def __call__(self, machine_graph, machine, plan_n_timesteps, placements):
        progress_bar = ProgressBar(7, "Routing")

        vertices_resources, nets, net_names = \
            convert_to_rig_graph_pure_mc(machine_graph, plan_n_timesteps)
        progress_bar.update()

        rig_machine = convert_to_rig_machine(machine)
        progress_bar.update()

        rig_constraints = create_rig_machine_constraints(machine)
        progress_bar.update()

        rig_constraints.extend(create_rig_graph_constraints(
            machine_graph, machine))
        progress_bar.update()

        rig_placements, rig_allocations = convert_to_rig_placements(
            placements, machine)
        progress_bar.update()

        rig_routes = route(
            vertices_resources, nets, rig_machine, rig_constraints,
            rig_placements, rig_allocations, "cores")
        rig_routes = {
            name: rig_routes[net] for net, name in iteritems(net_names)}
        progress_bar.update()

        routes = convert_from_rig_routes(rig_routes)
        progress_bar.update()
        progress_bar.end()

        return routes
示例#2
0
    def __call__(self, partitioned_graph, machine):
        progress_bar = ProgressBar(9, "Placing and Routing")

        vertices_resources, nets, net_names = \
            rig_converters.convert_to_rig_partitioned_graph(
                partitioned_graph)
        progress_bar.update()

        rig_machine = rig_converters.convert_to_rig_machine(machine)
        progress_bar.update()

        rig_constraints = rig_converters.create_rig_machine_constraints(
            machine)
        progress_bar.update()

        rig_constraints.extend(
            rig_converters.create_rig_partitioned_graph_constraints(
                partitioned_graph, rig_machine))
        progress_bar.update()

        rig_placements = place(vertices_resources, nets, rig_machine,
                               rig_constraints)
        progress_bar.update()

        rig_allocations = allocate(vertices_resources, nets, rig_machine,
                                   rig_constraints, rig_placements)
        progress_bar.update()

        rig_routes = route(vertices_resources, nets, rig_machine,
                           rig_constraints, rig_placements, rig_allocations,
                           "cores")
        rig_routes = {
            name: rig_routes[net]
            for net, name in net_names.iteritems()
        }
        progress_bar.update()

        placements = rig_converters.convert_from_rig_placements(
            rig_placements, rig_allocations, partitioned_graph)
        progress_bar.update()
        routes = rig_converters.convert_from_rig_routes(
            rig_routes, partitioned_graph)
        progress_bar.update()
        progress_bar.end()

        return {"placements": placements, "routing_paths": routes}
def make_routing_tables():
    # Construct the vertices
    vertex_a = [object() for _ in range(17*2)]
    vertex_b = [object() for _ in range(2056)]
    vertex_c = [object() for _ in range(17*4)]

    # Construct the nets
    nets = list()

    # Fan out from A
    for a, bs in zip(vertex_a,
                     share_list(vertex_b, len(vertex_a))):
        nets.extend(Net(a, b) for b in bs)

    # Fan in to C
    for c, bs in zip(vertex_c,
                     share_list(vertex_b, len(vertex_c))):
        nets.extend(Net(b, c) for b in bs)

    # Construct constraints that place elements of vertex A together on the
    # same chip
    constraints = list()
    for aa in share_list(vertex_a, 2):
        constraints.append(SameChipConstraint(aa))

    # Add constraints that place elements of vertex C together on the same
    # chip.
    for cs in share_list(vertex_c, 4):
        constraints.append(SameChipConstraint(cs))

    # Each vertex will require 1 core
    vertices_resources = {a: {Cores: 1} for a in vertex_a}
    vertices_resources.update({b: {Cores: 1} for b in vertex_b})
    vertices_resources.update({c: {Cores: 1} for c in vertex_c})

    # Construct a faux-machine on which to place
    machine = Machine(12, 12)

    # Place and route the net
    placements = place(vertices_resources, nets, machine, constraints)
    allocations = allocate(vertices_resources, nets, machine, constraints,
                           placements)
    routes = route(vertices_resources, nets, machine, constraints, placements,
                   allocations)
示例#4
0
def make_routing_tables():
    # Construct the vertices
    vertex_a = [object() for _ in range(17 * 2)]
    vertex_b = [object() for _ in range(2056)]
    vertex_c = [object() for _ in range(17 * 4)]

