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https://github.com/microsoft/mimalloc

mimalloc is a compact general purpose allocator with excellent performance.
https://github.com/microsoft/mimalloc

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mimalloc is a compact general purpose allocator with excellent performance.

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[](https://dev.azure.com/Daan0324/mimalloc/_build?definitionId=1&_a=summary)



# mimalloc



 



mimalloc (pronounced "me-malloc")

is a general purpose allocator with excellent [performance](#performance) characteristics.

Initially developed by Daan Leijen for the runtime systems of the

[Koka](https://koka-lang.github.io) and [Lean](https://github.com/leanprover/lean) languages.



Latest release tag: `v2.1.2` (2023-04-24).

Latest stable  tag: `v1.8.2` (2023-04-24).



mimalloc 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/lib/libmimalloc.so  myprogram

```

It also includes a robust way to override the default allocator in [Windows](#override_on_windows). Notable aspects of the design include:



- __small and consistent__: the library is about 8k 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).

  Partly due to its simplicity, mimalloc has been ported to many systems (Windows, macOS,

  Linux, WASM, various BSD's, Haiku, MUSL, etc) and has excellent support for dynamic overriding.

- __free list sharding__: instead of one big free list (per size class) we have

  many smaller lists per "mimalloc page" which reduces fragmentation and

  increases locality --

  things that are allocated close in time get allocated close in memory.

  (A mimalloc page contains blocks of one size class and is usually 64KiB on a 64-bit system).

- __free list multi-sharding__: the big idea! Not only do we shard the free list

  per mimalloc page, but for each page we have multiple free lists. In particular, there

  is one list for thread-local `free` operations, and another one for concurrent `free`

  operations. Free-ing from another thread can now be a single CAS without needing

  sophisticated coordination between threads. Since there will be

  thousands of separate free lists, contention is naturally distributed over the heap,

  and the chance of contending on a single location will be low -- this is quite

  similar to randomized algorithms like skip lists where adding

  a random oracle removes the need for a more complex algorithm.

- __eager page purging__: when a "page" becomes empty (with increased chance

  due to free list sharding) the memory is marked to the OS as unused (reset or decommitted)

  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 usually around 10% 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_) (upto OS primitives), bounded space overhead (~0.2% meta-data, with low

  internal fragmentation), and has no internal points of contention using only atomic operations.

- __fast__: In our benchmarks (see [below](#performance)),

  _mimalloc_ outperforms other leading allocators (_jemalloc_, _tcmalloc_, _Hoard_, etc),

  and often uses less memory. A nice property is that it does consistently well over a wide range

  of benchmarks. There is also good huge OS page support for larger server programs.



The [documentation](https://microsoft.github.io/mimalloc) gives a full overview of the API.

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!



### Branches



* `master`: latest stable release (based on `dev-slice`).

* `dev`: development branch for mimalloc v1. Use this branch for submitting PR's.

* `dev-slice`: development branch for mimalloc v2. This branch is downstream of `dev`.



### Releases



Note: the `v2.x` version has a new algorithm for managing internal mimalloc pages that tends to reduce memory usage

  and fragmentation compared to mimalloc `v1.x` (especially for large workloads). Should otherwise have similar performance

  (see [below](#performance)); please report if you observe any significant performance regression.



* 2023-04-24, `v1.8.2`, `v2.1.2`: Fixes build issues on freeBSD, musl, and C17 (UE 5.1.1). Reduce code size/complexity 

  by removing regions and segment-cache's and only use arenas with improved memory purging -- this may improve memory

  usage as well for larger services. Renamed options for consistency. Improved Valgrind and ASAN checking.

  

* 2023-04-03, `v1.8.1`, `v2.1.1`: Fixes build issues on some platforms.



* 2023-03-29, `v1.8.0`, `v2.1.0`: Improved support dynamic overriding on Windows 11. Improved tracing precision

  with [asan](#asan) and [Valgrind](#valgrind), and added Windows event tracing [ETW](#ETW) (contributed by Xinglong He). Created an OS

  abstraction layer to make it easier to port and separate platform dependent code (in `src/prim`). Fixed C++ STL compilation on older Microsoft C++ compilers, and various small bug fixes.



* 2022-12-23, `v1.7.9`, `v2.0.9`: Supports building with [asan](#asan) and improved [Valgrind](#valgrind) support.

