Heterogeneous Active Messages C++ library
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HAM - Heterogeneous Active Messages

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HAM (Heterogeneous Active Messages) is a research project on efficient active messaging between heterogeneous binary files and architectures. Its original motivation is to efficiently offload computations to accelerators like the Intel Xeon Phi, or nodes of a distributed system in general. Efficiently means with a minimal amount of overhead. An additional goal was to implement this functionality in pure C++ without introducing yet another language or compiler extension.

The current outcome of this project are two libraries:

  • HAM, a Heterogeneous Active Messages C++ library
  • HAM-Offload, an implementation of the offload programming model based on HAM with support for the Xeon Phi

The code originates from different research prototypes and branches whose features were integrated into a coherent code-base that resides in this repository. The source under active development and should be ready to be used in real-world applications. So don't hesitate to give it a try - and don't forget to provide us with feedback, like bug reports, issues, feature requests, or performance results.

There's a paper available, including a case study with a real world application, presented at SC'14:
"A Unified Programming Model for Intra- and Inter-Node Offloading on Xeon Phi Clusters" http://dl.acm.org/citation.cfm?id=2683616

Why should I use HAM or HAM-Offload?

If you are looking for a way to efficiently create and exchange (heterogeneous) active message in a distributed or parallel system using C++, HAM is your friend.

The HAM library allows to create active messages that can be transferred between heterogeneous binary files. Heterogeneous here means, compiled with different options and possibly for different architectures, but usually from the same source. The latter can be stretched to sources that at least share the relevant parts (everything connected to the creation and execution of active messages). Different architectures means not too different with respect to the used C++ ABI. HAM performs efficient and transparent code address translation for active message handlers and provides the hooks to add pre- and post-processing for transferred objects on a per-type basis as needed. Please refer to the documentation (work in progress) and publications for more details. Currently, HAM was successfully tested and used with the x86-64 and k1om (Intel MIC) architectures. Others might already work, or need some additional effort. For the near future, ARM is on the TODO list.

If you want to use the offload programming model for the Xeon Phi, have a look at HAM-Offload.

HAM-Offload uses HAM and combines it with a communication layer to implement the offload programming model. The HAM-Offload API provides similar functionality as vendor provided solutions like Intel LEO (Language Extensions for Offload) or the offload features of OpenMP 4.0. In contrast to these solution, HAM-Offload is pure C++ 11 instead of a language extension based on pragma directives. This means no additional tools or compiler-dependencies are introduced. HAM-Offload supports remote offloading, reverse offloading, and direct data transfers between offload targets, each of which are currently not possible with LEO or OpenMP. In general, arbitrary offload patterns within a distributed system are possible. Any host or accelerator can be the logical host in terms of the offload programming model, and any set of hosts and accelerators can act as offload targets. Due to the drastically reduced offload overhead, fine-grained offloading becomes affordable. Currently, there are two communication back-ends, one using SCIF (intra-node) and one using MPI (intra- and inter-node). Additinal implementations based on GASNet and MPI 3.0's one-sided communication are planned.


A simple example that introduces the HAM-Offload API can be found in src/inner_product_cpp.

Building with Boost.Build

The HAM library is header-only and is located in include/ham/msg.

The different HAM-Offload libraries need to be built with Boost.Build. They also depend on Boost Program Options, which means you need a boost installation. You can either use the packages of your distribution (including the developer packages and Boost.Build), or install Boost from the sources. Let's assume you decide for the latter, since you most likely want Xeon Phi support for which you won't find packages. If you want to use the MPI back-end, you will also need an MPI installation.

Building and Installing Boost

  1. Download Boost (as tar.bz2): http://www.boost.org/users/download/

    $ wget http://downloads.sourceforge.net/project/boost/boost/1.56.0/boost_1_56_0.tar.bz2
  2. Open the tools/install_boost.sh script in an editor of your choice. Read the comments, perform modifications as needed, and run it. This might take some time. If nothing goes wrong, you should end up with a working Boost installation.

  3. Copy the tools/user_config.jam to your home directory (or merge it with your existing one). This file contains some general, system specific configurations for Boost.Build. The example defines two new build variants debug_mic and release_mic, in addition to the debug, and release variant that Boost.Build offers by default. This allows to easily compile Xeon Phi binaries via Boost.Build and is used for HAM-Offload.

