17 KiB
mimalloc
mimalloc (pronounced "me-malloc") is a general purpose allocator with excellent performance characteristics. Initially developed by Daan Leijen for the run-time systems of the Koka and Lean languages.
It is a drop-in replacement for malloc
and can be used in other programs
without code changes, for example, on Unix you can use it as:
> LD_PRELOAD=/usr/bin/libmimalloc.so myprogram
Notable aspects of the design include:
- small and consistent: the library is less than 3500 LOC using simple and consistent data structures. This makes it very suitable to integrate and adapt in other projects. For runtime systems it provides hooks for a monotonic heartbeat and deferred freeing (for bounded worst-case times with reference counting).
- free list sharding: the big idea: instead of one big free list (per size class) we have many smaller lists per memory "page" which both reduces fragmentation and increases locality -- things that are allocated close in time get allocated close in memory. (A memory "page" in mimalloc contains blocks of one size class and is usually 64KB on a 64-bit system).
- eager page reset: when a "page" becomes empty (with increased chance due to free list sharding) the memory is marked to the OS as unused ("reset" or "purged") reducing (real) memory pressure and fragmentation, especially in long running programs.
- lazy initialization: pages in a segment are lazily initialized so no memory is touched until it becomes allocated, reducing the resident memory and potential page faults.
- bounded: it does not suffer from blowup [1], has bounded worst-case allocation times (wcat), bounded space overhead (~0.2% meta-data, with at most 16.7% waste in allocation sizes), and has no internal points of contention using atomic operations almost everywhere.
Enjoy!
Building
Windows
Open ide/vs2017/mimalloc.sln
in Visual Studio 2017 and build.
The mimalloc
project builds a static library (in out/msvc-x64
), while the
mimalloc-override
project builds a DLL for overriding malloc
in the entire program.
MacOSX, Linux, BSD, etc.
We use cmake
1 as the build system:
-
cd out/release
-
cmake ../..
(generate the make file) -
make
(and build)This builds the library as a shared (dynamic) library (
.so
or.dylib
), a static library (.a
), and as a single object file (.o
). -
sudo make install
(install the library and header files in/usr/local/lib
and/usr/local/include
)
You can build the debug version which does many internal checks and maintains detailed statistics as:
-
cd out/debug
-
cmake -DCMAKE_BUILD_TYPE=Debug ../..
-
make
This will name the shared library as
libmimalloc-debug.so
.
Or build with clang
:
CC=clang cmake ../..
Use ccmake
2 instead of cmake
to see and customize all the available build options.
Notes:
- Install CMake:
sudo apt-get install cmake
- Install CCMake:
sudo apt-get install cmake-curses-gui
Using the library
The preferred usage is including <mimalloc.h>
, linking with
the shared- or static library, and using the mi_malloc
API exclusively for allocation. For example,
gcc -o myprogram -lmimalloc myfile.c
mimalloc uses only safe OS calls (mmap
and VirtualAlloc
) and can co-exist
with other allocators linked to the same program.
If you use cmake
, you can simply use:
find_package(mimalloc 1.0 REQUIRED)
in your CMakeLists.txt
to find a locally installed mimalloc. Then use either:
target_link_libraries(myapp PUBLIC mimalloc)
to link with the shared (dynamic) library, or:
target_link_libraries(myapp PUBLIC mimalloc-static)
to link with the static library. See test\CMakeLists.txt
for an example.
You can pass environment variables to print verbose messages (MIMALLOC_VERBOSE=1
)
and statistics (MIMALLOC_STATS=1
) (in the debug version):
> env MIMALLOC_STATS=1 ./cfrac 175451865205073170563711388363
175451865205073170563711388363 = 374456281610909315237213 * 468551
heap stats: peak total freed unit
normal 2: 16.4 kb 17.5 mb 17.5 mb 16 b ok
normal 3: 16.3 kb 15.2 mb 15.2 mb 24 b ok
normal 4: 64 b 4.6 kb 4.6 kb 32 b ok
normal 5: 80 b 118.4 kb 118.4 kb 40 b ok
normal 6: 48 b 48 b 48 b 48 b ok
normal 17: 960 b 960 b 960 b 320 b ok
heap stats: peak total freed unit
normal: 33.9 kb 32.8 mb 32.8 mb 1 b ok
huge: 0 b 0 b 0 b 1 b ok
total: 33.9 kb 32.8 mb 32.8 mb 1 b ok
malloc requested: 32.8 mb
committed: 58.2 kb 58.2 kb 58.2 kb 1 b ok
reserved: 2.0 mb 2.0 mb 2.0 mb 1 b ok
reset: 0 b 0 b 0 b 1 b ok
segments: 1 1 1
-abandoned: 0
pages: 6 6 6
-abandoned: 0
mmaps: 3
mmap fast: 0
mmap slow: 1
threads: 0
elapsed: 2.022s
process: user: 1.781s, system: 0.016s, faults: 756, reclaims: 0, rss: 2.7 mb
The above model of using the mi_
prefixed API is not always possible
though in existing programs that already use the standard malloc interface,
and another option is to override the standard malloc interface
completely and redirect all calls to the mimalloc library instead.
