def __call__(self, subgraph, n_keys_map, routing_paths):
# check that this algorithm supports the constraints
utility_calls.check_algorithm_can_support_constraints(
constrained_vertices=subgraph.subedges,
supported_constraints=[
KeyAllocatorFixedMaskConstraint,
KeyAllocatorFixedKeyAndMaskConstraint,
KeyAllocatorContiguousRangeContraint],
abstract_constraint_type=AbstractKeyAllocatorConstraint)
routing_tables = MulticastRoutingTables()
# Get the partitioned edges grouped by those that require the same key
same_key_groups = \
routing_info_allocator_utilities.get_edge_groups(subgraph)
# Go through the groups and allocate keys
progress_bar = ProgressBar(len(same_key_groups),
"Allocating routing keys")
routing_infos = RoutingInfo()
for group in same_key_groups:
# Check how many keys are needed for the edges of the group
edge_n_keys = None
for edge in group:
n_keys = n_keys_map.n_keys_for_partitioned_edge(edge)
if edge_n_keys is None:
edge_n_keys = n_keys
elif edge_n_keys != n_keys:
raise PacmanRouteInfoAllocationException(
"Two edges require the same keys but request a"
" different number of keys")
# Get any fixed keys and masks from the group and attempt to
# allocate them
keys_and_masks = routing_info_allocator_utilities.\
get_fixed_key_and_mask(group)
fixed_mask, fields = \
routing_info_allocator_utilities.get_fixed_mask(group)
if keys_and_masks is not None:
self._allocate_fixed_keys_and_masks(keys_and_masks, fixed_mask)
else:
keys_and_masks = self._allocate_keys_and_masks(
fixed_mask, fields, edge_n_keys)
# Allocate the routing information
for edge in group:
subedge_info = SubedgeRoutingInfo(keys_and_masks, edge)
routing_infos.add_subedge_info(subedge_info)
# update routing tables with entries
routing_info_allocator_utilities.add_routing_key_entries(
routing_paths, subedge_info, edge, routing_tables)
progress_bar.update()
progress_bar.end()
return {'routing_infos': routing_infos,
'routing_tables': routing_tables}
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 get_v(self, label, buffer_manager, region, state_region, placements,
graph_mapper, partitionable_vertex):
subvertices = \
graph_mapper.get_subvertices_from_vertex(partitionable_vertex)
ms_per_tick = self._machine_time_step / 1000.0
data = list()
missing_str = ""
progress_bar = \
ProgressBar(len(subvertices),
"Getting membrane voltage for {}".format(label))
for subvertex in subvertices:
vertex_slice = graph_mapper.get_subvertex_slice(subvertex)
placement = placements.get_placement_of_subvertex(subvertex)
x = placement.x
y = placement.y
p = placement.p
# for buffering output info is taken form the buffer manager
neuron_param_region_data_pointer, missing_data =\
buffer_manager.get_data_for_vertex(
x, y, p, region, state_region)
if missing_data:
missing_str += "({}, {}, {}); ".format(x, y, p)
record_raw = neuron_param_region_data_pointer.read_all()
record_length = len(record_raw)
n_rows = record_length / ((vertex_slice.n_atoms + 1) * 4)
record = (numpy.asarray(record_raw, dtype="uint8").
view(dtype="<i4")).reshape((n_rows,
(vertex_slice.n_atoms + 1)))
split_record = numpy.array_split(record, [1, 1], 1)
record_time = numpy.repeat(
split_record[0] * float(ms_per_tick), vertex_slice.n_atoms, 1)
record_ids = numpy.tile(
numpy.arange(vertex_slice.lo_atom, vertex_slice.hi_atom + 1),
len(record_time)).reshape((-1, vertex_slice.n_atoms))
record_membrane_potential = split_record[2] / 32767.0
part_data = numpy.dstack(
[record_ids, record_time, record_membrane_potential])
part_data = numpy.reshape(part_data, [-1, 3])
data.append(part_data)
progress_bar.update()
progress_bar.end()
if len(missing_str) > 0:
logger.warn(
"Population {} is missing membrane voltage data in region {}"
" from the following cores: {}".format(
label, region, missing_str))
data = numpy.vstack(data)
order = numpy.lexsort((data[:, 1], data[:, 0]))
result = data[order]
return result
def get_spikes(self, label, buffer_manager, region, state_region, placements, graph_mapper, partitionable_vertex):
spike_times = list()
spike_ids = list()
ms_per_tick = self._machine_time_step / 1000.0
subvertices = graph_mapper.get_subvertices_from_vertex(partitionable_vertex)
missing_str = ""
progress_bar = ProgressBar(len(subvertices), "Getting spikes for {}".format(label))
for subvertex in subvertices:
placement = placements.get_placement_of_subvertex(subvertex)
subvertex_slice = graph_mapper.get_subvertex_slice(subvertex)
x = placement.x
y = placement.y
p = placement.p
lo_atom = subvertex_slice.lo_atom
# Read the spikes
n_words = int(math.ceil(subvertex_slice.n_atoms / 32.0))
n_bytes = n_words * 4
n_words_with_timestamp = n_words + 1
# for buffering output info is taken form the buffer manager
neuron_param_region_data_pointer, data_missing = buffer_manager.get_data_for_vertex(
x, y, p, region, state_region
)
if data_missing:
missing_str += "({}, {}, {}); ".format(x, y, p)
record_raw = neuron_param_region_data_pointer.read_all()
raw_data = (numpy.asarray(record_raw, dtype="uint8").view(dtype="<i4")).reshape(
[-1, n_words_with_timestamp]
)
split_record = numpy.array_split(raw_data, [1, 1], 1)
record_time = split_record[0] * float(ms_per_tick)
spikes = split_record[2].byteswap().view("uint8")
bits = numpy.fliplr(numpy.unpackbits(spikes).reshape((-1, 32))).reshape((-1, n_bytes * 8))
time_indices, indices = numpy.where(bits == 1)
times = record_time[time_indices].reshape((-1))
indices = indices + lo_atom
spike_ids.append(indices)
spike_times.append(times)
progress_bar.update()
progress_bar.end()
if len(missing_str) > 0:
logger.warn(
"Population {} is missing spike data in region {} from the"
" following cores: {}".format(label, region, missing_str)
)