  Support abitrary large alignments (in particular for `std::pmr` pools). 

  Added C++ STL allocators attached to a specific heap (thanks @vmarkovtsev). 

  Heap walks now visit all object (including huge objects). Support Windows nano server containers (by Johannes Schindelin,@dscho). Various small bug fixes.



* 2022-11-03, `v1.7.7`, `v2.0.7`: Initial support for [Valgrind](#valgrind) for leak testing and heap block overflow

  detection. Initial

  support for attaching heaps to a speficic memory area (only in v2). Fix `realloc` behavior for zero size blocks, remove restriction to integral multiple of the alignment in `alloc_align`, improved aligned allocation performance, reduced contention with many threads on few processors (thank you @dposluns!), vs2022 support, support `pkg-config`, .



* 2022-04-14, `v1.7.6`, `v2.0.6`: fix fallback path for aligned OS allocation on Windows, improve Windows aligned allocation

  even when compiling with older SDK's, fix dynamic overriding on macOS Monterey, fix MSVC C++ dynamic overriding, fix

  warnings under Clang 14, improve performance if many OS threads are created and destroyed, fix statistics for large object

  allocations, using MIMALLOC_VERBOSE=1 has no maximum on the number of error messages, various small fixes.



* 2022-02-14, `v1.7.5`, `v2.0.5` (alpha): fix malloc override on

  Windows 11, fix compilation with musl, potentially reduced

  committed memory, add `bin/minject` for Windows,

  improved wasm support, faster aligned allocation,

  various small fixes.



* [Older release notes](#older-release-notes)



Special thanks to:



* [David Carlier](https://devnexen.blogspot.com/) (@devnexen) for his many contributions, and making

  mimalloc work better on many less common operating systems, like Haiku, Dragonfly, etc.

* Mary Feofanova (@mary3000), Evgeniy Moiseenko, and Manuel Pöter (@mpoeter) for making mimalloc TSAN checkable, and finding

  memory model bugs using the [genMC] model checker.

* Weipeng Liu (@pongba), Zhuowei Li, Junhua Wang, and Jakub Szymanski, for their early support of mimalloc and deployment

  at large scale services, leading to many improvements in the mimalloc algorithms for large workloads.

* Jason Gibson (@jasongibson) for exhaustive testing on large scale workloads and server environments, and finding complex bugs

  in (early versions of) `mimalloc`.

* Manuel Pöter (@mpoeter) and Sam Gross(@colesbury) for finding an ABA concurrency issue in abandoned segment reclamation. Sam also created the [no GIL](https://github.com/colesbury/nogil) Python fork which

  uses mimalloc internally.



[genMC]: https://plv.mpi-sws.org/genmc/



### Usage



mimalloc is used in various large scale low-latency services and programs, for example:














# Building



## Windows



Open `ide/vs2019/mimalloc.sln` in Visual Studio 2019 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.



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 -DMI_SECURE=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`



## Single source



You can also directly build the single `src/static.c` file as part of your project without

needing `cmake` at all. Make sure to also add the mimalloc `include` directory to the include path.



# 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 1.4 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.



For best performance in C++ programs, it is also recommended to override the

global `new` and `delete` operators. For convience, mimalloc provides

[`mimalloc-new-delete.h`](https://github.com/microsoft/mimalloc/blob/master/include/mimalloc-new-delete.h) which does this for you -- just include it in a single(!) source file in your project.

In C++, mimalloc also provides the `mi_stl_allocator` struct which implements the `std::allocator`

interface.



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 .



## Environment Options



You can set further options either programmatically (using [`mi_option_set`](https://microsoft.github.io/mimalloc/group__options.html)), 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.



Advanced options:



- `MIMALLOC_PURGE_DELAY=N`: the delay in `N` milli-seconds (by default `10`) after which mimalloc will purge 

   OS pages that are not in use. This signals to the OS that the underlying physical memory can be reused which 

   can reduce memory fragmentation especially in long running (server) programs. Setting `N` to `0` purges immediately when

   a page becomes unused which can improve memory usage but also decreases performance. Setting `N` to a higher

   value like `100` can improve performance (sometimes by a lot) at the cost of potentially using more memory at times.

   Setting it to `-1` disables purging completely.   

- `MIMALLOC_ARENA_EAGER_COMMIT=1`: turns on eager commit for the large arenas (usually 1GiB) from which mimalloc 

   allocates segments and pages. This is by default 

   only enabled on overcommit systems (e.g. Linux) but enabling it explicitly on other systems (like Windows or macOS)

