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 an arbor.recipe over the distributed and local hardware resources described by an arbor.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 and gpu_group_size, and whether to prefer_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, and prefer_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. The domain_decomposition object also has meta-data 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 non-overlapping 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 non-distributed 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.

group_description(kind, gids, backend)

Construct a group description with parameters kind, gids and backend.

kind

The kind of cell in the group.

gids

The list of gids of the cells in the cell group.

backend

The hardware backend on which the cell group will run.