spike_ids = numpy.hstack(spike_ids)
spike_times = numpy.hstack(spike_times)
result = numpy.dstack((spike_ids, spike_times))[0]
return result[numpy.lexsort((spike_times, spike_ids))]
def get_spikes(self, label, buffer_manager, region, state_region,
placements, graph_mapper, partitionable_vertex,
base_key_function):
results = list()
missing_str = ""
ms_per_tick = self._machine_time_step / 1000.0
subvertices = \
graph_mapper.get_subvertices_from_vertex(partitionable_vertex)
progress_bar = ProgressBar(len(subvertices),
"Getting spikes for {}".format(label))
for subvertex in subvertices:
placement = placements.get_placement_of_subvertex(subvertex)
subvertex_slice = graph_mapper.get_subvertex_slice(subvertex)
x = placement.x
y = placement.y
p = placement.p
# Read the spikes
raw_spike_data, data_missing = \
buffer_manager.get_data_for_vertex(
x, y, p, region, state_region)
if data_missing:
missing_str += "({}, {}, {}); ".format(x, y, p)
spike_data = raw_spike_data.read_all()
number_of_bytes_written = len(spike_data)
offset = 0
while offset < number_of_bytes_written:
eieio_header = EIEIODataHeader.from_bytestring(
spike_data, offset)
offset += eieio_header.size
timestamp = eieio_header.payload_base * ms_per_tick
timestamps = numpy.repeat([timestamp], eieio_header.count)
keys = numpy.frombuffer(
spike_data, dtype="<u4", count=eieio_header.count,
offset=offset)
neuron_ids = ((keys - base_key_function(subvertex)) +
subvertex_slice.lo_atom)
offset += eieio_header.count * 4
results.append(numpy.dstack((neuron_ids, timestamps))[0])
progress_bar.update()
progress_bar.end()
if len(missing_str) > 0:
logger.warn(
"Population {} is missing spike data in region {} from the"
" following cores: {}".format(label, region, missing_str))
if len(results) != 0:
result = numpy.vstack(results)
result = result[numpy.lexsort((result[:, 1], result[:, 0]))]
else:
result = []
return result
def validate_routes(partitioned_graph, placements, routing_infos,
routing_tables, machine):
""" Go though the placements given during init and check that the\
routing entries within the routing tables support reach the\
correction destinations as well as not producing any cycles.
:param partitioned_graph: the subgraph of the problem spec
:param placements: the placements container
:param routing_infos: the routing info container
:param routing_tables: the routing tables generated by the\
routing algorithm
:param machine: the python machine object
:type machine: spinnmachine.machine.Machine object
:return: None
:raises PacmanRoutingException: when either no routing table entry is\
found by the search on a given router, or a cycle is\
detected
"""
progress = ProgressBar(
len(list(placements.placements)),
"Verifying the routes from each core travel to the correct locations")
for placement in placements.placements:
outgoing_edges_for_partitioned_vertex = \
partitioned_graph.outgoing_subedges_from_subvertex(
placement.subvertex)
# locate all placements to which this placement/subvertex will
# communicate with for a given key_and_mask and search its
# determined destinations
key_and_masks = \
routing_infos.get_key_and_masks_for_partitioned_vertex(
placement.subvertex)
# locate each set for a given key_and_mask
for key_and_mask in key_and_masks:
destination_placements = list()
for outgoing_edge in outgoing_edges_for_partitioned_vertex:
edge_key_and_masks = \
routing_infos.get_keys_and_masks_from_subedge(
outgoing_edge)
for edge_key_and_mask in edge_key_and_masks:
if edge_key_and_mask == key_and_mask:
dest_placement = \
placements.get_placement_of_subvertex(
outgoing_edge.post_subvertex)
dest_tuple = PlacementTuple(x=dest_placement.x,
y=dest_placement.y,
p=dest_placement.p)
if dest_tuple not in destination_placements:
destination_placements.append(dest_tuple)
# search for these destinations
_search_route(placement, destination_placements, key_and_mask,
routing_tables, machine)
progress.update()
progress.end()
def __call__(self, executable_targets, app_id, transciever,
loaded_application_data_token):
""" Go through the executable targets and load each binary to \
everywhere and then send a start request to the cores that \
actually use it
"""
if not loaded_application_data_token:
raise exceptions.ConfigurationException(
"The token for having loaded the application data token is set"
" to false and therefore I cannot run. Please fix and try "
"again")
progress_bar = ProgressBar(executable_targets.total_processors,
"Loading executables onto the machine")
for executable_target_key in executable_targets.binary_paths():
file_reader = SpinnmanFileDataReader(executable_target_key)
core_subset = executable_targets.\
retrieve_cores_for_a_executable_target(executable_target_key)
statinfo = os.stat(executable_target_key)
size = statinfo.st_size
# TODO there is a need to parse the binary and see if its
# ITCM and DTCM requirements are within acceptable params for
# operating on spinnaker. Currently there just a few safety
# checks which may not be accurate enough.
if size > constants.MAX_SAFE_BINARY_SIZE:
logger.warn(
"The size of {} is large enough that its"
" possible that the binary may be larger than what is"
" supported by spinnaker currently. Please reduce the"
" binary size if it starts to behave strangely, or goes"
" into the wdog state before starting.".format(
executable_target_key))
if size > constants.MAX_POSSIBLE_BINARY_SIZE:
raise exceptions.ConfigurationException(
"The size of {} is too large and therefore"
" will very likely cause a WDOG state. Until a more"
" precise measurement of ITCM and DTCM can be produced"
" this is deemed as an error state. Please reduce the"
" size of your binary or circumvent this error check."