Building HAM-Offload

  1. Checkout the GitHub repository:

    $ git clone https://github.com/noma/ham.git
  2. Build everything:

    $ b2 toolset=intel -j8

    If nothing went wrong, the libraries are somewhere in bin, where Boost.Build created sub-folders representing the used toolsets, variants and build options, e.g.
    For each back-end, there are two libraries. The one with the _explicit suffix is intended for library authors, who need to explicitly initialise and finalise HAM-Offload.

libham_offload_scif.so libham_offload_scif_explicit.so libham_offload_mpi.so libham_offload_mpi_explicit.so ``` 4. You can now link or copy the libraries and headers either to your system or project, or just use them where they are (in which case you have to add their path to LD_LIBRARY_PATH environment variable).

Building HAM-Offload Applications

  • Benchmarks and examples can be found in src. They can be built with Boost.Build, e.g.
$ b2 toolset=intel variant=release inner_product_scif
  • Your own applications can, of course, be built with any build system you prefer. All you need are the headers include/ham, and at least on of the libraries. You can call b2 with -a -n to see the generated compiler and linker commands. This should give you an impression of what you need for building.
$ b2 toolset=intel variant=release inner_product_scif -a -n
$ b2 toolset=intel variant=release inner_product_mpi -a -n

Running HAM-Offload Applications

How you start your applications depends on the back-end you use. In case of MPI, mpirun will do the trick. In case of SCIF, the job's processes need to be started manually. There will be a hamrun tool in the near future, that unifies and simplifies this task. For the time being, here is how you start your jobs. This documentation assumes that you have a shared home across your system including the Xeon Phis.


Simply use mpirun as for any other MPI job. Rank 0 will be the process that is your host, all other processes perform offloaded tasks. The node-ids inside the HAM-Offload API directly map to the MPI ranks. Here are generic examples:

# start two processes
$ mpirun -n 2 <application_binary>
# this is the syntax for starting jobs with different binaries
$ mpirun -host localhost -n 1 <application_binary> : -host mic0 -n 1 <application_binary_mic>

The inner_product example can be run using one host and one Xeon Phi process like this

$ mpirun -n 1 -host localhost bin/intel-linux/release/inlining-on/threading-multi/inner_product_mpi : -n 1 -host mic0 bin/intel-linux/release_mic/inlining-on/threading-multi/inner_product_mpi


With SCIF, you have to start the host process (either on a Xeon Phi, or on a host) first, followed by the other processes that are offload targets. During initialisation all the offload targets connect to the host in a server-client like fashion. There is a set of options that can be passed either via the environment (HAM_OPTIONS) or via the command-line (if it does not interfere with the application code). These are the options:

Option Description
--ham-cpu-affinity arg sets the CPU affinity of the process to arg (no affinity is set if omitted)
--ham-process-count arg sets the number of processes of your application
--ham-address arg sets an individual address for each process (between 0 and ham-process-count - 1)
--ham-host-address optional: use a host address other than 0
--ham-host-node optional: specifies the SCIF node (physical host or MIC) where the host process is spawned on (physical host node is always 0 = default)

Example (host to Xeon Phi offload):

# Use different terminals or start jobs in background (by appending &)
# host process 
$ <application_binary> --ham-process-count 2 --ham-address 0 
# MIC process
$ ssh mic0 `pwd`<application_binary_mic> --ham-process-count 2 --ham-address 1

The same Example using the environment to pass the options:

# host process 
$ HAM_OPTIONS="--ham-process-count 2 --ham-address 0" <application_binary> 
# MIC process
$ ssh mic0 env HAM_OPTIONS="--ham-process-count 2 --ham-address 1" `pwd`<application_binary_mic>

Example (reverse offload):
The MIC process is the logical host (address 0, and started first). The host's node address of the SCIF network must be explicitly specified this time.
MIC process:

# MIC process
$ ssh mic0 `pwd`<application_binary_mic> --ham-process-count 2 --ham-address 0 --ham-host-node 1
# host process:
$ <application_binary> --ham-process-count 2 --ham-address 0 --ham-host-node 1

The inner_product example can be started like this. The env LD_LIBRARY_PATH=$MIC_LD_LIBRARY_PATH may or may not be necessary depending on your configuration.

$ bin/intel-linux/release/inlining-on/threading-multi/inner_product_scif --ham-process-count 2 --ham-address 0 &
$ ssh mic0 env LD_LIBRARY_PATH=$MIC_LD_LIBRARY_PATH `pwd`/bin/intel-linux/release_mic/inlining-on/threading-multi/inner_product_scif --ham-process-count 2 --ham-address 1


See the wiki page Benchmarking.


This work is supported by the ZIB (Zuse Institute Berlin) and the IPCC (Intel Parallel Computing Center) activities there.

The architecture of the system was influenced by my earlier work with and on the TACO framework.