We completely rewrote the allocator from scratch. Please use the current version to obtain benchmarking numbers for scalloc. See Benchmarking.
A Fast, Multicore-Scalable, Low-Fragmentation Memory Allocator
The problem of concurrent memory allocation is to find the right balance between temporal and spatial performance and scalability across a large range of workloads. Our contributions to address this problem are: uniform treatment of small and big objects through the idea of virtual spans, efficiently and effectively reclaiming unused memory through fast and scalable global data structures, and constant-time (modulo synchronization) allocation and deallocation operations that trade off memory reuse and spatial locality without being subject to false sharing. We have implemented an allocator, scalloc, based on these ideas that generally performs and scales in our experiments better than other allocators while using less memory and is still competitive otherwise.
While the code provided in this repository is complete in the sence that it supports the full API, it may still require tuning for a specific production environment (read: your environment).
We do not explicitly provide pre-built binaries. However, binaries for Linux (x86_64) can be found in the release section of this repository.
Building from source
We support building on OS X (>10.6) and Linux-based systems using gyp. The only
requirement to actually get started is a working
Setting up a build environment
Checkout the latest version
git clone https://github.com/cksystemsgroup/scalloc.git cd scalloc
If you don't have a global gyp installation, you can get a local one using
Generate a build environment (using the gyp installation from the previous step)
build/gyp/gyp --depth=. scalloc.gyp
Additionally, scalloc provides some compile-time configuration flags:
- log_level: Log level that is used through the allocator. [default: kWarning]
- reuse_threshold: Utilization of spans that should be revived before they actually get empty (i.e. all objects have been returned). A threshold of 100 corresponds to disabling this feature at compile time. [default: 80]
Flags may be set when creating the build files using
gyp by passing them as flags, i.e.,
-Dflag=value. For example,
We support the following build configurations:
- Debug: Binaries are created with debugging symbols and without optimizations. We also include assertions checking for various invariants.
- Release: Binaries are created with maximum optimization levels, no debugging symbols, and without assertions.
... on Linux
After setting up the build environment building scalloc is as easy as
BUILDTYPE=Debug make # default BUILDTYPE=Release make
... on OSX
scalloc.xcodeproj and build the project using Xcode, or build it from the command
build/gyp/gyp --depth=. scalloc.gyp --build=Release
... on Linux
In order to make use of scalloc, preload it using
scalloc heavily makes use of 64bit address space. If you run into mmap limits you need to disable overcommit accounting. Additionally, make sure that transparent huge pages are disabled. On recent versions of Linux you can do this by
sudo sh -c "echo 1 > /proc/sys/vm/overcommit_memory" sudo sh -c "echo never > /sys/kernel/mm/transparent_hugepage/enabled"
See the kernel docs on overcommit accounting and transparent hugepages for more information.
... on OSX
Similar to preloading on Linux, one can preload scalloc using
DYLD_INSERT_LIBRARIES=/path/to/libscalloc.dylib DYLD_FORCE_FLAT_NAMESPACE=1 ./foo
See cksystemsgroup/scalloc-artifact for setting up a benchmarking environment to compare scalloc against other allocators.