.format(executable_target_key))
transciever.execute_flood(core_subset, file_reader, app_id, size)
acutal_cores_loaded = 0
for chip_based in core_subset.core_subsets:
for _ in chip_based.processor_ids:
acutal_cores_loaded += 1
progress_bar.update(amount_to_add=acutal_cores_loaded)
progress_bar.end()
return {"LoadBinariesToken": True}
def get_synaptic_list_from_machine(self, graph_mapper, partitioned_graph,
placements, transceiver, routing_infos):
"""
Get synaptic data for all connections in this Projection from the
machine.
"""
if self._stored_synaptic_data_from_machine is None:
timer = None
if conf.config.getboolean("Reports", "outputTimesForSections"):
timer = Timer()
timer.start_timing()
subedges = \
graph_mapper.get_partitioned_edges_from_partitionable_edge(
self)
if subedges is None:
subedges = list()
synaptic_list = copy.copy(self._synapse_list)
synaptic_list_rows = synaptic_list.get_rows()
progress_bar = ProgressBar(
len(subedges),
"Reading back synaptic matrix for edge between"
" {} and {}".format(self._pre_vertex.label,
self._post_vertex.label))
for subedge in subedges:
n_rows = subedge.get_n_rows(graph_mapper)
pre_vertex_slice = \
graph_mapper.get_subvertex_slice(subedge.pre_subvertex)
post_vertex_slice = \
graph_mapper.get_subvertex_slice(subedge.post_subvertex)
sub_edge_post_vertex = \
graph_mapper.get_vertex_from_subvertex(
subedge.post_subvertex)
rows = sub_edge_post_vertex.get_synaptic_list_from_machine(
placements, transceiver, subedge.pre_subvertex, n_rows,
subedge.post_subvertex,
self._synapse_row_io, partitioned_graph,
routing_infos, subedge.weight_scales).get_rows()
for i in range(len(rows)):
synaptic_list_rows[
i + pre_vertex_slice.lo_atom].set_slice_values(
rows[i], vertex_slice=post_vertex_slice)
progress_bar.update()
progress_bar.end()
self._stored_synaptic_data_from_machine = synaptic_list
if conf.config.getboolean("Reports", "outputTimesForSections"):
logger.info("Time to read matrix: {}".format(
timer.take_sample()))
return self._stored_synaptic_data_from_machine
def __call__(self, subgraph, graph_mapper):
"""
:param subgraph: the subgraph whose edges are to be filtered
:param graph_mapper: the graph mapper between partitionable and \
partitioned graphs.
:return: a new graph mapper and partitioned graph
"""
new_sub_graph = PartitionedGraph(label=subgraph.label)
new_graph_mapper = GraphMapper(graph_mapper.first_graph_label,
subgraph.label)
# create progress bar
progress_bar = ProgressBar(
len(subgraph.subvertices) + len(subgraph.subedges),
"Filtering edges")
# add the subverts directly, as they wont be pruned.
for subvert in subgraph.subvertices:
new_sub_graph.add_subvertex(subvert)
associated_vertex = graph_mapper.get_vertex_from_subvertex(subvert)
vertex_slice = graph_mapper.get_subvertex_slice(subvert)
new_graph_mapper.add_subvertex(
subvertex=subvert, vertex_slice=vertex_slice,
vertex=associated_vertex)
progress_bar.update()
# start checking subedges to decide which ones need pruning....
for subvert in subgraph.subvertices:
out_going_partitions = \
subgraph.outgoing_edges_partitions_from_vertex(subvert)
for partitioner_identifier in out_going_partitions:
for subedge in \
out_going_partitions[partitioner_identifier].edges:
if not self._is_filterable(subedge, graph_mapper):
logger.debug("this subedge was not pruned {}"
.format(subedge))
new_sub_graph.add_subedge(subedge,
partitioner_identifier)
associated_edge = graph_mapper.\
get_partitionable_edge_from_partitioned_edge(
subedge)
new_graph_mapper.add_partitioned_edge(
subedge, associated_edge)
else:
logger.debug("this subedge was pruned {}"
.format(subedge))
progress_bar.update()
progress_bar.end()
# returned the pruned partitioned_graph and graph_mapper
return {'new_sub_graph': new_sub_graph,
'new_graph_mapper': new_graph_mapper}
def _write_router_provenance_data(
self, root, router_tables, machine, txrx):
""" Writes the provenance data of the router diagnostics
:param root: the root element to add diagnostics to
:return: None
"""
progress = ProgressBar(
machine.n_chips,
"Getting provenance data from machine's routing tables")
# acquire diagnostic data
router_diagnostics = dict()
reinjector_statuses = dict()
for router_table in router_tables.routing_tables:
x = router_table.x
y = router_table.y
if not machine.get_chip_at(x, y).virtual:
router_diagnostic = txrx.get_router_diagnostics(x, y)
router_diagnostics[x, y] = router_diagnostic
reinjector_status = txrx.get_reinjection_status(x, y)
reinjector_statuses[x, y] = reinjector_status
doc = etree.SubElement(root, "router_counters")
expected_routers = etree.SubElement(doc, "Used_Routers")
for x, y in router_diagnostics:
self._write_router_diag(
expected_routers, x, y, router_diagnostics[x, y],
reinjector_statuses[x, y])
progress.update()
unexpected_routers = etree.SubElement(doc, "Unexpected_Routers")
for chip in machine.chips:
if not chip.virtual:
if (chip.x, chip.y) not in router_diagnostics:
router_diagnostic = \
txrx.get_router_diagnostics(chip.x, chip.y)
has_dropped_mc_packets = \
router_diagnostic.n_dropped_multicast_packets != 0
has_local_multicast_packets = \
router_diagnostic.n_local_multicast_packets != 0
has_external_multicast_packets = \
router_diagnostic.n_external_multicast_packets != 0
reinjector_status = \
txrx.get_reinjection_status(chip.x, chip.y)
if (has_dropped_mc_packets or
has_local_multicast_packets or
has_external_multicast_packets):
self._write_router_diag(
unexpected_routers, chip.x, chip.y,
router_diagnostic, reinjector_status)
progress.update()
progress.end()
def generate_sub_edges(subgraph, graph_to_subgraph_mapper, graph):
""" Generate the sub edges for the subvertices in the graph
:param subgraph: the partitioned graph to work with
:type subgraph:\
:py:class:`pacman.model.partitioned_graph.partitioned_graph.PartitionedGraph`
:param graph_to_subgraph_mapper: the mapper between the \
partitionable graph and the partitioned graph
:type graph_to_subgraph_mapper:\
:py:class:`pacman.model.graph_mapper.GraphMapper`
:param graph: the partitionable graph to work with
:type graph:\
:py:class:`pacman.model.graph.partitionable_graph.PartitionableGraph`
"""
# start progress bar
progress_bar = ProgressBar(len(subgraph.subvertices),
"on partitioning the partitionable_graph's "
"edges")
# Partition edges according to vertex partitioning
for src_sv in subgraph.subvertices:
# For each out edge of the parent vertex...
vertex = graph_to_subgraph_mapper.get_vertex_from_subvertex(src_sv)
outgoing_partitions = \
graph.outgoing_edges_partitions_from_vertex(vertex)
for outgoing_partition_identifier in outgoing_partitions:
out_edges = \
outgoing_partitions[outgoing_partition_identifier].edges
for edge in out_edges:
# and create and store a new subedge for each post-subvertex
post_vertex = edge.post_vertex
post_subverts = (graph_to_subgraph_mapper
.get_subvertices_from_vertex(post_vertex))
for dst_sv in post_subverts:
subedge = edge.create_subedge(src_sv, dst_sv)
subgraph.add_subedge(subedge,
outgoing_partition_identifier)
graph_to_subgraph_mapper.add_partitioned_edge(
subedge, edge)
progress_bar.update()
progress_bar.end()
def __call__(self, placements, file_path):
"""
:param placements:
:param folder_path:
:return:
"""
progress_bar = ProgressBar(
len(placements), "Converting to json core allocations")
# write basic stuff
json_core_allocations_dict = dict()
json_core_allocations_dict['type'] = "cores"
# process placements
for placement in placements:
json_core_allocations_dict[placement.subvertex.label] = \
[placement.p, placement.p + 1]
progress_bar.update()
# dump dict into json file
file_to_write = open(file_path, "w")
json.dump(json_core_allocations_dict, file_to_write)
file_to_write.close()
# validate the schema
core_allocations_schema_file_path = os.path.join(
os.path.dirname(file_format_schemas.__file__),
"core_allocations.json"
)
file_to_read = open(core_allocations_schema_file_path, "r")
core_allocations_schema = json.load(file_to_read)
jsonschema.validate(
json_core_allocations_dict, core_allocations_schema)
# complete progress bar
progress_bar.end()
# return the file format
return {"FileCoreAllocations": file_path}
def __call__(self, placements, file_path):
"""
:param placements: the memory placements object
:param file_path: the file path for the placements.json
:return: file path for the placements.json
"""
# write basic stuff
json_placement_dictory_rep = dict()
progress_bar = ProgressBar(len(placements.placements) + 1,
"converting to json placements")
# process placements
for placement in placements:
json_placement_dictory_rep[placement.subvertex.label] = \
[placement.x, placement.y]
progress_bar.update()
# dump dict into json file
file_to_write = open(file_path, "w")
json.dump(json_placement_dictory_rep, file_to_write)
file_to_write.close()
# validate the schema
placements_schema_file_path = os.path.join(
os.path.dirname(file_format_schemas.__file__), "placements.json"
)
file_to_read = open(placements_schema_file_path, "r")
placements_schema = json.load(file_to_read)
jsonschema.validate(
json_placement_dictory_rep, placements_schema)
progress_bar.update()
progress_bar.end()
# return the file format
return {"FilePlacements": file_path}
def __call__(self, partitioned_graph, graph_mapper):
""" Generate an n_keys map for the graph and add constraints
:param partitioned_graph:
:param graph_mapper:
:return:
"""
progress_bar = ProgressBar(
len(partitioned_graph.subedges),
"Deducing edge to number of keys map")
n_keys_map = DictBasedPartitionedEdgeNKeysMap()
for edge in partitioned_graph.subedges:
vertex_slice = graph_mapper.get_subvertex_slice(
edge.pre_subvertex)
super_edge = (graph_mapper
.get_partitionable_edge_from_partitioned_edge(edge))
if not isinstance(super_edge.pre_vertex,
AbstractProvidesNKeysForEdge):
n_keys_map.set_n_keys_for_patitioned_edge(edge,
vertex_slice.n_atoms)
else:
n_keys_map.set_n_keys_for_patitioned_edge(
edge,
super_edge.pre_vertex.get_n_keys_for_partitioned_edge(
edge, graph_mapper))
if isinstance(super_edge.pre_vertex,
AbstractProvidesOutgoingEdgeConstraints):
edge.add_constraints(
super_edge.pre_vertex.get_outgoing_edge_constraints(
edge, graph_mapper))
if isinstance(super_edge.post_vertex,
AbstractProvidesIncomingEdgeConstraints):
edge.add_constraints(
super_edge.post_vertex.get_incoming_edge_constraints(
edge, graph_mapper))
progress_bar.update()
progress_bar.end()
return {'n_keys_map': n_keys_map}
def __call__(self, router_tables, app_id, transciever, machine):
progress_bar = ProgressBar(len(list(router_tables.routing_tables)),
"Loading routing data onto the machine")
# load each router table that is needed for the application to run into
# the chips SDRAM
for router_table in router_tables.routing_tables:
if not machine.get_chip_at(router_table.x, router_table.y).virtual:
transciever.clear_multicast_routes(router_table.x,
router_table.y)
transciever.clear_router_diagnostic_counters(router_table.x,
router_table.y)
if len(router_table.multicast_routing_entries) > 0:
transciever.load_multicast_routes(
router_table.x, router_table.y,
router_table.multicast_routing_entries, app_id=app_id)
progress_bar.update()
progress_bar.end()
return {"LoadedRoutingTablesToken": True}
def __call__(self, file_path, transceiver, machine, router_tables, has_ran,
placements):
"""
:param file_path: the file path to write the provenance data to
:param transceiver: the spinnman interface object
:param machine: the python representation of the spinnaker machine
:param router_tables: the router tables that have been generated
:param has_ran: token that states that the simulation has ran
