Domain Decomposition¶
The Python API for partitioning a model over distributed and local hardware is described here.
Load Balancers¶
Load balancing generates a domain_decomposition
given an arbor.recipe
and a description of the hardware on which the model will run. Currently Arbor provides
one load balancer, partition_load_balance()
, and more will be added over time.
If the model is distributed with MPI, the partitioning algorithm for cells is
distributed with MPI communication. The returned domain_decomposition
describes the cell groups on the local MPI rank.

arbor.
partition_load_balance
(recipe, context, hints)¶ Construct a
domain_decomposition
that distributes the cells in the model described by anarbor.recipe
over the distributed and local hardware resources described by anarbor.context
.The algorithm counts the number of each cell type in the global model, then partitions the cells of each type equally over the available nodes. If a GPU is available, and if the cell type can be run on the GPU, the cells on each node are put into one large group to maximise the amount of fine grained parallelism in the cell group. Otherwise, cells are grouped into small groups that fit in cache, and can be distributed over the available cores. Optionally, provide a dictionary of
partition_hint
s for certain cell kinds, by default this dictionary is empty.Note
The partitioning assumes that all cells of the same kind have equal computational cost, hence it may not produce a balanced partition for models with cells that have a large variance in computational costs.

class
arbor.
partition_hint
¶ Provide a hint on how the cell groups should be partitioned.

partition_hint
(cpu_group_size, gpu_group_size, prefer_gpu)¶ Construct a partition hint with arguments
cpu_group_size
andgpu_group_size
, and whether toprefer_gpu
.By default returns a partition hint with
cpu_group_size
=1
, i.e., each cell is put in its own group,gpu_group_size
=max
, i.e., all cells are put in one group, andprefer_gpu
=True
, i.e., GPU usage is preferred.

cpu_group_size
¶ The size of the cell group assigned to CPU. Must be positive, else set to default value.

gpu_group_size
¶ The size of the cell group assigned to GPU. Must be positive, else set to default value.

prefer_gpu
¶ Whether GPU usage is preferred.

max_size
¶ Get the maximum size of cell groups.

An example of a partition load balance with hints reads as follows:
import arbor
# Get a communication context (with 4 threads, no GPU)
context = arbor.context(threads=4, gpu_id=None)
# Initialise a recipe of user defined type my_recipe with 100 cells.
n_cells = 100
recipe = my_recipe(n_cells)
# The hints perfer the multicore backend, so the decomposition is expected
# to never have cell groups on the GPU, regardless of whether a GPU is
# available or not.
cable_hint = arb.partition_hint()
cable_hint.prefer_gpu = False
cable_hint.cpu_group_size = 3
spike_hint = arb.partition_hint()
spike_hint.prefer_gpu = False
spike_hint.cpu_group_size = 4
hints = dict([(arb.cell_kind.cable, cable_hint), (arb.cell_kind.spike_source, spike_hint)])
decomp = arb.partition_load_balance(recipe, context, hints)
Decomposition¶
As defined in Domain Decomposition a domain decomposition is a description of the distribution of the model over the available computational resources. Therefore, the following data structures are used to describe domain decompositions.

class
arbor.
backend
¶ Enumeration used to indicate which hardware backend to execute a cell group on.

multicore
¶ Use multicore backend.

gpu
¶ Use GPU backend.
Note
Setting the GPU back end is only meaningful if the cell group type supports the GPU backend.


class
arbor.
domain_decomposition
¶ Describes a domain decomposition and is soley responsible for describing the distribution of cells across cell groups and domains. It holds cell group descriptions (
groups
) for cells assigned to the local domain, and a helper function (gid_domain()
) used to look up which domain a cell has been assigned to. Thedomain_decomposition
object also has metadata about the number of cells in the global model, and the number of domains over which the model is destributed.Note
The domain decomposition represents a division of all of the cells in the model into nonoverlapping sets, with one set of cells assigned to each domain.

gid_domain
(gid)¶ A function for querying the domain id that a cell is assigned to (using global identifier
arbor.cell_member.gid
).

num_domains
¶ The number of domains that the model is distributed over.

domain_id
¶ The index of the local domain. Always 0 for nondistributed models, and corresponds to the MPI rank for distributed runs.

num_local_cells
¶ The total number of cells in the local domain.

num_global_cells
¶ The total number of cells in the global model (sum of
num_local_cells
over all domains).

groups
¶ The descriptions of the cell groups on the local domain. See
group_description
.


class
arbor.
group_description
¶ Return the indexes of a set of cells of the same kind that are grouped together in a cell group in an
arbor.simulation
.