   may improve performance. Note that eager commit only increases the commit but not the actual the peak resident set 

   (rss) so it is generally ok to enable this.



Further options for large workloads and services:



- `MIMALLOC_USE_NUMA_NODES=N`: pretend there are at most `N` NUMA nodes. If not set, the actual NUMA nodes are detected

   at runtime. Setting `N` to 1 may avoid problems in some virtual environments. Also, setting it to a lower number than

   the actual NUMA nodes is fine and will only cause threads to potentially allocate more memory across actual NUMA

   nodes (but this can happen in any case as NUMA local allocation is always a best effort but not guaranteed).

- `MIMALLOC_ALLOW_LARGE_OS_PAGES=1`: use large OS pages (2MiB) when available; for some workloads this can significantly

   improve performance. Use `MIMALLOC_VERBOSE` to check if the large OS pages are enabled -- usually one needs

   to explicitly allow large OS pages (as on [Windows][windows-huge] and [Linux][linux-huge]). 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 (for that reason, we generally recommend to use `MIMALLOC_RESERVE_HUGE_OS_PAGES` instead whenever possible).   

- `MIMALLOC_RESERVE_HUGE_OS_PAGES=N`: where `N` is the number of 1GiB _huge_ OS pages. This reserves the huge pages at

   startup and sometimes this can give a large (latency) performance improvement on big workloads.

   Usually it is better to not use `MIMALLOC_ALLOW_LARGE_OS_PAGES=1` in 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 (but reserving the huge pages is done at

   startup only once).

   Note that we usually need to explicitly enable huge OS pages (as on [Windows][windows-huge] and [Linux][linux-huge])).

   With huge OS pages, it may be beneficial to set the setting

   `MIMALLOC_EAGER_COMMIT_DELAY=N` (`N` is 1 by default) to delay the initial `N` segments (of 4MiB)

   of a thread to not allocate in the huge OS pages; this prevents threads that are short lived

   and allocate just a little to take up space in the huge OS page area (which cannot be purged).

   The huge pages are usually allocated evenly among NUMA nodes.

   We can use `MIMALLOC_RESERVE_HUGE_OS_PAGES_AT=N` where `N` is the numa node (starting at 0) to allocate all

   the huge pages at a specific numa node instead.



Use caution when using `fork` in combination with either large or huge OS pages: on a fork, the OS uses copy-on-write

for all pages in the original process including the huge OS pages. When any memory is now written in that area, the

OS will copy the entire 1GiB huge page (or 2MiB large page) which can cause the memory usage to grow in large increments.



[linux-huge]: https://access.redhat.com/documentation/en-us/red_hat_enterprise_linux/5/html/tuning_and_optimizing_red_hat_enterprise_linux_for_oracle_9i_and_10g_databases/sect-oracle_9i_and_10g_tuning_guide-large_memory_optimization_big_pages_and_huge_pages-configuring_huge_pages_in_red_hat_enterprise_linux_4_or_5

[windows-huge]: https://docs.microsoft.com/en-us/sql/database-engine/configure-windows/enable-the-lock-pages-in-memory-option-windows?view=sql-server-2017



## Secure Mode



_mimalloc_ can be build in secure mode by using the `-DMI_SECURE=ON` flags in `cmake`. This build enables various mitigations

to make mimalloc more robust against exploits. In particular:



- All internal mimalloc pages are surrounded by guard pages and the heap metadata is behind a guard page as well (so a buffer overflow

  exploit cannot reach into the metadata).

- All free list pointers are

  [encoded](https://github.com/microsoft/mimalloc/blob/783e3377f79ee82af43a0793910a9f2d01ac7863/include/mimalloc-internal.h#L396)

  with per-page keys which is used both to prevent overwrites with a known pointer, as well as to detect heap corruption.

- Double free's are detected (and ignored).

- The free lists are initialized in a random order and allocation randomly chooses between extension and reuse within a page to

  mitigate against attacks that rely on a predicable allocation order. Similarly, the larger heap blocks allocated by mimalloc

  from the OS are also address randomized.



As always, evaluate with care as part of an overall security strategy as all of the above are mitigations but not guarantees.



## Debug Mode



When _mimalloc_ is built using debug mode, various checks are done at runtime to catch development errors.



- Statistics are maintained in detail for each object size. They can be shown using `MIMALLOC_SHOW_STATS=1` at runtime.

- All objects have padding at the end to detect (byte precise) heap block overflows.

- Double free's, and freeing invalid heap pointers are detected.

- Corrupted free-lists and some forms of use-after-free are detected.



# Overriding Standard Malloc



Overriding the standard `malloc` (and `new`) can be done either _dynamically_ or _statically_.



## Dynamic override



This is the recommended way to override the standard malloc interface.



### Dynamic Override on Linux, BSD



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