:return: none
"""
if has_ran:
root = etree.Element("root")
router_file_path = os.path.join(file_path, "router_provenance.xml")
self._write_router_provenance_data(
root, router_tables, machine, transceiver)
writer = open(router_file_path, "w")
writer.write(etree.tostring(root, pretty_print=True))
progress = ProgressBar(placements.n_placements,
"Getting provenance data")
# retrieve provenance data from any cores that provide data
for placement in placements.placements:
if isinstance(placement.subvertex,
AbstractProvidesProvenanceData):
core_file_path = os.path.join(
file_path,
"Provanence_data_for_{}_{}_{}_{}.xml".format(
placement.subvertex.label,
placement.x, placement.y, placement.p))
placement.subvertex.write_provenance_data_in_xml(
core_file_path, transceiver, placement)
progress.update()
progress.end()
else:
raise exceptions.ConfigurationException(
"This function has been called before the simulation has ran."
" This is deemed an error, please rectify and try again")
def __call__(self, partitioned_graph, machine, file_path):
"""
:param partitioned_graph: the partitioned graph
:param machine: the machine
:return:
"""
progress_bar = ProgressBar(
(len(partitioned_graph.subvertices) +
len(partitioned_graph.subedges)) + 2,
"creating json constraints")
json_constraints_dictory_rep = list()
self._add_monitor_core_reserve(json_constraints_dictory_rep)
progress_bar.update()
self._add_extra_monitor_cores(json_constraints_dictory_rep, machine)
progress_bar.update()
self._search_graph_for_placement_constraints(
json_constraints_dictory_rep, partitioned_graph, machine,
progress_bar)
file_to_write = open(file_path, "w")
json.dump(json_constraints_dictory_rep, file_to_write)
file_to_write.close()
# validate the schema
partitioned_graph_schema_file_path = os.path.join(
os.path.dirname(file_format_schemas.__file__), "constraints.json"
)
# for debug purposes, read schema and validate
file_to_read = open(partitioned_graph_schema_file_path, "r")
partitioned_graph_schema = json.load(file_to_read)
jsonschema.validate(
json_constraints_dictory_rep, partitioned_graph_schema)
# complete progress bar
progress_bar.end()
return {'constraints': file_path}
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, file_routing_paths, partitioned_graph, placements,
machine):
# load the json files
file_routing_paths = self._handle_json_files(file_routing_paths)
progress_bar = ProgressBar(len(file_routing_paths),
"Converting to PACMAN routing paths")
# iterate though the path for each edge and create entries
for edge_id in file_routing_paths:
edge = partitioned_graph.get_subedge_with_label(edge_id)
# if the vertex is none, its a vertex with the special skills of
# needing no cores. therefore ignore
if edge is not None:
placement = placements.get_placement_of_subvertex(
edge.pre_subvertex)
self._create_entries_for_path(
edge_id, file_routing_paths[edge_id], None, placement.p,
partitioned_graph, machine, placements)
progress_bar.update()
progress_bar.end()
return {'routing_paths': self._multi_cast_routing_paths}
def __call__(
self, placements, tags, txrx, reports_states, app_data_folder):
progress_bar = ProgressBar(
len(list(placements.placements)), "Initialising buffers")
# Create the buffer manager
buffer_manager = BufferManager(
placements, tags, txrx, reports_states, app_data_folder)
for placement in placements.placements:
if isinstance(
placement.subvertex,
AbstractSendsBuffersFromHostPartitionedVertex):
if placement.subvertex.buffering_input():
buffer_manager.add_sender_vertex(placement.subvertex)
if isinstance(placement.subvertex, AbstractReceiveBuffersToHost):
if placement.subvertex.buffering_output():
buffer_manager.add_receiving_vertex(placement.subvertex)
progress_bar.update()
progress_bar.end()
return {"buffer_manager": buffer_manager}
def __call__(
self, machine, partitionable_graph=None, partitioned_graph=None):
"""
:param partitionable_graph:
:param partitioned_graph:
:param machine:
:return:
"""
if partitionable_graph is not None:
# Go through the groups and allocate keys
progress_bar = ProgressBar(
(len(partitionable_graph.vertices) + len(list(machine.chips))),
"Allocating virtual identifiers")
elif partitioned_graph is not None:
# Go through the groups and allocate keys
progress_bar = ProgressBar(
(len(partitioned_graph.subvertices) +
len(list(machine.chips))),
"Allocating virtual identifiers")
else:
progress_bar = ProgressBar(len(list(machine.chips)),
"Allocating virtual identifiers")
# allocate standard ids for real chips
for chip in machine.chips:
expected_chip_id = (chip.x << 8) + chip.y
self._allocate_elements(expected_chip_id, 1)
progress_bar.update()
if partitionable_graph is not None:
# allocate ids for virtual chips
for vertex in partitionable_graph.vertices:
if isinstance(vertex, AbstractVirtualVertex):
chip_id_x, chip_id_y = self._allocate_id()
vertex.set_virtual_chip_coordinates(chip_id_x, chip_id_y)
machine_algorithm_utilities.create_virtual_chip(
machine, vertex)
progress_bar.update()
progress_bar.end()
elif partitioned_graph is not None:
# allocate ids for virtual chips
for vertex in partitioned_graph.subvertices:
if isinstance(vertex, AbstractVirtualVertex):
chip_id_x, chip_id_y = self._allocate_id()
