Copyright (C) 1998-2020 by Emery Berger
The Hoard memory allocator is a fast, scalable, and memory-efficient memory allocator that works on a range of platforms, including Linux, Mac OS X, and Windows.
Hoard is a drop-in replacement for malloc that can dramatically improve application performance, especially for multithreaded programs running on multiprocessors and multicore CPUs. No source code changes necessary: just link it in or set one environment variable (see Building Hoard, below).
"If you'll be running on multiprocessor machines, ... use Emery Berger's excellent Hoard multiprocessor memory management code. It's a drop-in replacement for the C and C++ memory routines and is very fast on multiprocessor machines."
"(To improve scalability), consider an open source alternative such as the Hoard Memory Manager..."
"Hoard dramatically improves program performance through its more efficient use of memory. Moreover, Hoard has provably bounded memory blowup and low synchronization costs."
Companies using Hoard in their products and servers include AOL, British Telecom, Blue Vector, Business Objects (formerly Crystal Decisions), Cisco, Credit Suisse, Entrust, InfoVista, Kamakura, Novell, Oktal SE, OpenText, OpenWave Systems (for their Typhoon and Twister servers), Pervasive Software, Plath GmbH, Quest Software, Reuters, Royal Bank of Canada, SAP, Sonus Networks, Tata Communications, and Verite Group.
Open source projects using Hoard include the Asterisk Open Source Telephony Project, Bayonne GNU telephony server, the Cilk parallel programming language, the GNU Common C++ system, the OpenFOAM computational fluid dynamics toolkit, and the SafeSquid web proxy.
Hoard is now a standard compiler option for the Standard Performance Evaluation Corporation's CPU2006 benchmark suite for the Intel and Open64 compilers.
Hoard has now been released under the widely-used and permissive Apache license, version 2.0.
There are a number of problems with existing memory allocators that make Hoard a better choice.
Multithreaded programs often do not scale because the heap is a bottleneck. When multiple threads simultaneously allocate or deallocate memory from the allocator, the allocator will serialize them. Programs making intensive use of the allocator actually slow down as the number of processors increases. Your program may be allocation-intensive without you realizing it, for instance, if your program makes many calls to the C++ Standard Template Library (STL). Hoard eliminates this bottleneck.
System-provided memory allocators can cause insidious problems for multithreaded code. They can lead to a phenomenon known as "false sharing": threads on different CPUs can end up with memory in the same cache line, or chunk of memory. Accessing these falsely-shared cache lines is hundreds of times slower than accessing unshared cache lines. Hoard is designed to prevent false sharing.
Multithreaded programs can also lead the allocator to blowup memory consumption. This effect can multiply the amount of memory needed to run your application by the number of CPUs on your machine: four CPUs could mean that you need four times as much memory. Hoard is guaranteed (provably!) to bound memory consumption.
Homebrew (Mac OS X)
You can use Homebrew to install the current version of Hoard as follows:
brew tap emeryberger/hoard brew install --HEAD emeryberger/hoard/libhoard
This not only installs the Hoard library, but also creates a
hoard command you can use to run Hoard with anything at the command-line.
Building Hoard from source (Mac OS X, Linux, and Windows WSL2)
To build Hoard from source, do the following:
git clone https://github.com/emeryberger/Hoard cd src make
You can then use Hoard by linking it with your executable, or
by setting the
LD_PRELOAD environment variable, as in
or, in Mac OS X:
Building Hoard (Windows)
Change into the
src directory and build the Windows version:
To use Hoard, link your executable with
You must use the
C:\hoard\src> cl /Ox /MD yourapp.cpp source\uselibhoard.cpp libhoard.lib
yourapp.exe, you will need to have
libhoard.dll in your path.
benchmarks/ contains a number of benchmarks used to
evaluate and tune Hoard.
Hoard has changed quite a bit over the years, but for technical details of the first version of Hoard, read Hoard: A Scalable Memory Allocator for Multithreaded Applications, by Emery D. Berger, Kathryn S. McKinley, Robert D. Blumofe, and Paul R. Wilson. The Ninth International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS-IX). Cambridge, MA, November 2000.