Source code for pacman.operations.router_algorithms.basic_dijkstra_routing

# Copyright (c) 2017-2019 The University of Manchester
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# GNU General Public License for more details.
# You should have received a copy of the GNU General Public License
# along with this program.  If not, see <>.

import logging
import sys
from spinn_utilities.log import FormatAdapter
from spinn_utilities.progress_bar import ProgressBar, DummyProgressBar
from pacman.exceptions import PacmanRoutingException
from pacman.model.graphs.common import EdgeTrafficType
from pacman.model.routing_table_by_partition import (
    MulticastRoutingTableByPartition, MulticastRoutingTableByPartitionEntry)

logger = FormatAdapter(logging.getLogger(__name__))
infinity = float("inf")

class _NodeInfo(object):
    :ivar list(~spinn_machine.Link) neighbours:
    :ivar list(float) bws:
    :ivar list(float) weights:
    __slots__ = ["neighbours", "bws", "weights"]

    def __init__(self):
        self.neighbours = list()
        self.bws = list()
        self.weights = list()

    def neighweights(self):
        return zip(self.neighbours, self.weights)

class _DijkstraInfo(object):
    __slots__ = ["activated", "cost"]

    def __init__(self):
        self.activated = False
        self.cost = None

[docs]class BasicDijkstraRouting(object): """ An routing algorithm that can find routes for edges between vertices\ in a machine graph that have been placed on a machine by the use of a\ Dijkstra shortest path algorithm. """ __slots__ = [ # the routing path objects used to be returned to the work flow "_routing_paths", # parameter to control ........... "_bw_per_route_entry", # parameter to control ........... "_max_bw", # the SpiNNMachine object used within the system. "_machine" ] BW_PER_ROUTE_ENTRY = 0.01 MAX_BW = 250
[docs] def __call__(self, placements, machine, machine_graph, bw_per_route_entry=BW_PER_ROUTE_ENTRY, max_bw=MAX_BW, use_progress_bar=True): """ Find routes between the edges with the allocated information, placed in the given places :param Placements placements: The placements of the edges :param ~spinn_machine.Machine machine: The machine through which the routes are to be found :param MachineGraph machine_graph: the machine_graph object :param bool use_progress_bar: whether to show a progress bar :return: The discovered routes :rtype: MulticastRoutingTables :raise PacmanRoutingException: If something goes wrong with the routing """ # set up basic data structures self._routing_paths = MulticastRoutingTableByPartition() self._bw_per_route_entry = bw_per_route_entry self._max_bw = max_bw self._machine = machine nodes_info = self._initiate_node_info(machine) tables = self._initiate_dijkstra_tables(machine) self._update_all_weights(nodes_info) # each vertex represents a core in the board pb_factory = ProgressBar if use_progress_bar else DummyProgressBar progress = pb_factory(placements.n_placements, "Creating routing entries") for placement in progress.over(placements.placements): self._route(placement, placements, machine, machine_graph, nodes_info, tables) return self._routing_paths
def _route(self, placement, placements, machine, graph, node_info, tables): """ :param Placement placement: :param Placements placements: :param ~spinn_machine.Machine machine: :param MachineGraph graph: :param dict(tuple(int,int),_NodeInfo) node_info: :param dict(tuple(int,int),_DijkstraInfo) tables: """ # pylint: disable=too-many-arguments out_going_edges = ( edge for edge in graph.get_edges_starting_at_vertex(placement.vertex) if edge.traffic_type == EdgeTrafficType.MULTICAST) dest_chips = set() edges_to_route = list() for edge in out_going_edges: destination = edge.post_vertex dest_place = placements.get_placement_of_vertex(destination) chip = machine.get_chip_at(dest_place.x, dest_place.y) dest_chips.add((chip.x, chip.y)) edges_to_route.append(edge) if dest_chips: self._update_all_weights(node_info) self._reset_tables(tables) tables[placement.x, placement.y].activated = True tables[placement.x, placement.y].cost = 0 self._propagate_costs_until_reached_destinations( tables, node_info, dest_chips, placement.x, placement.y) for edge in edges_to_route: dest = edge.post_vertex dest_placement = placements.get_placement_of_vertex(dest) self._retrace_back_to_source( dest_placement, tables, edge, node_info, placement.p, graph) def _initiate_node_info(self, machine): """ Set up a dictionary which contains data for each chip in the\ machine :param ~spinn_machine.Machine machine: the machine object :return: nodes_info dictionary :rtype: dict(tuple(int,int),_NodeInfo) """ nodes_info = dict() for chip in machine.chips: # get_neighbours should return a list of # dictionaries of 'x' and 'y' values node = _NodeInfo() for source_id in range(6): n = chip.router.get_link(source_id) node.neighbours.append(n) node.weights.append(infinity) node.bws.append(None