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Parallel processing#

In most passmethods, particles are independent. So multi-particle tracking is well adapted for parallel processing. But a few elements, like impedance elements, need the knowledge of the whole particle distribution. These are subclasses of the Collective class. Depending on the multiprocessing option chosen, they must be excluded from parallel tracking or be protected by a fence.

PyAT has several options for parallel computing. Though nothing wrong should happen when simultaneously activating several options, it is usually advised not to do so.

Python multiprocessing package#

Some PyAT functions are based on the standard Python multiprocessing package.

  • Always available, on all platforms, with the default installation,

  • Must be explicitly called, either by calling patpass() instead of lattice_pass() for tracking, or by specifying a use_mp boolean keyword argument in some functions (see get_acceptance() for example).

  • Collective element classes are not allowed.

OpenMP#

PyAT C integrators can be compiled with OpenMP. Parallelism is activated inside each integrator, when possible.

  • Available on all platforms with a special installation procedure,

  • Fully transparent once installed: OpenMP is automatically activated when tracking more than OMP_PARTICLE_THRESHOLD particles (10 by default). As a consequence, many internal PyAT functions (closed orbit, transfer matrices…) benefit from parallelism,

  • No restriction on element classes,

  • Uses shared-memory multiprocessing: small overhead, but limited to single-node multicore machines,

Installation#

OpenMP is integrated in most modern compilers and does not require any preliminary installation, except for macOS:

macOS preliminary OpenMP installation

While the clang compiler integrates OpenMP, libomp.dylib must be installed with your favorite package manager, Homebrew or MacPorts:

Use $ brew install libomp
or $ sudo port install libomp

From the PyPI repository, you must install from source (disable the binary distribution):

pip install --no-binary accelerator-toolbox --config-settings openmp=1 accelerator-toolbox

From a local clone: when switching from a standard install to an OpenMP one or vice-versa, remove the build directory to force a re-compilation:

cd <at>
rm -rf build
pip install --config-settings openmp=1 .

Configuration#

The minimum number of particles triggering parallelism can be configured at compile time either by setting a OMP_PARTICLE_THRESHOLD environment variable or by specifying the omp_particle_threshold configuration option:

OPENMP=1 OMP_PARTICLE_THRESHOLD=4 pip install . # or
pip install --config-settings openmp=1 --config-settings omp_particle_threshold=4 .

The number of threads is by default automatically selected by OpenMP. It can be forced to any value with the omp_num_threads keyword argument of lattice_pass()

MPI#

PyAT can be installed with MPI compatibility.

  • Available on all platforms with a special installation procedure, but mostly interesting on computer clusters,

  • Message-passing interface implies a significant overhead for data transfer, but allows large scale and heterogeneous computer infrastructures,

  • No restriction on element classes,

  • The whole python session needs to be started with mpirun: better suited for massive multi-particle batch jobs than for interactive sessions,

Installation#

MPI must be preliminary installed on the computer.

From the PyPI repository, you must install from source (disable the binary distribution):

pip install --no-binary accelerator-toolbox --config-settings mpi=1 "accelerator-toolbox[mpi]"

Installing PyAT from a local clone: when switching from a standard install to a MPI one or vice-versa, remove the build directory to force a re-compilation:

cd <at>
rm -rf build
pip install --config-settings mpi=1 ".[mpi]"
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