def __call__(self, partitioned_graph, machine):
"""
:param partitioned_graph: The partitioned_graph to measure
:type partitioned_graph:\
:py:class:`pacman.model.partitioned_graph.partitioned_graph.PartitionedGraph`
:return: The size of the graph in number of chips
:rtype: int
"""
# check that the algorithm can handle the constraints
utility_calls.check_algorithm_can_support_constraints(
constrained_vertices=partitioned_graph.subvertices,
supported_constraints=[PlacerChipAndCoreConstraint],
abstract_constraint_type=AbstractPlacerConstraint)
ordered_subverts = utility_calls.sort_objects_by_constraint_authority(
partitioned_graph.subvertices)
# Iterate over subvertices and allocate
progress_bar = ProgressBar(len(ordered_subverts),
"Measuring the partitioned graph")
resource_tracker = ResourceTracker(machine)
for subvertex in ordered_subverts:
resource_tracker.allocate_constrained_resources(
subvertex.resources_required, subvertex.constraints)
progress_bar.update()
progress_bar.end()
return {'n_chips': len(resource_tracker.keys)}
def _do_allocation(self, ordered_subverts, placements, machine):
# Iterate over subvertices and generate placements
progress_bar = ProgressBar(len(ordered_subverts),
"Placing graph vertices")
resource_tracker = ResourceTracker(
machine, self._generate_radial_chips(machine))
# iterate over subverts
for subvertex_list in ordered_subverts:
# if too many one to ones to fit on a chip, allocate individually
if len(subvertex_list) > self.MAX_CORES_PER_CHIP_TO_CONSIDER:
for subvertex in subvertex_list:
self._allocate_individual(subvertex, placements,
progress_bar, resource_tracker)
else: # can allocate in one block
# merge constraints
placement_constraint, ip_tag_constraints, \
reverse_ip_tag_constraints = \
self._merge_constraints(subvertex_list)
# locate most cores on a chip
max_size_on_a_chip = resource_tracker.\
max_available_cores_on_chips_that_satisfy(
placement_constraint, ip_tag_constraints,
reverse_ip_tag_constraints)
# if size fits block allocate, otherwise allocate individually
if max_size_on_a_chip < len(subvertex_list):
# collect resource requirement
resources = list()
for subvert in subvertex_list:
resources.append(subvert.resources_required)
# get cores
cores = resource_tracker.allocate_group(
resources, placement_constraint, ip_tag_constraints,
reverse_ip_tag_constraints)
# allocate cores to subverts
for subvert, (x, y, p, _, _) in zip(subvertex_list, cores):
placement = Placement(subvert, x, y, p)
placements.add_placement(placement)
progress_bar.update()
else:
for subvertex in subvertex_list:
self._allocate_individual(subvertex, placements,
progress_bar,
resource_tracker)
progress_bar.end()
def __call__(self, partitioned_graph, machine):
# check that the algorithm can handle the constraints
utility_calls.check_algorithm_can_support_constraints(
constrained_vertices=partitioned_graph.subvertices,
supported_constraints=[
PlacerRadialPlacementFromChipConstraint,
TagAllocatorRequireIptagConstraint,
TagAllocatorRequireReverseIptagConstraint,
PlacerChipAndCoreConstraint],
abstract_constraint_type=AbstractPlacerConstraint)
placements = Placements()
ordered_subverts = utility_calls.sort_objects_by_constraint_authority(
partitioned_graph.subvertices)
# Iterate over subvertices and generate placements
progress_bar = ProgressBar(len(ordered_subverts),
"Placing graph vertices")
resource_tracker = ResourceTracker(
machine, self._generate_radial_chips(machine))
for vertex in ordered_subverts:
self._place_vertex(vertex, resource_tracker, machine, placements)
progress_bar.update()
progress_bar.end()
return {'placements': placements}
def __call__(self, partitioned_graph, machine):
""" Place a partitioned_graph so that each subvertex is placed on a\
core
:param partitioned_graph: The partitioned_graph to place
:type partitioned_graph:\
:py:class:`pacman.model.partitioned_graph.partitioned_graph.PartitionedGraph`
:return: A set of placements
:rtype: :py:class:`pacman.model.placements.placements.Placements`
:raise pacman.exceptions.PacmanPlaceException: If something\
goes wrong with the placement
"""
# check that the algorithm can handle the constraints
utility_calls.check_algorithm_can_support_constraints(
constrained_vertices=partitioned_graph.subvertices,
supported_constraints=[PlacerChipAndCoreConstraint],
abstract_constraint_type=AbstractPlacerConstraint)
placements = Placements()
ordered_subverts = utility_calls.sort_objects_by_constraint_authority(
partitioned_graph.subvertices)
# Iterate over subvertices and generate placements
progress_bar = ProgressBar(len(ordered_subverts),
"Placing graph vertices")
resource_tracker = ResourceTracker(machine)
for subvertex in ordered_subverts:
# Create and store a new placement anywhere on the board
(x, y, p, _, _) = resource_tracker.allocate_constrained_resources(
subvertex.resources_required, subvertex.constraints)
placement = Placement(subvertex, x, y, p)
placements.add_placement(placement)
progress_bar.update()
progress_bar.end()
return {'placements': placements}
def __call__(self, partitioned_graph, machine):
""" Place a partitioned_graph so that each subvertex is placed on a\
core
:param partitioned_graph: The partitioned_graph to place
:type partitioned_graph:\
:py:class:`pacman.model.partitioned_graph.partitioned_graph.PartitionedGraph`
:return: A set of placements
:rtype: :py:class:`pacman.model.placements.placements.Placements`