if n is None else self._max_bw) nodes_info[chip.x, chip.y] = node return nodes_info @staticmethod def _initiate_dijkstra_tables(machine): """ Set up the Dijkstra's table which includes if you've reached a\ given node :param ~spinn_machine.Machine machine: the machine object :return: the Dijkstra's table dictionary :rtype: dict(tuple(int,int),_DijkstraInfo) """ # Holds all the information about nodes within one full run of # Dijkstra's algorithm tables = dict() for chip in machine.chips: tables[chip.x, chip.y] = _DijkstraInfo() return tables def _update_all_weights(self, nodes_info): """ Change the weights of the neighbouring nodes :param dict(tuple(int,int),_NodeInfo) nodes_info: the node info dictionary """ for key in nodes_info: if nodes_info[key] is not None: self._update_neighbour_weights(nodes_info, key) def _update_neighbour_weights(self, nodes_info, key): """ Change the weights of the neighbouring nodes :param dict(tuple(int,int),_NodeInfo) nodes_info: the node info dictionary :param tuple(int,int) key: the identifier to the object in `nodes_info` """ for n, neighbour in enumerate(nodes_info[key].neighbours): if neighbour is not None: nodes_info[key].weights[n] = 1 @staticmethod def _reset_tables(tables): """ Reset the Dijkstra tables for a new path search :param dict(tuple(int,int),_DijkstraInfo) tables: the dictionary object for the Dijkstra-tables """ for key in tables: tables[key] = _DijkstraInfo() def _propagate_costs_until_reached_destinations( self, tables, nodes_info, dest_chips, x_source, y_source): """ Propagate the weights till the destination nodes of the source\ nodes are retraced :param dict(tuple(int,int),_DijkstraInfo) tables: the dictionary object for the Dijkstra-tables :param dict(tuple(int,int),_NodeInfo) nodes_info: the dictionary object for the nodes inside a route scope :param set(tuple(int,int)) dest_chips: :param int x_source: :param int y_source: :raise PacmanRoutingException: when the destination node could not be reached from this source node """ dest_chips_to_find = set(dest_chips) source = (x_source, y_source) dest_chips_to_find.discard(source) current = source # Iterate only if the destination node hasn't been activated while dest_chips_to_find: # PROPAGATE! for neighbour, weight in nodes_info[current].neighweights: # "neighbours" is a list of 6 links or None objects. There is # a None object where there is no connection to that neighbour if (neighbour is not None and not (neighbour.destination_x == x_source and neighbour.destination_y == y_source)): # These variables change with every look at a new neighbour self._update_neighbour( tables, neighbour, current, source, weight) # Set the next activated node as the deactivated node with the # lowest current cost current = self._minimum(tables) tables[current].activated = True dest_chips_to_find.discard(current) @staticmethod def _minimum(tables): """ :param dict(tuple(int,int),_DijkstraInfo) tables: :rtype: tuple(int,int) """ # This is the lowest cost across ALL deactivated nodes in the graph. lowest_cost = sys.maxsize lowest = None # Find the next node to be activated for key in tables: # Don't continue if the node hasn't even been touched yet if (tables[key].cost is not None and not tables[key].activated and tables[key].cost < lowest_cost): lowest_cost = tables[key].cost lowest = key # If there were no deactivated nodes with costs, but the destination # was not reached this iteration, raise an exception if lowest is None: raise PacmanRoutingException( "Destination could not be activated, ending run") return int(lowest[0]), int(lowest[1]) @staticmethod def _update_neighbour(tables, neighbour, current, source, weight): """ Update the lowest cost for each neighbour_xy of a node :param dict(tuple(int,int),_DijkstraInfo) tables: :param ~spinn_machine.Link neighbour: :param tuple(int,int) current: :param tuple(int,int) source: :param float weight: :raise PacmanRoutingException: when the algorithm goes to a node that doesn't exist in the machine or the node's cost was set too low. """ neighbour_xy = (neighbour.destination_x, neighbour.destination_y) if neighbour_xy not in tables: raise PacmanRoutingException( "Tried to propagate to ({}, {}), which is not in the" " graph: remove non-existent neighbours" .format(neighbour.destination_x, neighbour.destination_y)) chip_cost = tables[current].cost neighbour_cost = tables[neighbour_xy].cost # Only try to update if the neighbour_xy is within the graph and the # cost if the node hasn't already been activated and the lowest cost # if the new cost is less, or if there is no current cost. new_weight = float(chip_cost + weight) if (not tables[neighbour_xy].activated and (neighbour_cost is None or new_weight < neighbour_cost)): # update Dijkstra table tables[neighbour_xy].cost = new_weight if tables[neighbour_xy].cost == 0 and neighbour_xy != source: raise