    # Construct the nets
    nets = list()

    # Fan out from A
    for a, bs in zip(vertex_a, share_list(vertex_b, len(vertex_a))):
        nets.extend(Net(a, b) for b in bs)

    # Fan in to C
    for c, bs in zip(vertex_c, share_list(vertex_b, len(vertex_c))):
        nets.extend(Net(b, c) for b in bs)

    # Construct constraints that place elements of vertex A together on the
    # same chip
    constraints = list()
    for aa in share_list(vertex_a, 2):
        constraints.append(SameChipConstraint(aa))

    # Add constraints that place elements of vertex C together on the same
    # chip.
    for cs in share_list(vertex_c, 4):
        constraints.append(SameChipConstraint(cs))

    # Each vertex will require 1 core
    vertices_resources = {a: {Cores: 1} for a in vertex_a}
    vertices_resources.update({b: {Cores: 1} for b in vertex_b})
    vertices_resources.update({c: {Cores: 1} for c in vertex_c})

    # Construct a faux-machine on which to place
    machine = Machine(12, 12)

    # Place and route the net
    placements = place(vertices_resources, nets, machine, constraints)
    allocations = allocate(vertices_resources, nets, machine, constraints,
                           placements)
    routes = route(vertices_resources, nets, machine, constraints, placements,
                   allocations)
示例#5
0
    def __call__(self, machine_graph, machine, placements):
        progress_bar = ProgressBar(7, "Routing")

        vertices_resources, nets, net_names = \
            rig_converters.convert_to_rig_graph_pure_mc(machine_graph)
        progress_bar.update()

        rig_machine = rig_converters.convert_to_rig_machine(machine)
        progress_bar.update()

        rig_constraints = rig_converters.create_rig_machine_constraints(
            machine)
        progress_bar.update()

        rig_constraints.extend(
            rig_converters.create_rig_graph_constraints(
                machine_graph, machine))
        progress_bar.update()

        rig_placements, rig_allocations = \
            rig_converters.convert_to_rig_placements(placements, machine)
        progress_bar.update()

        rig_routes = route(vertices_resources, nets, rig_machine,
                           rig_constraints, rig_placements, rig_allocations,
                           "cores")
        rig_routes = {
            name: rig_routes[net]
            for net, name in iteritems(net_names)
        }
        progress_bar.update()

        routes = rig_converters.convert_from_rig_routes(rig_routes)
        progress_bar.update()
        progress_bar.end()

        return routes
示例#6
0
def make_routing_tables():
    # Create a perfect SpiNNaker machine to build against
    machine = Machine(12, 12)

    # Assign a vertex to each of the 17 application cores on each chip
    vertices = OrderedDict(
        ((x, y, p), object()) for x, y in machine for p in range(1, 18)
    )

    # Generate the vertex resources, placements and allocations (required for
    # routing)
    vertices_resources = OrderedDict(
        (vertex, {Cores: 1}) for vertex in itervalues(vertices)
    )
    placements = OrderedDict(
        (vertex, (x, y)) for (x, y, p), vertex in iteritems(vertices)
    )
    allocations = OrderedDict(
        (vertex, {Cores: slice(p, p+1)}) for (x, y, p), vertex in
        iteritems(vertices)
    )

    # Compute the distance dependent probabilities - this is a geometric
    # distribution such that each core has a 50% chance of being connected to
    # each core on the same chip, 25% on chips one hop away, 12.5% on chips two
    # hops away, etc.
    p = 0.5
    probs = {d: p*(1 - p)**d for d in
             range(max(machine.width, machine.height))}

    p = 0.3
    dprobs = {d: p*(1 - p)**d for d in
              range(max(machine.width, machine.height))}

