mimalloc/readme.md

mi-malloc

 

mi-malloc (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 cmake¹ 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 ccmake² 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 <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 commit 94f6cb (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 in sh6bench). 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).

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

• [2] P. Larson and M. Krishnan. Memory allocation for long-running server applications. In ISMM, Vancouver, B.C., Canada, 1998. 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

• [4] J. Barnes and P. Hut. A hierarchical O(n*log(n)) force-calculation algorithm. Nature, 324:446-449, 1986.