PacmanRoutingException( "!!!Cost of non-source node ({}, {}) was set to zero!!!" .format(neighbour.destination_x, neighbour.destination_y)) def _retrace_back_to_source( self, dest, tables, edge, nodes_info, source_processor, graph): """ :param Placement dest: Destination placement :param dict(tuple(int,int),_DijkstraInfo) tables: :param MachineEdge edge: :param dict(tuple(int,int),_NodeInfo) nodes_info: :param int source_processor: :param MachineGraph graph: :return: the next coordinates to look into :rtype: tuple(int, int) :raise PacmanRoutingException: when the algorithm doesn't find a next point to search from. AKA, the neighbours of a chip do not have a cheaper cost than the node itself, but the node is not the destination or when the algorithm goes to a node that's not considered in the weighted search. """ # Set the tracking node to the destination to begin with x, y = dest.x, dest.y routing_entry_route_processors = [] # if the processor is None, don't add to router path entry if dest.p is not None: routing_entry_route_processors.append(dest.p) routing_entry_route_links = None # build the multicast entry partitions = graph.get_multicast_edge_partitions_starting_at_vertex( edge.pre_vertex) prev_entry = None for partition in partitions: if edge in partition: entry = MulticastRoutingTableByPartitionEntry( out_going_links=routing_entry_route_links, outgoing_processors=routing_entry_route_processors) self._routing_paths.add_path_entry( entry, dest.x, dest.y, partition) prev_entry = entry while tables[x, y].cost != 0: for idx, neighbour in enumerate(nodes_info[x, y].neighbours): if neighbour is not None: n_xy = (neighbour.destination_x, neighbour.destination_y) # Only check if it can be a preceding node if it actually # exists if n_xy not in tables: raise PacmanRoutingException( "Tried to trace back to node not in " "graph: remove non-existent neighbours") if tables[n_xy].cost is not None: x, y, prev_entry, added = self._create_routing_entry( n_xy, tables, idx, nodes_info, x, y, prev_entry, edge, graph) if added: break else: raise PacmanRoutingException( "Iterated through all neighbours of tracking node but" " did not find a preceding node! Consider increasing " "acceptable discrepancy between sought traceback cost" " and actual cost at node. Terminating...") prev_entry.incoming_processor = source_processor return x, y def _create_routing_entry( self, neighbour_xy, tables, neighbour_index, nodes_info, x, y, previous_entry, edge, graph): """ Create a new routing entry :param tuple(int,int) neighbour_xy: :param dict(tuple(int,int),_DijkstraInfo) tables: :param int neighbour_index: :param dict(tuple(int,int),_NodeInfo) nodes_info: :param int x: :param int y: :param MulticastRoutingTableByPartitionEntry previous_entry: :param MachineEdge edge: :param MachineGraph graph: :return: x, y, previous_entry, made_an_entry :rtype: tuple(int, int, MulticastRoutingTableByPartitionEntry, bool) :raise PacmanRoutingException: when the bandwidth of a router is beyond expected parameters """ # Set the direction of the routing other_entry as that which is from # the preceding node to the current tracking node. # neighbour_xy is the 'old' coordinates since it is from the preceding # node. x and y are the 'new' coordinates since they are where the # router should send the packet to. dec_direction = self._get_reverse_direction(neighbour_index) made_an_entry = False neighbour_weight = nodes_info[neighbour_xy].weights[dec_direction] chip_sought_cost = tables[x, y].cost - neighbour_weight neighbours_lowest_cost = tables[neighbour_xy].cost if (neighbours_lowest_cost is not None and self._close_enough(neighbours_lowest_cost, chip_sought_cost)): # build the multicast entry partns = graph.get_multicast_edge_partitions_starting_at_vertex( edge.pre_vertex) entry = None for partition in partns: if edge in partition: entry = MulticastRoutingTableByPartitionEntry( dec_direction, None) previous_entry.incoming_link = neighbour_index # add entry for next hop going backwards into path self._routing_paths.add_path_entry( entry, neighbour_xy[0], neighbour_xy[1], partition) previous_entry = entry made_an_entry = True # Finally move the tracking node x, y = neighbour_xy return x, y, previous_entry, made_an_entry @staticmethod def _close_enough(v1, v2, delta=0.00000000001): """ :param float v1: :param float v2: :param float delta: How close values have to be to be "equal" """ return abs(v1 - v2) < delta @staticmethod def _get_reverse_direction(neighbour_position): """ Determine the direction of a link to go down :param int neighbour_position: the position the neighbour is at :return: The position of the opposite link :rtype: int """ if neighbour_position == 0: return 3 elif neighbour_position == 1: return 4 elif neighbour_position == 2: return 5 elif neighbour_position == 3: return 0 elif neighbour_position == 4: return 1 elif neighbour_position == 5: return 2 return None