Overriding Malloc
Overriding the standard malloc
can be done either dynamically or statically.
Dynamic override
This is the recommended way to override the standard malloc interface.
Unix, BSD, MacOSX
On these systems we preload the mimalloc shared
library so all calls to the standard malloc
interface are
resolved to the mimalloc library.
-
env LD_PRELOAD=/usr/lib/libmimalloc.so myprogram
(on Linux, BSD, etc.) -
env DYLD_INSERT_LIBRARIES=usr/lib/libmimalloc.dylib myprogram
(On MacOSX)Note certain security restrictions may apply when doing this from the shell.
You can set extra environment variables to check that mimalloc is running, like:
env MIMALLOC_VERBOSE=1 LD_PRELOAD=/usr/lib/libmimalloc.so myprogram
or run with the debug version to get detailed statistics:
env MIMALLOC_STATS=1 LD_PRELOAD=/usr/lib/libmimalloc-debug.so myprogram
Windows
On Windows you need to link your program explicitly with the mimalloc
DLL, and use the C-runtime library as a DLL (the /MD
or /MDd
switch).
To ensure the mimalloc DLL gets loaded it is easiest to insert some
call to the mimalloc API in the main
function, like mi_version()
.
Due to the way mimalloc intercepts the standard malloc at runtime, it is best
to link to the mimalloc import library first on the command line so it gets
loaded right after the universal C runtime DLL (ucrtbase
). See
the mimalloc-override-test
project for an example.
Static override
On Unix systems, you can also statically link with mimalloc to override the standard
malloc interface. The recommended way is to link the final program with the
mimalloc single object file (mimalloc-override.o
(or .obj
)). We use
an object file instead of a library file as linkers give preference to
that over archives to resolve symbols. To ensure that the standard
malloc interface resolves to the mimalloc library, link it as the first
object file. For example:
gcc -o myprogram mimalloc-override.o myfile1.c ...
Performance
Tldr: In our benchmarks, mimalloc always outperforms all other leading allocators (jemalloc, tcmalloc, hoard, and glibc), and usually uses less memory (with less then 25% more in the worst case) (as of Jan 2019). A nice property is that it does consistently well over a wide range of benchmarks.
Disclaimer: allocators are interesting as there is no optimal algorithm -- for a given allocator one can always construct a workload where it does not do so well. The goal is thus to find an allocation strategy that performs well over a wide range of benchmarks without suffering from underperformance in less common situations (which is what our second benchmark set tests for).
Benchmarking
We tested mimalloc with 5 other allocators over 11 benchmarks. The tested allocators are:
- mi: The mimalloc allocator (version tag
v1.0.0
). - je: jemalloc, by Jason Evans (Facebook);
currently (2018) one of the leading allocators and is widely used, for example
in BSD, Firefox, and at Facebook. Installed as package
libjemalloc-dev:amd64/bionic 3.6.0-11
. - tc: tcmalloc, by Google as part of the performance tools.
Highly performant and used in the Chrome browser. Installed as package
libgoogle-perftools-dev:amd64/bionic 2.5-2.2ubuntu3
. - jx: A compiled version of a more recent instance of jemalloc.
Using commit
7a815c1b
(dev, 2019-01-15). - hd: Hoard, by Emery Berger [1].
One of the first multi-thread scalable allocators.
(master, 2019-01-01, version tag
3.13
) - mc: The system allocator. Here we use the LibC allocator (which is originally based on PtMalloc). Using version 2.27. (Note that version 2.26 significantly improved scalability over earlier versions).
All allocators run exactly the same benchmark programs and use LD_PRELOAD
to override the system 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
(variation between runs is very low though).
Performance is reported relative to mimalloc, e.g. a time of 106% means that
the program took 6% longer to finish than with mimalloc.