```



### Dynamic Override on MacOS



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_INSERT_LIBRARIES=/usr/lib/libmimalloc.dylib myprogram

```



Note that 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).



### Dynamic Override on Windows



Overriding on Windows is robust and has the

particular advantage to be able to redirect all malloc/free calls that go through

the (dynamic) C runtime allocator, including those from other DLL's or libraries.



The overriding on Windows requires that you link your program explicitly with

the mimalloc DLL and use the C-runtime library as a DLL (using the `/MD` or `/MDd` switch).

Also, the `mimalloc-redirect.dll` (or `mimalloc-redirect32.dll`) must be put

in the same folder as the main `mimalloc-override.dll` at runtime (as it is a dependency).

The redirection DLL ensures that all calls to the C runtime malloc API get redirected to

mimalloc (in `mimalloc-override.dll`).



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. For best performance on Windows with C++, it

is also recommended to also override the `new`/`delete` operations (by including

[`mimalloc-new-delete.h`](https://github.com/microsoft/mimalloc/blob/master/include/mimalloc-new-delete.h) a single(!) source file in your project).



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 was successfully redirected.



(Note: in principle, it is possible to even patch existing executables without any recompilation

if they are linked with the dynamic C runtime (`ucrtbase.dll`) -- just put the `mimalloc-override.dll`

into the import table (and put `mimalloc-redirect.dll` in the same folder)

Such patching can be done for example with [CFF Explorer](https://ntcore.com/?page_id=388)).



## Static override



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.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.o  myfile1.c ...

```



Another way to override statically that works on all platforms, is to

link statically to mimalloc (as shown in the introduction) and include a

header file in each source file that re-defines `malloc` etc. to `mi_malloc`.

This is provided by [`mimalloc-override.h`](https://github.com/microsoft/mimalloc/blob/master/include/mimalloc-override.h). This only works reliably though if all sources are

under your control or otherwise mixing of pointers from different heaps may occur!



## Tools



Generally, we recommend using the standard allocator with memory tracking tools, but mimalloc

can also be build to support the [address sanitizer][asan] or the excellent [Valgrind] tool. 

Moreover, it can be build to support Windows event tracing ([ETW]).

This has a small performance overhead but does allow detecting memory leaks and byte-precise 

buffer overflows directly on final executables. See also the `test/test-wrong.c` file to test with various tools.



### Valgrind



To build with [valgrind] support, use the `MI_TRACK_VALGRIND=ON` cmake option:



```

> cmake ../.. -DMI_TRACK_VALGRIND=ON

```



This can also be combined with secure mode or debug mode.

You can then run your programs directly under valgrind:



```

> valgrind 

```



If you rely on overriding `malloc`/`free` by mimalloc (instead of using the `mi_malloc`/`mi_free` API directly),

you also need to tell `valgrind` to not intercept those calls itself, and use:



```

> MIMALLOC_SHOW_STATS=1 valgrind  --soname-synonyms=somalloc=*mimalloc* -- 

```



By setting the `MIMALLOC_SHOW_STATS` environment variable you can check that mimalloc is indeed

used and not the standard allocator. Even though the [Valgrind option][valgrind-soname]

is called `--soname-synonyms`, this also

works when overriding with a static library or object file. Unfortunately, it is not possible to

dynamically override mimalloc using `LD_PRELOAD` together with `valgrind`.

See also the `test/test-wrong.c` file to test with `valgrind`.