vertex.set_virtual_chip_coordinates(chip_id_x, chip_id_y)
machine_algorithm_utilities.create_virtual_chip(
machine, vertex)
progress_bar.update()
progress_bar.end()
return {"machine": machine}
def __call__(self, placements, machine, partitioned_graph, k=1, l=0, m=0,
bw_per_route_entry=BW_PER_ROUTE_ENTRY, max_bw=MAX_BW):
""" Find routes between the subedges with the allocated information,
placed in the given places
:param placements: The placements of the subedges
:type placements:\
:py:class:`pacman.model.placements.placements.Placements`
:param machine: The machine through which the routes are to be found
:type machine: :py:class:`spinn_machine.machine.Machine`
:param partitioned_graph: the partitioned_graph object
:type partitioned_graph:\
:py:class:`pacman.partitioned_graph.partitioned_graph.PartitionedGraph`
:return: The discovered routes
:rtype:\
:py:class:`pacman.model.routing_tables.multicast_routing_tables.MulticastRoutingTables`
:raise pacman.exceptions.PacmanRoutingException: If something\
goes wrong with the routing
"""
# set up basic data structures
self._routing_paths = MulticastRoutingPaths()
self._k = k
self._l = l
self._m = m
self._bw_per_route_entry = bw_per_route_entry
self._max_bw = max_bw
self._machine = machine
nodes_info = self._initiate_node_info(machine)
dijkstra_tables = self._initiate_dijkstra_tables(machine)
self._update_all_weights(nodes_info, machine)
# each subvertex represents a core in the board
progress = ProgressBar(len(list(placements.placements)),
"Creating routing entries")
for placement in placements.placements:
subvert = placement.subvertex
out_going_sub_edges = \
partitioned_graph.outgoing_subedges_from_subvertex(subvert)
out_going_sub_edges = filter(
lambda edge: isinstance(edge, MultiCastPartitionedEdge),
out_going_sub_edges)
dest_chips = set()
subedges_to_route = list()
for subedge in out_going_sub_edges:
destination_subvetex = subedge.post_subvertex
destination_placement = \
placements.get_placement_of_subvertex(destination_subvetex)
chip = machine.get_chip_at(destination_placement.x,
destination_placement.y)
dest_chips.add((chip.x, chip.y))
subedges_to_route.append(subedge)
if len(dest_chips) != 0:
self._update_all_weights(nodes_info, machine)
self._reset_tables(dijkstra_tables)
dijkstra_tables[
(placement.x, placement.y)]["activated?"] = True
dijkstra_tables[(placement.x, placement.y)]["lowest cost"] = 0
self._propagate_costs_until_reached_destinations(
dijkstra_tables, nodes_info, dest_chips, placement.x,
placement.y)
for subedge in subedges_to_route:
dest = subedge.post_subvertex
dest_placement = placements.get_placement_of_subvertex(dest)
self._retrace_back_to_source(
dest_placement.x, dest_placement.y, dijkstra_tables,
dest_placement.p, subedge, nodes_info, placement.p)
progress.update()
progress.end()
return {'routing_paths': self._routing_paths}
def host_based_data_specification_execution(
hostname, placements, graph_mapper, write_text_specs,
application_data_runtime_folder, machine,
report_default_directory, dsg_targets):
"""
:param hostname:
:param placements:
:param graph_mapper:
:param write_text_specs:
:param application_data_runtime_folder:
:param machine:
:return:
"""
next_position_tracker = dict()
space_available_tracker = dict()
processor_to_app_data_base_address = dict()
placement_to_application_data_files = dict()
# create a progress bar for end users
progress_bar = ProgressBar(len(list(placements.placements)),
"Executing data specifications")
for placement in placements.placements:
associated_vertex = graph_mapper.get_vertex_from_subvertex(
placement.subvertex)
# if the vertex can generate a DSG, call it
if isinstance(associated_vertex, AbstractDataSpecableVertex):
placement_to_application_data_files[
(placement.x, placement.y, placement.p,
associated_vertex.label)] = list()
data_spec_file_paths = dsg_targets[placement.subvertex]
for data_spec_file_path in data_spec_file_paths:
app_data_file_path = \
associated_vertex.get_application_data_file_path(
placement.x, placement.y, placement.p, hostname,
application_data_runtime_folder)
# update application data file path tracker
placement_to_application_data_files[
(placement.x, placement.y, placement.p,
associated_vertex.label)].append(
app_data_file_path)
# build writers
data_spec_reader = FileDataReader(data_spec_file_path)
data_writer = FileDataWriter(app_data_file_path)
# locate current memory requirement
chip = machine.get_chip_at(placement.x, placement.y)
next_position = chip.sdram.user_base_address
space_available = chip.sdram.size
placement_key = (placement.x, placement.y)
if placement_key in next_position_tracker:
next_position = next_position_tracker[placement_key]
space_available = \
space_available_tracker[placement_key]
# generate a file writer for DSE report (app pointer table)
report_writer = None
if write_text_specs:
new_report_directory = os.path.join(
report_default_directory, "data_spec_text_files")
if not os.path.exists(new_report_directory):
os.mkdir(new_report_directory)
file_name = "{}_DSE_report_for_{}_{}_{}.txt".format(
hostname, placement.x, placement.y, placement.p)
report_file_path = os.path.join(new_report_directory,
file_name)
report_writer = FileDataWriter(report_file_path)
