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Add gap junctions and improve general model overview in docs. * Replace Arbor Model::Common Types with Arbor Model::Concepts, that introduces the concept of cells, connections and gap junctions. * Add gap junction documentation to cpp:recipe * Document the implicit rule forcing cells connected by gap junctions to be in the same cell group. * Add documentation that discusses core concepts in Arbor models.
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Distributed Context
To support running on systems from laptops and workstations to large distributed HPC clusters, Arbor uses distributed contexts to:
- Describe the distributed computer system that a simulation is to be distributed over and run on.
- Perform collective operations over the distributed system, such as gather and synchronization.
- Query information about the distributed system, such as the number of distributed processes and the index/rank of the calling process.
The global context used to run a simulation is determined at run time, not at compile time. This means that if Arbor is compiled with support for MPI enabled, then at run time the user can choose between using a non-distributed (local) context, or an distributed MPI context.
An execution context is created by a user before building and running a simulation. This context is then used to perform domain decomposition and initialize the simulation (see :ref:`cppsimulation` for more about the simulation building workflow). In the example below, a context that uses MPI is used to run a distributed simulation:
The public API does not directly expose :cpp:class:`arb::distributed_context` or any of its implementations. By default :cpp:class:`arb::context` uses only local "on-node" resources. To use an MPI communicator for distributed communication, it can be initialised with the communicator:
arb::proc_allocation resources;
my_recipe recipe;
// Create a context that uses the local resources enumerated in resources,
// and that uses the standard MPI communicator MPI_COMM_WORLD for
// distributed communication.
arb::context context = arb::make_context(resources, MPI_COMM_WORLD);
// Partition model over the distributed system.
arb::domain_decomposition decomp = arb::partition_load_balance(recipe, context);
// Instantiate the simulation over the distributed system.
arb::simulation sim(recipe, decomp, context);
// Run the simulation for 100ms over the distributed system.
sim.run(100, 0.01);In the back end :cpp:class:`arb::distributed_context` defines the interface for distributed contexts, for which two implementations are provided: :cpp:class:`arb::local_context` and :cpp:class:`arb::mpi_context`. Distributed contexts are wrapped in shared pointers:
A distributed context can then be generated using helper functions :cpp:func:`arb::make_local_context` and :cpp:func:`arb::make_mpi_context`:
// Create a context that uses only local resources (is non-distributed).
auto dist_ctx arb::make_local_context();
// Create an MPI context that uses the standard MPI_COMM_WORLD communicator.
auto dist_ctx = arb::make_mpi_context(MPI_COMM_WORLD);