Valgrind support is in its initial development -- please report any issues.



[Valgrind]: https://valgrind.org/

[valgrind-soname]: https://valgrind.org/docs/manual/manual-core.html#opt.soname-synonyms



### ASAN



To build with the address sanitizer, use the `-DMI_TRACK_ASAN=ON` cmake option:



```

> cmake ../.. -DMI_TRACK_ASAN=ON

```



This can also be combined with secure mode or debug mode. 

You can then run your programs as:'



```

> ASAN_OPTIONS=verbosity=1 

```



When you link a program with an address sanitizer build of mimalloc, you should

generally compile that program too with the address sanitizer enabled. 

For example, assuming you build mimalloc in `out/debug`:



```

clang -g -o test-wrong -Iinclude test/test-wrong.c out/debug/libmimalloc-asan-debug.a -lpthread -fsanitize=address -fsanitize-recover=address

```



Since the address sanitizer redirects the standard allocation functions, on some platforms (macOSX for example)

it is required to compile mimalloc with `-DMI_OVERRIDE=OFF`.

Adress sanitizer support is in its initial development -- please report any issues.



[asan]: https://github.com/google/sanitizers/wiki/AddressSanitizer



### ETW



Event tracing for Windows ([ETW]) provides a high performance way to capture all allocations though

mimalloc and analyze them later. To build with ETW support, use the `-DMI_TRACK_ETW=ON` cmake option. 



You can then capture an allocation trace using the Windows performance recorder (WPR), using the 

`src/prim/windows/etw-mimalloc.wprp` profile. In an admin prompt, you can use:

```

> wpr -start src\prim\windows\etw-mimalloc.wprp -filemode

> 

> wpr -stop .etl

``` 

and then open `.etl` in the Windows Performance Analyzer (WPA), or 

use a tool like [TraceControl] that is specialized for analyzing mimalloc traces.



[ETW]: https://learn.microsoft.com/en-us/windows-hardware/test/wpt/event-tracing-for-windows

[TraceControl]: https://github.com/xinglonghe/TraceControl



# Performance



Last update: 2021-01-30



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 benchmark suite, _mimalloc_ outperforms other leading

allocators (_jemalloc_, _tcmalloc_, _Hoard_, etc), and has a similar memory footprint. A nice property is that it

does consistently well over the wide range of benchmarks.



General memory allocators are interesting as there exists no algorithm that is

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 (too much) underperformance in less common situations.



As always, interpret these results with care since some benchmarks test synthetic

or uncommon situations that may never apply to your workloads. For example, most

allocators do not do well on `xmalloc-testN` but that includes even the best

industrial allocators like _jemalloc_ and _tcmalloc_ that are used in some of

the world's largest systems (like Chrome or FreeBSD).



Also, the benchmarks here do not measure the behaviour on very large and long-running server workloads,

or worst-case latencies of allocation. Much work has gone into `mimalloc` to work well on such

workloads (for example, to reduce virtual memory fragmentation on long-running services)

but such optimizations are not always reflected in the current benchmark suite.



We show here only an overview -- for

more 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 automated and available separately

as [mimalloc-bench](https://github.com/daanx/mimalloc-bench).



## Benchmark Results on a 16-core AMD 5950x (Zen3)



Testing on the 16-core AMD 5950x processor at 3.4Ghz (4.9Ghz boost), with

with 32GiB memory at 3600Mhz, running	Ubuntu 20.04 with glibc 2.31 and GCC 9.3.0.



We measure three versions of _mimalloc_: the main version `mi` (tag:v1.7.0),

the new v2.0 beta version as `xmi` (tag:v2.0.0), and the main version in secure mode as `smi` (tag:v1.7.0).



The other allocators are

Google's [_tcmalloc_](https://github.com/gperftools/gperftools) (`tc`, tag:gperftools-2.8.1) used in Chrome,

Facebook's [_jemalloc_](https://github.com/jemalloc/jemalloc) (`je`, tag:5.2.1) by Jason Evans used in Firefox and FreeBSD,

the Intel thread building blocks [allocator](https://github.com/intel/tbb) (`tbb`, tag:v2020.3),

[rpmalloc](https://github.com/mjansson/rpmalloc) (`rp`,tag:1.4.1) by Mattias Jansson,

the original scalable [_Hoard_](https://github.com/emeryberger/Hoard) (git:d880f72) allocator by Emery Berger \[1],

the memory compacting [_Mesh_](https://github.com/plasma-umass/Mesh) (git:67ff31a) allocator by

Bobby Powers _et al_ \[8],

and finally the default system allocator (`glibc`, 2.31) (based on _PtMalloc2_).