On a 16-core AMD EPYC running Linux
Testing on a big Amazon EC2 instance (r5a.4xlarge) 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.
The first benchmark set consists of programs that allocate a lot:
Memory usage:
The benchmarks above are (with N=16 in our case):
- cfrac: by Dave Barrett, implementation of continued fraction factorization:
uses many small short-lived allocations. Factorizes as
./cfrac 175451865205073170563711388363274837927895
. - espresso: a programmable logic array analyzer [3].
- barnes: a hierarchical n-body particle solver [4]. Simulates 163840 particles.
- leanN: by Leonardo de Moura et al, the lean
compiler, version 3.4.1, compiling its own standard library concurrently using N cores (
./lean --make -j N
). Big real-world workload with intensive allocation, takes about 1:40s when running on a single high-end core. - redis: running the redis 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.
- alloc-test: a modern allocator test
developed by by OLogN Technologies AG at ITHare.com. Simulates intensive allocation workloads with a Pareto
size distribution. The
alloc-testN
benchmark runs on N cores doing 100×106 allocations per thread with objects up to 1KB in size. Using commit94f6cb
(master, 2018-07-04)
We can see mimalloc outperforms the other allocators moderately but all
these modern allocators perform well.
In cfrac
, mimalloc is about 13%
faster than jemalloc for many small and short-lived allocations.
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 (and 4× less than Hoard on espresso
).
The leanN
program is most interesting as a large realistic and concurrent
workload and there is a 6% speedup over both tcmalloc and jemalloc. (This is
quite significant: if Lean spends (optimistically) 20% of its time in the allocator
that implies a 1.5× speedup with mimalloc).
The large redis
benchmark shows a similar speedup.
The alloc-test
is very allocation intensive and we see the largest
diffrerences here when running with 16 cores in parallel.
The second benchmark tests specific aspects of the allocators and shows more extreme differences between allocators:
The benchmarks in the second set are (again with N=16):
- larson: by Larson and Krishnan [2]. Simulates a server workload using 100 separate threads where they 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.
- sh6bench: by MicroQuill as part of SmartHeap. Stress test for single-threaded allocation 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 (retrieved 2019-01-02)
- sh8bench: by MicroQuill as part of SmartHeap. Stress test for
multithreaded allocation (with N threads) where, just as in
larson
, some objects are freed by other threads, and some objects freed in reverse (as insh6bench
). Using the public source (retrieved 2019-01-02) - cache-scratch: by Emery Berger et al [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 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.
In the larson
server workload mimalloc is 2.5× faster than
tcmalloc and jemalloc which is quite surprising -- probably due to the object
migration between different threads. Also in sh6bench
mimalloc does much
better than the others (more than 4× faster than jemalloc).
We cannot explain this well but believe it may be
caused in part by the "reverse" free-ing in sh6bench
. Again in sh8bench
the mimalloc allocator handles object migration between threads much better .
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
20× faster than tcmalloc here. Only the original jemalloc does almost
as well (but the most recent version, jxmalloc, regresses). The
Hoard allocator is specifically designed to avoid this false sharing and we
are not sure why it is not doing well here (although it still runs almost 5×
faster than tcmalloc and jxmalloc).
Benchmarks on a 4-core Intel workstation
References
-
1] Emery D. Berger, Kathryn S. McKinley, Robert D. Blumofe, and Paul R. Wilson. _Hoard: A Scalable Memory Allocator for Multithreaded Applications_ the Ninth International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS-IX). Cambridge, MA, November 2000. [pdf](http://www.cs.utexas.edu/users/mckinley/papers/asplos-2000.pdf)
-
2] P. Larson and M. Krishnan. _Memory allocation for long-running server applications_. In ISMM, Vancouver, B.C., Canada, 1998. [pdf](http://citeseemi.ist.psu.edu/viewdoc/download;jsessionid=5F0BFB4F57832AEB6C11BF8257271088?doi=10.1.1.45.1947&rep=rep1&type=pdf)
-
3] D. Grunwald, B. Zorn, and R. Henderson. _Improving the cache locality of memory allocation_. In R. Cartwright, editor, Proceedings of the Conference on Programming Language Design and Implementation, pages 177–186, New York, NY, USA, June 1993. [pdf](http://citeseemi.ist.psu.edu/viewdoc/download?doi=10.1.1.43.6621&rep=rep1&type=pdf)
-
4] J. Barnes and P. Hut. _A hierarchical O(n*log(n)) force-calculation algorithm_. Nature, 324:446-449, 1986.