# mimalloc   mimalloc (pronounced "me-malloc") is a general purpose allocator with excellent [performance](#performance) characteristics. Initially developed by Daan Leijen for the run-time systems of the [Koka](https://github.com/koka-lang/koka) and [Lean](https://github.com/leanprover/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 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 build 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_ \[1\], 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 only atomic operations. - __fast__: In our benchmarks (see [below](#performance)), _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. You can read more on the design of _mimalloc_ in the [technical report](https://www.microsoft.com/en-us/research/publication/mimalloc-free-list-sharding-in-action) which also has detailed benchmark results. 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`](https://cmake.org)¹ as the build system: ``` > mkdir -p out/release > cd out/release > cmake ../.. > make ``` 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: ``` > mkdir -p out/debug > cd out/debug > cmake -DCMAKE_BUILD_TYPE=Debug ../.. > make ``` This will name the shared library as `libmimalloc-debug.so`. Finally, you can build a _secure_ version that uses guard pages, encrypted free lists, etc, as: ``` > mkdir -p out/secure > cd out/secure > cmake -DSECURE=ON ../.. > make ``` This will name the shared library as `libmimalloc-secure.so`. Use `ccmake`² instead of `cmake` to see and customize all the available build options. Notes: 1. Install CMake: `sudo apt-get install cmake` 2. Install CCMake: `sudo apt-get install cmake-curses-gui` # Using the library The preferred usage is including ``, 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](https://stackoverflow.com/questions/43941322/dyld-insert-libraries-ignored-when-calling-application-through-bash). 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`). 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 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](https://www.microsoft.com/en-us/research/publication/mimalloc-free-list-sharding-in-action). The benchmark suite is scripted and available separately as [mimalloc-bench](https://github.com/daanx/mimalloc-bench). ## Benchmark Results 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. The measured allocators are _mimalloc_ (mi), Google's [_tcmalloc_](https://github.com/gperftools/gperftools) (tc) used in Chrome, [_jemalloc_](https://github.com/jemalloc/jemalloc) (je) by Jason Evans used in Firefox and FreeBSD, [_snmalloc_](https://github.com/microsoft/snmalloc) (sn) by Liétar et al. \[8], [_rpmalloc_](https://github.com/rampantpixels/rpmalloc) (rp) by Mattias Jansson at Rampant Pixels, [_Hoard_](https://github.com/emeryberger/Hoard) by Emery Berger \[1], the system allocator (glibc) (based on _PtMalloc2_), and the Intel thread building blocks [allocator](https://github.com/intel/tbb) (tbb).   Memory usage:   (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 of the [Lean](https://github.com/leanprover/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 \[2] 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](http://ithare.com/testing-memory-allocators-ptmalloc2-tcmalloc-hoard-jemalloc-while-trying-to-simulate-real-world-loads/), 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](http://www.microquill.com/) 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 \[5] 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 \[1], 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 \[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. # 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. - \[5] 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 - \[6] Timothy Crundal. _Reducing Active-False Sharing in TCMalloc._ 2016. . CS16S1 project at the Australian National University. - \[7] Alexey Kukanov, and Michael J Voss. _The Foundations for Scalable Multi-Core Software in Intel Threading Building Blocks._ Intel Technology Journal 11 (4). 2007 - \[8] 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. # Contributing This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.microsoft.com. 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