Any benchmarks ending in `N` run on all 32 logical cores in parallel.

Results are averaged over 10 runs and reported relative

to mimalloc (where 1.2 means it took 1.2× longer to run).

The legend also contains the _overall relative score_ between the

allocators where 100 points is the maximum if an allocator is fastest on

all benchmarks.



The single threaded _cfrac_ benchmark by Dave Barrett is an implementation of

continued fraction factorization which uses many small short-lived allocations.

All allocators do well on such common usage, where _mimalloc_ is just a tad

faster than _tcmalloc_ and

_jemalloc_.



The _leanN_ program is 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 13%

speedup over _tcmalloc_. This is

quite significant: if Lean spends 20% of its time in the

allocator that means that _mimalloc_ is 1.6× 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 single threaded _redis_ benchmark again show that most allocators do well on such workloads.



The _larsonN_ server benchmark by Larson and Krishnan \[2] allocates and frees between threads. They observed this

behavior (which they call _bleeding_) in actual server applications, and the benchmark simulates this.

Here, _mimalloc_ is quite a bit faster than _tcmalloc_ and _jemalloc_ probably due to the object migration between different threads.



The _mstressN_ workload performs many allocations and re-allocations,

and migrates objects between threads (as in _larsonN_). However, it also

creates  and destroys the _N_ worker threads a few times keeping some objects

alive beyond the life time of the allocating thread. We observed this

behavior in many larger server applications.



The [_rptestN_](https://github.com/mjansson/rpmalloc-benchmark) benchmark

by Mattias Jansson is a allocator test originally designed

for _rpmalloc_, and tries to simulate realistic allocation patterns over

multiple threads. Here the differences between allocators become more apparent.



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.



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 2.5× faster than _jemalloc_).

We cannot explain this well but believe it is

caused in part by the "reverse" free-ing pattern in _sh6bench_.

The _sh8bench_ is a variation with object migration

between threads; whereas _tcmalloc_ did well on _sh6bench_, the addition of object migration causes it to be 10× 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 -- they observed this pattern in

larger server applications. Here we see that

the _mimalloc_ technique of having non-contended sharded thread free

lists pays off as it outperforms others by a very large margin. Only _rpmalloc_, _tbb_, and _glibc_ also scale well on this benchmark.



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 can cause large run-time differences.

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 the _tbb_, _rpmalloc_ and _mesh_ allocators also avoid the

cache line sharing completely, while _Hoard_ and _glibc_ seem to mitigate

the effects. Kukanov and Voss \[7] describe in detail

how the design of _tbb_ avoids the false cache line sharing.



## On a 36-core Intel Xeon



For completeness, here are the results on a big Amazon

[c5.18xlarge](https://aws.amazon.com/ec2/instance-types/#Compute_Optimized) instance

consisting of a 2×18-core Intel Xeon (Cascade Lake) at 3.4GHz (boost 3.5GHz)

with 144GiB ECC memory, running	Ubuntu 20.04 with glibc 2.31, GCC 9.3.0, and

Clang 10.0.0. This time, the mimalloc allocators (mi, xmi, and smi) were

compiled with the Clang compiler instead of GCC.

The results are similar to the AMD results but it is interesting to

see the differences in the _larsonN_, _mstressN_, and _xmalloc-testN_ benchmarks.








## Peak Working Set



The following figure shows the peak working set (rss) of the allocators

on the benchmarks (on the c5.18xlarge instance).








Note that the _xmalloc-testN_ memory usage should be disregarded as it

allocates more the faster the program runs. Similarly, memory usage of

_larsonN_, _mstressN_, _rptestN_ and _sh8bench_ can vary depending on scheduling and

speed. Nevertheless, we hope to improve the memory usage on _mstressN_

and _rptestN_ (just as _cfrac_, _larsonN_ and _sh8bench_ have a small working set which skews the results).



