def __call__(self, graph, machine):
utility_calls.check_algorithm_can_support_constraints(
constrained_vertices=graph.vertices,
supported_constraints=[PartitionerMaximumSizeConstraint],
abstract_constraint_type=AbstractPartitionerConstraint)
# start progress bar
progress_bar = ProgressBar(len(graph.vertices),
"Partitioning graph vertices")
vertices = graph.vertices
subgraph = PartitionedGraph(label="partitioned_graph for partitionable"
"_graph {}".format(graph.label))
graph_to_subgraph_mapper = GraphMapper(graph.label, subgraph.label)
resource_tracker = ResourceTracker(machine)
# Partition one vertex at a time
for vertex in vertices:
# Get the usage of the first atom, then assume that this
# will be the usage of all the atoms
requirements = vertex.get_resources_used_by_atoms(Slice(0, 1),
graph)
# Locate the maximum resources available
max_resources_available = \
resource_tracker.get_maximum_constrained_resources_available(
vertex.constraints)
# Find the ratio of each of the resources - if 0 is required,
# assume the ratio is the max available
atoms_per_sdram = self._get_ratio(
max_resources_available.sdram.get_value(),
requirements.sdram.get_value())
atoms_per_dtcm = self._get_ratio(
max_resources_available.dtcm.get_value(),
requirements.dtcm.get_value())
atoms_per_cpu = self._get_ratio(
max_resources_available.cpu.get_value(),
requirements.cpu.get_value())
max_atom_values = [atoms_per_sdram, atoms_per_dtcm, atoms_per_cpu]
max_atoms_constraints = utility_calls.locate_constraints_of_type(
vertex.constraints, PartitionerMaximumSizeConstraint)
for max_atom_constraint in max_atoms_constraints:
max_atom_values.append(max_atom_constraint.size)
atoms_per_core = min(max_atom_values)
# Partition into subvertices
counted = 0
while counted < vertex.n_atoms:
# Determine subvertex size
remaining = vertex.n_atoms - counted
if remaining > atoms_per_core:
alloc = atoms_per_core
else:
alloc = remaining
# Create and store new subvertex, and increment elements
# counted
if counted < 0 or counted + alloc - 1 < 0:
raise PacmanPartitionException("Not enough resources"
" available to create"
" subvertex")
vertex_slice = Slice(counted, counted + (alloc - 1))
subvertex_usage = vertex.get_resources_used_by_atoms(
vertex_slice, graph)
subvert = vertex.create_subvertex(
vertex_slice, subvertex_usage,
"{}:{}:{}".format(vertex.label, counted,
(counted + (alloc - 1))),
partition_algorithm_utilities.
get_remaining_constraints(vertex))
subgraph.add_subvertex(subvert)
graph_to_subgraph_mapper.add_subvertex(
subvert, vertex_slice, vertex)
counted = counted + alloc
# update allocated resources
resource_tracker.allocate_constrained_resources(
subvertex_usage, vertex.constraints)
# update and end progress bars as needed
progress_bar.update()
progress_bar.end()
partition_algorithm_utilities.generate_sub_edges(
subgraph, graph_to_subgraph_mapper, graph)
return {'Partitioned_graph': subgraph,
'Graph_mapper': graph_to_subgraph_mapper}
def __call__(self, graph, machine):
""" Partition a partitionable_graph so that each subvertex will fit\
on a processor within the machine
:param graph: The partitionable_graph to partition
:type graph:\
:py:class:`pacman.model.graph.partitionable_graph.PartitionableGraph`
:param machine: The machine with respect to which to partition the\
partitionable_graph
:type machine: :py:class:`spinn_machine.machine.Machine`
:return: A partitioned_graph of partitioned vertices and partitioned\
edges
:rtype:\
:py:class:`pacman.model.partitioned_graph.partitioned_graph.PartitionedGraph`
:raise pacman.exceptions.PacmanPartitionException: If something\
goes wrong with the partitioning
"""
utility_calls.check_algorithm_can_support_constraints(
constrained_vertices=graph.vertices,
abstract_constraint_type=AbstractPartitionerConstraint,
supported_constraints=[PartitionerMaximumSizeConstraint,
PartitionerSameSizeAsVertexConstraint])
# Load the vertices and create the subgraph to fill
vertices = graph.vertices
subgraph = PartitionedGraph(
label="partitioned graph for {}".format(graph.label))
graph_mapper = GraphMapper(graph.label, subgraph.label)
# sort out vertex's by constraints
vertices = utility_calls.sort_objects_by_constraint_authority(vertices)
# Set up the progress
n_atoms = 0
for vertex in vertices:
n_atoms += vertex.n_atoms
progress_bar = ProgressBar(n_atoms, "Partitioning graph vertices")
resource_tracker = ResourceTracker(machine)
# Partition one vertex at a time
for vertex in vertices:
# check that the vertex hasn't already been partitioned
subverts_from_vertex = \
graph_mapper.get_subvertices_from_vertex(vertex)
# if not, partition
if subverts_from_vertex is None:
self._partition_vertex(vertex, subgraph, graph_mapper,
resource_tracker, graph)
progress_bar.update(vertex.n_atoms)
progress_bar.end()
partition_algorithm_utilities.generate_sub_edges(
subgraph, graph_mapper, graph)
results = dict()
results['partitioned_graph'] = subgraph
results['graph_mapper'] = graph_mapper
return results
def __call__(self, graph, machine):
""" Partition a partitionable_graph so that each subvertex will fit\
on a processor within the machine
:param graph: The partitionable_graph to partition
:type graph:\
:py:class:`pacman.model.graph.partitionable_graph.PartitionableGraph`
:param machine: The machine with respect to which to partition the\
partitionable_graph
:type machine: :py:class:`spinn_machine.machine.Machine`
:return: A partitioned_graph of partitioned vertices and partitioned\
edges
:rtype:\
:py:class:`pacman.model.partitioned_graph.partitioned_graph.PartitionedGraph`
:raise pacman.exceptions.PacmanPartitionException: If something\
goes wrong with the partitioning
"""
utility_calls.check_algorithm_can_support_constraints(
constrained_vertices=graph.vertices,
abstract_constraint_type=AbstractPartitionerConstraint,
supported_constraints=[PartitionerMaximumSizeConstraint,
PartitionerSameSizeAsVertexConstraint])
# Load the vertices and create the subgraph to fill
vertices = graph.vertices
subgraph = PartitionedGraph(
label="partitioned graph for {}".format(graph.label))
graph_mapper = GraphMapper(graph.label, subgraph.label)
# sort out vertex's by constraints
vertices = utility_calls.sort_objects_by_constraint_authority(vertices)
# Set up the progress
n_atoms = 0
for vertex in vertices:
n_atoms += vertex.n_atoms
progress_bar = ProgressBar(n_atoms, "Partitioning graph vertices")
resource_tracker = ResourceTracker(machine)
# Partition one vertex at a time
for vertex in vertices:
# check that the vertex hasn't already been partitioned
subverts_from_vertex = \
graph_mapper.get_subvertices_from_vertex(vertex)
# if not, partition
if subverts_from_vertex is None:
self._partition_vertex(
vertex, subgraph, graph_mapper, resource_tracker, graph)
progress_bar.update(vertex.n_atoms)
progress_bar.end()
partition_algorithm_utilities.generate_sub_edges(
subgraph, graph_mapper, graph)
results = dict()
results['partitioned_graph'] = subgraph
results['graph_mapper'] = graph_mapper
results['nChips'] = len(resource_tracker.keys)
return results