    # Compute offsets to get to centroids
    vector_centroids = list()
    for d in (5, 6, 7):
        for i in range(d + 1):
            for j in range(d + 1 - i):
                vector_centroids.append((i, j, d - i - j))

    # Make the nets, each vertex is connected with distance dependent
    # probability to other vertices.
    random.seed(123)
    nets = OrderedDict()
    for source_coord, source in iteritems(vertices):
        # Convert source_coord to xyz form
        source_coord_xyz = minimise_xyz(to_xyz(source_coord[:-1]))

        # Add a number of centroids
        x, y, z = source_coord_xyz
        possible_centroids = [minimise_xyz((x + i, y + j, z + k)) for
                              i, j, k in vector_centroids]
        n_centroids = random.choice(17*(0, ) + (1, 1) + (2, ))
        centroids = random.sample(possible_centroids, n_centroids)

        # Construct the sinks list
        sinks = list()
        for sink_coord, sink in iteritems(vertices):
            # Convert sink_coord to xyz form
            sink_coord = minimise_xyz(to_xyz(sink_coord[:-1]))

            # Get the path length to the original source
            dist = shortest_torus_path_length(source_coord_xyz, sink_coord,
                                              machine.width, machine.height)
            if random.random() < probs[dist]:
                sinks.append(sink)
                continue

            # See if the sink is connected to the centre of any of the
            # centroids.
            for coord in centroids:
                dist = shortest_torus_path_length(
                    coord, sink_coord, machine.width, machine.height
                )

                if random.random() < dprobs[dist]:
                    sinks.append(sink)
                    break

        # Add the net
        nets[source_coord] = Net(source, sinks)

    rig_nets = list(itervalues(nets))  # Just the nets

    # Determine how many bits to use in the keys
    xyp_fields = BitField(32)
    xyp_fields.add_field("x", length=8, start_at=24)
    xyp_fields.add_field("y", length=8, start_at=16)
    xyp_fields.add_field("p", length=5, start_at=11)

    xyzp_fields = BitField(32)
    xyzp_fields.add_field("x", length=8, start_at=24)
    xyzp_fields.add_field("y", length=8, start_at=16)
    xyzp_fields.add_field("z", length=8, start_at=8)
    xyzp_fields.add_field("p", length=5, start_at=3)

    hilbert_fields = BitField(32)
    hilbert_fields.add_field("index", length=16, start_at=16)
    hilbert_fields.add_field("p", length=5, start_at=11)

    random.seed(321)
    rnd_fields = BitField(32)
    rnd_fields.add_field("rnd", length=12, start_at=20)
    rnd_seen = set()

    # Generate the routing keys
    net_keys_xyp = OrderedDict()
    net_keys_xyzp = OrderedDict()
    net_keys_hilbert = OrderedDict()
    net_keys_rnd = OrderedDict()
    for i, (x, y) in enumerate(chip for chip in hilbert_chip_order(machine) if
                               chip in machine):
        # Add the key for each net from each processor
        for p in range(1, 18):
            # Get the net
            net = nets[(x, y, p)]

            # Construct the xyp key/mask
            net_keys_xyp[net] = xyp_fields(x=x, y=y, p=p)

            # Construct the xyzp mask
            x_, y_, z_ = minimise_xyz(to_xyz((x, y)))
            net_keys_xyzp[net] = xyzp_fields(x=x_, y=y_, z=abs(z_), p=p)

            # Construct the Hilbert key/mask
            net_keys_hilbert[net] = hilbert_fields(index=i, p=p)

            # Construct the random 12 bit value field
            val = None
            while val is None or val in rnd_seen:
                val = random.getrandbits(12)
            rnd_seen.add(val)
            net_keys_rnd[net] = rnd_fields(rnd=val)

    # Route the network and then generate the routing tables
    constraints = list()
    print("Routing...")
    routing_tree = route(vertices_resources, rig_nets, machine, constraints,
                         placements, allocations)