# 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://citeseer.ist.psu.edu/viewdoc/download?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://citeseer.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. [pdf](http://courses.cecs.anu.edu.au/courses/CSPROJECTS/16S1/Reports/Timothy_Crundal_Report.pdf)



- \[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] Bobby Powers, David Tench, Emery D. Berger, and Andrew McGregor.

 _Mesh: Compacting Memory Management for C/C++_

 In Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI'19), June 2019, pages 333-–346.



# 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.



When you submit a pull request, a CLA-bot will automatically determine whether you need to provide

a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions

provided by the bot. You will only need to do this once across all repos using our CLA.



# Older Release Notes



* 2021-11-14, `v1.7.3`, `v2.0.3` (beta): improved WASM support, improved macOS support and performance (including

  M1), improved performance for v2 for large objects, Python integration improvements, more standard

  installation directories, various small fixes.

* 2021-06-17, `v1.7.2`, `v2.0.2` (beta): support M1, better installation layout on Linux, fix

  thread_id on Android, prefer 2-6TiB area for aligned allocation to work better on pre-windows 8, various small fixes.

* 2021-04-06, `v1.7.1`, `v2.0.1` (beta): fix bug in arena allocation for huge pages, improved aslr on large allocations, initial M1 support (still experimental).

* 2021-01-31, `v2.0.0`: beta release 2.0: new slice algorithm for managing internal mimalloc pages.

* 2021-01-31, `v1.7.0`: stable release 1.7: support explicit user provided memory regions, more precise statistics,

  improve macOS overriding, initial support for Apple M1, improved DragonFly support, faster memcpy on Windows, various small fixes.



* 2020-09-24, `v1.6.7`: stable release 1.6: using standard C atomics, passing tsan testing, improved

  handling of failing to commit on Windows, add [`mi_process_info`](https://github.com/microsoft/mimalloc/blob/master/include/mimalloc.h#L156) api call.

* 2020-08-06, `v1.6.4`: stable release 1.6: improved error recovery in low-memory situations,

  support for IllumOS and Haiku, NUMA support for Vista/XP, improved NUMA detection for AMD Ryzen, ubsan support.

* 2020-05-05, `v1.6.3`: stable release 1.6: improved behavior in out-of-memory situations, improved malloc zones on macOS,

  build PIC static libraries by default, add option to abort on out-of-memory, line buffered statistics.

* 2020-04-20, `v1.6.2`: stable release 1.6: fix compilation on Android, MingW, Raspberry, and Conda,

  stability fix for Windows 7, fix multiple mimalloc instances in one executable, fix `strnlen` overload,

  fix aligned debug padding.

* 2020-02-17, `v1.6.1`: stable release 1.6: minor updates (build with clang-cl, fix alignment issue for small objects).

* 2020-02-09, `v1.6.0`: stable release 1.6: fixed potential memory leak, improved overriding

  and thread local support on FreeBSD, NetBSD, DragonFly, and macOSX. New byte-precise

  heap block overflow detection in debug mode (besides the double-free detection and free-list

  corruption detection). Add `nodiscard` attribute to most allocation functions.

  Enable `MIMALLOC_PAGE_RESET` by default. New reclamation strategy for abandoned heap pages

  for better memory footprint.

* 2020-02-09, `v1.5.0`: stable release 1.5: improved free performance, small bug fixes.

* 2020-01-22, `v1.4.0`: stable release 1.4: improved performance for delayed OS page reset,

more eager concurrent free, addition of STL allocator, fixed potential memory leak.

* 2020-01-15, `v1.3.0`: stable release 1.3: bug fixes, improved randomness and [stronger

free list encoding](https://github.com/microsoft/mimalloc/blob/783e3377f79ee82af43a0793910a9f2d01ac7863/include/mimalloc-internal.h#L396) in secure mode.



* 2019-12-22, `v1.2.2`: stable release 1.2: minor updates.

* 2019-11-22, `v1.2.0`: stable release 1.2: bug fixes, improved secure mode (free list corruption checks, double free mitigation). Improved dynamic overriding on Windows.

* 2019-10-07, `v1.1.0`: stable release 1.1.

* 2019-09-01, `v1.0.8`: pre-release 8: more robust windows dynamic overriding, initial huge page support.

* 2019-08-10, `v1.0.6`: pre-release 6: various performance improvements.