# generate data spec executor
host_based_data_spec_executor = DataSpecificationExecutor(
data_spec_reader, data_writer, space_available,
report_writer)
# update memory calc and run data spec executor
bytes_used_by_spec = 0
bytes_written_by_spec = 0
try:
bytes_used_by_spec, bytes_written_by_spec = \
host_based_data_spec_executor.execute()
except exceptions.DataSpecificationException as e:
logger.error(
"Error executing data specification for {}"
.format(associated_vertex))
raise e
# update base address mapper
processor_mapping_key = \
(placement.x, placement.y, placement.p,
associated_vertex.label)
processor_to_app_data_base_address[
processor_mapping_key] = {
'start_address': next_position,
'memory_used': bytes_used_by_spec,
'memory_written': bytes_written_by_spec}
next_position_tracker[placement_key] = (next_position +
bytes_used_by_spec)
space_available_tracker[placement_key] = \
(space_available - bytes_used_by_spec)
# update the progress bar
progress_bar.update()
# close the progress bar
progress_bar.end()
return {'processor_to_app_data_base_address':
processor_to_app_data_base_address,
'placement_to_app_data_files':
placement_to_application_data_files}
def __call__(self, partitioned_graph, file_path):
"""
:param partitioned_graph:
:param folder_path:
:return:
"""
progress_bar = ProgressBar(len(partitioned_graph.subvertices), "Converting to json partitioned graph")
# write basic stuff
json_graph_dictory_rep = dict()
# write vertices data
vertices_resources = dict()
json_graph_dictory_rep["vertices_resources"] = vertices_resources
edges_resources = defaultdict()
json_graph_dictory_rep["edges"] = edges_resources
for vertex in partitioned_graph.subvertices:
# handle external devices
if isinstance(vertex, AbstractVirtualVertex):
vertex_resources = dict()
vertices_resources[id(vertex)] = vertex_resources
vertex_resources["cores"] = 0
# handle tagged vertices
elif len(utility_calls.locate_constraints_of_type(vertex.constraints, AbstractTagAllocatorConstraint)) != 0:
# handle the edge between the tag-able vertex and the fake
# vertex
hyper_edge_dict = dict()
edges_resources[hashlib.md5(vertex.label).hexdigest()] = hyper_edge_dict
hyper_edge_dict["source"] = str(id(vertex))
hyper_edge_dict["sinks"] = [hashlib.md5(vertex.label).hexdigest()]
hyper_edge_dict["weight"] = 1.0
hyper_edge_dict["type"] = "FAKE_TAG_EDGE"
# add the tag-able vertex
vertex_resources = dict()
vertices_resources[id(vertex)] = vertex_resources
vertex_resources["cores"] = DEFAULT_NOUMBER_OF_CORES_USED_PER_PARTITIONED_VERTEX
vertex_resources["sdram"] = int(vertex.resources_required.sdram.get_value())
# add fake vertex
vertex_resources = dict()
vertices_resources[hashlib.md5(vertex.label).hexdigest()] = vertex_resources
vertex_resources["cores"] = 0
vertex_resources["sdram"] = 0
# handle standard vertices
else:
vertex_resources = dict()
vertices_resources[id(vertex)] = vertex_resources
vertex_resources["cores"] = DEFAULT_NOUMBER_OF_CORES_USED_PER_PARTITIONED_VERTEX
vertex_resources["sdram"] = int(vertex.resources_required.sdram.get_value())
vertex_outgoing_partitions = partitioned_graph.outgoing_edges_partitions_from_vertex(vertex)
# handle the vertex edges
for vertex_partition in vertex_outgoing_partitions:
hyper_edge_dict = dict()
edges_resources["{}:{}".format(id(vertex), vertex_partition)] = hyper_edge_dict
hyper_edge_dict["source"] = str(id(vertex))
sinks_string = []
for edge in vertex_outgoing_partitions[vertex_partition].edges:
sinks_string.append(str(id(edge.post_subvertex)))
hyper_edge_dict["sinks"] = sinks_string
hyper_edge_dict["weight"] = 1.0
hyper_edge_dict["type"] = vertex_outgoing_partitions[vertex_partition].type.name.lower()
progress_bar.update()
file_to_write = open(file_path, "w")
json.dump(json_graph_dictory_rep, file_to_write)
file_to_write.close()
# validate the schema
partitioned_graph_schema_file_path = os.path.join(
os.path.dirname(file_format_schemas.__file__), "partitioned_graph.json"
)
file_to_read = open(partitioned_graph_schema_file_path, "r")
partitioned_graph_schema = json.load(file_to_read)
jsonschema.validate(json_graph_dictory_rep, partitioned_graph_schema)
progress_bar.end()
return {"partitioned_graph": file_path}
def __call__(
self, partitioned_graph, user_create_database, tags,
runtime, machine, time_scale_factor, machine_time_step,
partitionable_graph, graph_mapper, placements, routing_infos,
router_tables, execute_mapping, database_directory):
self._writer = DatabaseWriter(database_directory)
# add database generation if requested
self._needs_database = \
helpful_functions.auto_detect_database(partitioned_graph)
if ((self._user_create_database == "None" and self._needs_database) or
self._user_create_database == "True"):
database_progress = ProgressBar(10, "Creating database")
self._writer.add_system_params(
time_scale_factor, machine_time_step, runtime)
database_progress.update()
self._writer.add_machine_objects(machine)
database_progress.update()
self._writer.add_partitioned_vertices(
partitioned_graph, graph_mapper, partitionable_graph)
database_progress.update()
self._writer.add_placements(placements, partitioned_graph)
database_progress.update()
self._writer.add_routing_infos(
routing_infos, partitioned_graph)
database_progress.update()
