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 dynamically linked ELF-based systems (Linux, BSD, etc.) you can use it as:
> LD_PRELOAD=/usr/bin/libmimalloc.so myprogram
Notable aspects of the design include:
- small and consistent: the library is about 6k 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 64KiB 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.
- secure: mimalloc can be built in secure mode, adding guard pages, randomized allocation, encrypted free lists, etc. to protect against various heap vulnerabilities. The performance penalty is only around 3% on average over our benchmarks.
- first-class heaps: efficiently create and use multiple heaps to allocate across different regions. A heap can be destroyed at once instead of deallocating each object separately.
- bounded: it does not suffer from blowup , has bounded worst-case allocation times (wcat), bounded space overhead (~0.2% meta-data, with at most 12.5% waste in allocation sizes), and has no internal points of contention using only atomic operations.
- fast: In our benchmarks (see below), mimalloc always outperforms all other leading allocators (jemalloc, tcmalloc, Hoard, etc), and usually uses less memory (up to 25% more in the worst case). A nice property is that it does consistently well over a wide range of benchmarks.
v1.1.0: stable release 1.1.
v1.0.8: pre-release 8: more robust windows dynamic overriding, initial huge page support.
v1.0.6: pre-release 6: various performance improvements.
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.
macOS, Linux, BSD, etc.
cmake1 as the build system:
> mkdir -p out/release > cd out/release > cmake ../.. > make
This builds the library as a shared (dynamic) library (
.dylib), a static library (
.a), and as a single object file (
> sudo make install (install the library and header files in
You can build the debug version which does many internal checks and maintains detailed statistics as:
> mkdir -p out/debug > cd out/debug > cmake -DCMAKE_BUILD_TYPE=Debug ../.. > make
This will name the shared library as
Finally, you can build a secure version that uses guard pages, encrypted free lists, etc, as:
> mkdir -p out/secure > cd out/secure > cmake -DMI_SECURE=ON ../.. > make
This will name the shared library as
ccmake2 instead of
cmake to see and customize all the available build options.
- 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 (
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)
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_SHOW_STATS=1) (in the debug version):
> env MIMALLOC_SHOW_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.
You can set further options either programmatically (using
mi_option_set), or via environment variables.
MIMALLOC_SHOW_STATS=1: show statistics when the program terminates.
MIMALLOC_VERBOSE=1: show verbose messages.
MIMALLOC_SHOW_ERRORS=1: show error and warning messages.
MIMALLOC_LARGE_OS_PAGES=1: use large OS pages when available; for some workloads this can significantly improve performance. Use
MIMALLOC_VERBOSEto check if the large OS pages are enabled -- usually one needs to explicitly allow large OS pages (as on Windows and Linux). However, sometimes the OS is very slow to reserve contiguous physical memory for large OS pages so use with care on systems that can have fragmented memory.
MIMALLOC_EAGER_REGION_COMMIT=1: on Windows, commit large (256MiB) regions eagerly. On Windows, these regions show in the working set even though usually just a small part is committed to physical memory. This is why it turned off by default on Windows as it looks not good in the task manager. However, in reality it is always better to turn it on as it improves performance and has no other drawbacks.
MIMALLOC_RESERVE_HUGE_OS_PAGES=N: where N is the number of 1GiB huge OS pages. This reserves the huge pages at startup and can give quite a performance improvement on long running workloads. Usually it is better to not use
MIMALLOC_LARGE_OS_PAGESin combination with this setting. Just like large OS pages, use with care as reserving contiguous physical memory can take a long time when memory is fragmented. Still experimental.
Overriding the standard
malloc can be done either dynamically or statically.
This is the recommended way to override the standard malloc interface.
On these ELF-based 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
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_SHOW_STATS=1 LD_PRELOAD=/usr/lib/libmimalloc-debug.so myprogram
On macOS we can also preload the mimalloc shared library so all calls to the standard
malloc interface are resolved to the mimalloc library.
> env DYLD_FORCE_FLAT_NAMESPACE=1 DYLD_INSERT_LIBRARIES=/usr/lib/libmimalloc.dylib myprogram
Note that certain security restrictions may apply when doing this from the shell.
Note: unfortunately, at this time, dynamic overriding on macOS seems broken but it is actively worked on to fix this (see issue
On Windows you need to link your program explicitly with the mimalloc DLL and use the C-runtime library as a DLL (using the
/MDd switch). Moreover, you need to ensure the
mimalloc-redirect32.dll) is available in the same folder as the mimalloc DLL at runtime (as it as referred to by the mimalloc DLL). The redirection DLL's ensure all calls to the C runtime malloc API get redirected to mimalloc.
