and it even outperforms _snmalloc_. Only _jemalloc_
also handles this reasonably well, while the others underperform by
a large margin. The optimization on _mimalloc_ to do a *delayed free*
only once for full pages is quite important -- without it _mimalloc_
is almost twice as slow (as then all frees contend again on the
single heap delayed free list).
The _cache-scratch_ benchmark also demonstrates the different
architectures of the allocators nicely. With a single thread they all
perform the same, but when running with multiple threads the allocator
induced false sharing of the cache lines causes large run-time
differences, where _mimalloc_ is more than 18&times; faster than _jemalloc_ and
_tcmalloc_! Crundal \[6] describes in detail why the false cache line
sharing occurs in the _tcmalloc_ design, and also discusses how this
can be avoided with some small implementation changes.
Only _snmalloc_ and _tbb_ also avoid the
cache line sharing like _mimalloc_. Kukanov and Voss \[7] describe in detail
how the design of _tbb_ avoids the false cache line sharing.
The _Hoard_ allocator is also specifically
designed to avoid this false sharing and we are not sure why it is not
doing well here (although it runs still 5&times; as fast as _tcmalloc_).
## On a 4-core Intel Xeon workstation
Below are the benchmark results on an HP
Z4-G4 workstation with a 4-core Intel® Xeon® W2123 at 3.6 GHz with 16GB
ECC memory, running Ubuntu 18.04.1 with LibC 2.27 and GCC 7.3.0.
![bench-z4-1](doc/bench-z4-1.svg)
![bench-z4-2](doc/bench-z4-2.svg)
Memory usage:
![bench-z4-rss-1](doc/bench-z4-rss-1.svg)
![bench-z4-rss-2](doc/bench-z4-rss-2.svg)
(note: the _xmalloc-testN_ memory usage should be disregarded is it
allocates more the faster the program runs).
This time SuperMalloc (_sm_) is included as this platform supports
hardware transactional memory. Unfortunately,
there are no entries for _SuperMalloc_ in the _leanN_ and _xmalloc-testN_ benchmarks
as it faulted on those. We also added the secure version of
_mimalloc_ as smi.
Overall, the relative results are quite similar as before. Most
allocators fare better on the _larsonN_ benchmark now -- either due to
architectural changes (AMD vs. Intel) or because there is just less
concurrency. Unfortunately, the SuperMalloc faulted on the _leanN_
and _xmalloc-testN_ benchmarks.
The secure mimalloc version uses guard pages around each (_mimalloc_) page,
encodes the free lists and uses randomized initial free lists, and we
expected it would perform quite a bit worse -- but on the first benchmark set
it performed only about 3% slower on average, and is second best overall.
-->
# References
- \[1] Emery D. Berger, Kathryn S. McKinley, Robert D. Blumofe, and Paul R. Wilson.
@ -651,3 +400,14 @@ it performed only about 3% slower on average, and is second best overall.
Alex Shamis, Christoph M Wintersteiger, and David Chisnall.
_Snmalloc: A Message Passing Allocator._
In Proceedings of the 2019 ACM SIGPLAN International Symposium on Memory Management, 122–135. ACM. 2019.
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