self._writer.add_routing_tables(router_tables)
database_progress.update()
self._writer.add_tags(partitioned_graph, tags)
database_progress.update()
if execute_mapping:
self._writer.create_atom_to_event_id_mapping(
graph_mapper=graph_mapper,
partitionable_graph=partitionable_graph,
partitioned_graph=partitioned_graph,
routing_infos=routing_infos)
database_progress.update()
database_progress.update()
database_progress.end()
return {"database_interface": self,
"database_file_path": self.database_file_path}
def __call__(self, machine, file_path):
"""
:param machine:
:param file_path:
:return:
"""
progress_bar = ProgressBar(
((machine.max_chip_x + 1) * (machine.max_chip_y + 1)) + 2,
"Converting to json machine")
# write basic stuff
json_dictory_rep = dict()
json_dictory_rep['width'] = machine.max_chip_x + 1
json_dictory_rep['height'] = machine.max_chip_y + 1
json_dictory_rep['chip_resources'] = dict()
json_dictory_rep['chip_resources']['cores'] = CHIP_HOMOGENIOUS_CORES
json_dictory_rep['chip_resources']['sdram'] = CHIP_HOMOGENIOUS_SDRAM
json_dictory_rep['chip_resources']['sram'] = CHIP_HOMOGENIOUS_SRAM
json_dictory_rep['chip_resources']["router_entries"] = \
ROUTER_HOMOGENIOUS_ENTRIES
json_dictory_rep['chip_resources']['tags'] = CHIP_HOMOGENIOUS_TAGS
# handle exceptions
json_dictory_rep['dead_chips'] = list()
json_dictory_rep['dead_links'] = list()
chip_resource_exceptions = defaultdict()
# write dead chips
for x_coord in range(0, machine.max_chip_x + 1):
for y_coord in range(0, machine.max_chip_y + 1):
if (not machine.is_chip_at(x_coord, y_coord) or
machine.get_chip_at(x_coord, y_coord).virtual):
json_dictory_rep['dead_chips'].append([x_coord, y_coord])
else:
# write dead links
for link_id in range(0, ROUTER_MAX_NUMBER_OF_LINKS):
router = machine.get_chip_at(x_coord, y_coord).router
if not router.is_link(link_id):
json_dictory_rep['dead_links'].append(
[x_coord, y_coord, "{}".format(
constants.EDGES(link_id).name.lower())])
self._check_for_exceptions(
json_dictory_rep, x_coord, y_coord, machine,
chip_resource_exceptions)
progress_bar.update()
# convert dict into list
chip_resouce_exceptions_list = []
for (chip_x, chip_y) in chip_resource_exceptions:
chip_resouce_exceptions_list.append(
[chip_x, chip_y, chip_resource_exceptions[(chip_x, chip_y)]])
progress_bar.update()
# store exceptions into json form
json_dictory_rep['chip_resource_exceptions'] = \
chip_resouce_exceptions_list
# dump to json file
file_to_write = open(file_path, "w")
json.dump(json_dictory_rep, file_to_write)
file_to_write.close()
# validate the schema
machine_schema_file_path = os.path.join(
os.path.dirname(file_format_schemas.__file__), "machine.json"
)
file_to_read = open(machine_schema_file_path, "r")
machine_schema = json.load(file_to_read)
jsonschema.validate(
json_dictory_rep, machine_schema)
# update and complete progress bar
progress_bar.update()
progress_bar.end()
return {'file_machine': file_path}
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):
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, processor_to_app_data_base_address, transceiver,
placement_to_app_data_files, app_id, verify=False):
# go through the placements and see if there's any application data to
# load
progress_bar = ProgressBar(len(placement_to_app_data_files),
"Loading application data onto the machine")
for (x, y, p, label) in placement_to_app_data_files:
logger.debug(
"loading application data for vertex {}".format(label))
key = (x, y, p, label)
start_address = \
processor_to_app_data_base_address[key]['start_address']
memory_written = \
processor_to_app_data_base_address[key]['memory_written']
memory_used = \
processor_to_app_data_base_address[key]['memory_used']
# Allocate the SDRAM requirement and replace the start address
# assigned via the DSE
start_address_malloced = \
transceiver.malloc_sdram(x, y, memory_used, app_id)
processor_to_app_data_base_address[key]['start_address'] = \
start_address_malloced
# set start address to be that of the allocated version
start_address = start_address_malloced
application_file_paths = placement_to_app_data_files[key]
for file_path_for_application_data in application_file_paths:
application_data_file_reader = SpinnmanFileDataReader(
file_path_for_application_data)
logger.debug(
"writing application data for vertex {}".format(label))
transceiver.write_memory(
x, y, start_address, application_data_file_reader,
memory_written)
application_data_file_reader.close()
if verify:
application_data_file_reader = SpinnmanFileDataReader(
file_path_for_application_data)
all_data = application_data_file_reader.readall()
read_data = transceiver.read_memory(
x, y, start_address, memory_written)
if read_data != all_data:
raise Exception("Miss Write of {}, {}, {}, {}"
.format(x, y, p, start_address))
application_data_file_reader.close()
# update user 0 so that it points to the start of the
# applications data region on SDRAM
logger.debug(
"writing user 0 address for vertex {}".format(label))
user_o_register_address = \
transceiver.get_user_0_register_address_from_core(x, y, p)
transceiver.write_memory(
x, y, user_o_register_address, start_address)
progress_bar.update()
progress_bar.end()
return {"LoadedApplicationDataToken": True}