:raise pacman.exceptions.PacmanPlaceException: If something\
goes wrong with the placement
"""
# check that the algorithm can handle the constraints
utility_calls.check_algorithm_can_support_constraints(
constrained_vertices=partitioned_graph.subvertices,
supported_constraints=[PlacerChipAndCoreConstraint],
abstract_constraint_type=AbstractPlacerConstraint)
placements = Placements()
ordered_subverts = utility_calls.sort_objects_by_constraint_authority(
partitioned_graph.subvertices)
# Iterate over subvertices and generate placements
progress_bar = ProgressBar(len(ordered_subverts),
"Placing graph vertices")
resource_tracker = ResourceTracker(machine)
for subvertex in ordered_subverts:
# Create and store a new placement anywhere on the board
(x, y, p, _, _) = resource_tracker.allocate_constrained_resources(
subvertex.resources_required, subvertex.constraints)
placement = Placement(subvertex, x, y, p)
placements.add_placement(placement)
progress_bar.update()
progress_bar.end()
return {'placements': placements}
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, machine, placements):
""" see AbstractTagAllocatorAlgorithm.allocate_tags
"""
resource_tracker = ResourceTracker(machine)
# Check that the algorithm can handle the constraints
progress_bar = ProgressBar(placements.n_placements,
"Allocating tags")
placements_with_tags = list()
for placement in placements.placements:
utility_calls.check_algorithm_can_support_constraints(
constrained_vertices=[placement.subvertex],
supported_constraints=[
TagAllocatorRequireIptagConstraint,
TagAllocatorRequireReverseIptagConstraint
],
abstract_constraint_type=AbstractTagAllocatorConstraint)
if len(utility_calls.locate_constraints_of_type(
placement.subvertex.constraints,
AbstractTagAllocatorConstraint)):
placements_with_tags.append(placement)
progress_bar.update()
# Go through and allocate the tags
tags = Tags()
for placement in placements_with_tags:
vertex = placement.subvertex
# Get the constraint details for the tags
(board_address, ip_tags, reverse_ip_tags) =\
utility_calls.get_ip_tag_info(vertex.constraints)
# Allocate the tags, first-come, first-served, using the
# fixed placement of the vertex, and the required resources
chips = [(placement.x, placement.y)]
resources = vertex.resources_required
(_, _, _, returned_ip_tags, returned_reverse_ip_tags) = \
resource_tracker.allocate_resources(
resources, chips, placement.p, board_address, ip_tags,
reverse_ip_tags)
# Put the allocated ip tag information into the tag object
if returned_ip_tags is not None:
for (tag_constraint, (board_address, tag)) in zip(
ip_tags, returned_ip_tags):
ip_tag = IPTag(
board_address, tag, tag_constraint.ip_address,
tag_constraint.port, tag_constraint.strip_sdp)
tags.add_ip_tag(ip_tag, vertex)
# Put the allocated reverse ip tag information into the tag object
if returned_reverse_ip_tags is not None:
for (tag_constraint, (board_address, tag)) in zip(
reverse_ip_tags, returned_reverse_ip_tags):
reverse_ip_tag = ReverseIPTag(
board_address, tag, tag_constraint.port, placement.x,
placement.y, placement.p, tag_constraint.sdp_port)
tags.add_reverse_ip_tag(reverse_ip_tag, vertex)
progress_bar.end()
return {'tags': tags}
def __call__(self, machine, placements):
""" see AbstractTagAllocatorAlgorithm.allocate_tags
"""
resource_tracker = ResourceTracker(machine)
# Check that the algorithm can handle the constraints
progress_bar = ProgressBar(placements.n_placements, "Allocating tags")
placements_with_tags = list()
for placement in placements.placements:
utility_calls.check_algorithm_can_support_constraints(
constrained_vertices=[placement.subvertex],
supported_constraints=[
TagAllocatorRequireIptagConstraint,
TagAllocatorRequireReverseIptagConstraint
],
abstract_constraint_type=AbstractTagAllocatorConstraint)
if len(
utility_calls.locate_constraints_of_type(
placement.subvertex.constraints,
AbstractTagAllocatorConstraint)):
placements_with_tags.append(placement)
progress_bar.update()
# Go through and allocate the tags
tags = Tags()
for placement in placements_with_tags:
vertex = placement.subvertex
# Get the constraint details for the tags
(board_address, ip_tags, reverse_ip_tags) =\
utility_calls.get_ip_tag_info(vertex.constraints)
# Allocate the tags, first-come, first-served, using the
# fixed placement of the vertex, and the required resources
chips = [(placement.x, placement.y)]
resources = vertex.resources_required
(_, _, _, returned_ip_tags, returned_reverse_ip_tags) = \
resource_tracker.allocate_resources(
resources, chips, placement.p, board_address, ip_tags,
reverse_ip_tags)
# Put the allocated ip tag information into the tag object
if returned_ip_tags is not None:
for (tag_constraint, (board_address,
tag)) in zip(ip_tags, returned_ip_tags):
ip_tag = IPTag(board_address, tag,
tag_constraint.ip_address,
tag_constraint.port,
tag_constraint.strip_sdp)
tags.add_ip_tag(ip_tag, vertex)
# Put the allocated reverse ip tag information into the tag object
if returned_reverse_ip_tags is not None:
for (tag_constraint, (board_address,
tag)) in zip(reverse_ip_tags,
returned_reverse_ip_tags):
reverse_ip_tag = ReverseIPTag(board_address, tag,
tag_constraint.port,
placement.x, placement.y,
placement.p,
tag_constraint.sdp_port)
tags.add_reverse_ip_tag(reverse_ip_tag, vertex)
progress_bar.end()
return {'tags': tags}
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