To ensure the mimalloc DLL is loaded at run-time it is easiest to insert some call to the mimalloc API in the
main function, like
mi_version() (or use the
/INCLUDE:mi_version switch on the linker). See the
mimalloc-override-test project for an example on how to use this.
The environment variable
MIMALLOC_DISABLE_REDIRECT=1 can be used to disable dynamic overriding at run-time. Use
MIMALLOC_VERBOSE=1 to check if mimalloc successfully redirected.
(Note: in principle, it should be possible to patch existing executables that are linked with the dynamic C runtime (
ucrtbase.dll) by just putting the mimalloc DLL into the import table (and putting
mimalloc-redirect.dll in the same folder) Such patching can be done for example with CFF Explorer).
On Unix-like 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). 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 ...
We tested mimalloc against many other top allocators over a wide range of benchmarks, ranging from various real world programs to synthetic benchmarks that see how the allocator behaves under more extreme circumstances.
In our benchmarks, mimalloc always outperforms all other leading allocators (jemalloc, tcmalloc, Hoard, etc), and usually uses less memory (up to 25% more in the worst case). A nice property is that it does consistently well over the wide range of benchmarks.
Allocators are interesting as there exists no algorithm that is generally optimal -- for a given allocator one can usually 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 the second half of our benchmark set tests for).
We show here only the results on an AMD EPYC system (Apr 2019) -- for specific details and further benchmarks we refer to the technical report.
The benchmark suite is scripted and available separately as mimalloc-bench.
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 measured allocators are mimalloc (mi), Google's tcmalloc (tc) used in Chrome, jemalloc (je) by Jason Evans used in Firefox and FreeBSD, snmalloc (sn) by Liétar et al. , rpmalloc (rp) by Mattias Jansson at Rampant Pixels, Hoard by Emery Berger , the system allocator (glibc) (based on PtMalloc2), and the Intel thread building blocks allocator (tbb).
(note: the xmalloc-testN memory usage should be disregarded as 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 of the Lean theorem prover compiling its own standard library, 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× 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 allocates and frees objects between many threads. Larson and Krishnan  observe this behavior (which they call bleeding) in actual server applications, and the benchmark simulates this. Here, mimalloc is more than 2.5× faster than tcmalloc and jemalloc 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, by OLogN Technologies AG, is a very allocation intensive benchmark 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.
The sh6bench and sh8bench benchmarks are developed by MicroQuill as part of SmartHeap. In sh6bench mimalloc does much better than the others (more than 2× 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 by Lever and Boreham  and Christian Eder, 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 (like the [Pony] language for which snmalloc was initially developed). Here we see that the mimalloc technique of having non-contended sharded thread free lists pays off as it even outperforms snmalloc here. Only jemalloc also handles this reasonably well, while the others underperform by a large margin.
The cache-scratch benchmark by Emery Berger , and introduced with the Hoard allocator to test for passive-false sharing of cache lines. With a single thread they all perform the same, but when running with multiple threads the potential allocator induced false sharing of the cache lines causes large run-time differences, where mimalloc is more than 18× faster than jemalloc and tcmalloc! Crundal  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  describe in detail how the design of tbb avoids the false cache line sharing.
 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
 P. Larson and M. Krishnan. Memory allocation for long-running server applications. In ISMM, Vancouver, B.C., Canada, 1998. pdf
 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
 J. Barnes and P. Hut. A hierarchical O(n*log(n)) force-calculation algorithm. Nature, 324:446-449, 1986.
 C. Lever, and D. Boreham. Malloc() Performance in a Multithreaded Linux Environment. In USENIX Annual Technical Conference, Freenix Session. San Diego, CA. Jun. 2000. Available at https://github.com/kuszmaul/SuperMalloc/tree/master/tests
 Timothy Crundal. Reducing Active-False Sharing in TCMalloc. 2016. http://courses.cecs.anu.edu.au/courses/CSPROJECTS/16S1/Reports/Timothy_Crundal_Report.pdf. CS16S1 project at the Australian National University.
 Alexey Kukanov, and Michael J Voss. The Foundations for Scalable Multi-Core Software in Intel Threading Building Blocks. Intel Technology Journal 11 (4). 2007
 Paul Liétar, Theodore Butler, Sylvan Clebsch, Sophia Drossopoulou, Juliana Franco, Matthew J Parkinson, 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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