Source code for pacman.operations.placer_algorithms.connective_based_placer

# 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
from spinn_utilities.log import FormatAdapter
from spinn_utilities.progress_bar import ProgressBar
from pacman.model.constraints.placer_constraints import (
from pacman.model.placements import Placements
from pacman.operations.placer_algorithms import RadialPlacer
from pacman.utilities.algorithm_utilities.placer_algorithm_utilities import (
    sort_vertices_by_known_constraints, get_same_chip_vertex_groups)
from pacman.utilities.utility_calls import locate_constraints_of_type
from pacman.utilities.utility_objs import ResourceTracker

logger = FormatAdapter(logging.getLogger(__name__))

class ConnectiveBasedPlacer(RadialPlacer):
    """ A radial algorithm that can place a machine graph onto a\
        machine using a circle out behaviour from a Ethernet at a given point\
        and which will place things that are most connected closest to each\

    __slots__ = []

[docs] def __call__(self, machine_graph, machine, plan_n_timesteps): """ :param MachineGraph machine_graph: The machine_graph to place :param ~spinn_machine.Machine machine: The machine with respect to which to partition the application graph :param int plan_n_timesteps: number of timesteps to plan for :return: A set of placements :rtype: ~pacman.model.placements.Placements :raise PacmanPlaceException: If something goes wrong with the placement """ # check that the algorithm can handle the constraints self._check_constraints(machine_graph.vertices) # Sort the vertices into those with and those without # placement constraints placements = Placements() constrained = list() unconstrained = set() for vertex in machine_graph.vertices: if locate_constraints_of_type( vertex.constraints, AbstractPlacerConstraint): constrained.append(vertex) else: unconstrained.add(vertex) # Iterate over constrained vertices and generate placements progress = ProgressBar( machine_graph.n_vertices, "Placing graph vertices") resource_tracker = ResourceTracker( machine, plan_n_timesteps, self._generate_radial_chips(machine)) constrained = sort_vertices_by_known_constraints(constrained) vertices_on_same_chip = get_same_chip_vertex_groups(machine_graph) for vertex in progress.over(constrained, False): self._place_vertex( vertex, resource_tracker, machine, placements, vertices_on_same_chip, machine_graph) while unconstrained: # Place the subgraph with the overall most connected vertex max_connected_vertex = self._find_max_connected_vertex( unconstrained, machine_graph) self._place_unconstrained_subgraph( max_connected_vertex, machine_graph, unconstrained, machine, placements, resource_tracker, progress, vertices_on_same_chip) # finished, so stop progress bar and return placements progress.end() return placements
def _place_unconstrained_subgraph( self, starting_vertex, machine_graph, unplaced_vertices, machine, placements, resource_tracker, progress, vertices_on_same_chip): # pylint: disable=too-many-arguments # Keep track of all unplaced_vertices connected to the currently # placed ones to_do = set() to_do.add(starting_vertex) while to_do: # Find the vertex most connected of the currently-to-be-placed ones vertex = self._find_max_connected_vertex(to_do, machine_graph) # Place the vertex self._place_vertex( vertex, resource_tracker, machine, placements, vertices_on_same_chip, machine_graph) progress.update() # Remove from collections of unplaced_vertices to work on unplaced_vertices.remove(vertex) to_do.remove(vertex) # Add all unplaced_vertices connected to this one to the set for edge in machine_graph.get_edges_ending_at_vertex(vertex): if edge.pre_vertex in unplaced_vertices: to_do.add(edge.pre_vertex) for edge in machine_graph.get_edges_starting_at_vertex(vertex): if edge.post_vertex in unplaced_vertices: to_do.add(edge.post_vertex) @staticmethod def _find_max_connected_vertex(vertices, graph): max_connected_vertex = None max_weight = 0 for vertex in vertices: in_weight = sum( edge.pre_vertex.vertex_slice.n_atoms for edge in graph.get_edges_starting_at_vertex(vertex)) out_weight = sum( edge.pre_vertex.vertex_slice.n_atoms for edge in graph.get_edges_ending_at_vertex(vertex)) weight = in_weight + out_weight if max_connected_vertex is None or weight > max_weight: max_connected_vertex = vertex max_weight = weight return max_connected_vertex