    # Write the routing tables to file
    for fields, desc in ((net_keys_xyp, "xyp"),
                         (net_keys_xyzp, "xyzp"),
                         (net_keys_hilbert, "hilbert"),
                         (net_keys_rnd, "rnd")):
        print("Getting keys and masks...")
        keys = OrderedDict(
            (net, (bf.get_value(), bf.get_mask())) for net, bf in
            iteritems(fields)
        )

        print("Constructing routing tables for {}...".format(desc))
        tables = routing_tree_to_tables(routing_tree, keys)
        print([len(x) for x in itervalues(tables)])

        print("Writing to file...")
        fn = "uncompressed/centroid_{}_{}_{}.bin".format(
            machine.width, machine.height, desc)
        with open(fn, "wb+") as f:
            dump_routing_tables(f, tables)
示例#7
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def make_routing_tables():
    # Create a perfect SpiNNaker machine to build against
    machine = Machine(12, 12)

    # Assign a vertex to each of the 17 application cores on each chip
    vertices = OrderedDict(
        ((x, y, p), object()) for x, y in machine for p in range(1, 18))

    # Generate the vertex resources, placements and allocations (required for
    # routing)
    vertices_resources = OrderedDict((vertex, {
        Cores: 1
    }) for vertex in itervalues(vertices))
    placements = OrderedDict(
        (vertex, (x, y)) for (x, y, p), vertex in iteritems(vertices))
    allocations = OrderedDict((vertex, {
        Cores: slice(p, p + 1)
    }) for (x, y, p), vertex in iteritems(vertices))

    # Compute the distance dependent probabilities - this is a geometric
    # distribution such that each core has a 50% chance of being connected to
    # each core on the same chip, 25% on chips one hop away, 12.5% on chips two
    # hops away, etc.
    p = 0.5
    probs = {
        d: p * (1 - p)**d
        for d in range(max(machine.width, machine.height))
    }

    p = 0.3
    dprobs = {
        d: p * (1 - p)**d
        for d in range(max(machine.width, machine.height))
    }

    # Compute offsets to get to centroids
    vector_centroids = list()
    for d in (5, 6, 7):
        for i in range(d + 1):
            for j in range(d + 1 - i):
                vector_centroids.append((i, j, d - i - j))

    # Make the nets, each vertex is connected with distance dependent
    # probability to other vertices.
    random.seed(123)
    nets = OrderedDict()
    for source_coord, source in iteritems(vertices):
        # Convert source_coord to xyz form
        source_coord_xyz = minimise_xyz(to_xyz(source_coord[:-1]))

        # Add a number of centroids
        x, y, z = source_coord_xyz
        possible_centroids = [
            minimise_xyz((x + i, y + j, z + k)) for i, j, k in vector_centroids
        ]
        n_centroids = random.choice(17 * (0, ) + (1, 1) + (2, ))
        centroids = random.sample(possible_centroids, n_centroids)

        # Construct the sinks list
        sinks = list()
        for sink_coord, sink in iteritems(vertices):
            # Convert sink_coord to xyz form
            sink_coord = minimise_xyz(to_xyz(sink_coord[:-1]))

            # Get the path length to the original source
            dist = shortest_torus_path_length(source_coord_xyz, sink_coord,
                                              machine.width, machine.height)
            if random.random() < probs[dist]:
                sinks.append(sink)
                continue

            # See if the sink is connected to the centre of any of the
            # centroids.
            for coord in centroids:
                dist = shortest_torus_path_length(coord, sink_coord,
                                                  machine.width,
                                                  machine.height)

                if random.random() < dprobs[dist]:
                    sinks.append(sink)
                    break

        # Add the net
        nets[source_coord] = Net(source, sinks)

    rig_nets = list(itervalues(nets))  # Just the nets

    # Determine how many bits to use in the keys
    xyp_fields = BitField(32)
    xyp_fields.add_field("x", length=8, start_at=24)
    xyp_fields.add_field("y", length=8, start_at=16)
    xyp_fields.add_field("p", length=5, start_at=11)

    xyzp_fields = BitField(32)
    xyzp_fields.add_field("x", length=8, start_at=24)
    xyzp_fields.add_field("y", length=8, start_at=16)
    xyzp_fields.add_field("z", length=8, start_at=8)
    xyzp_fields.add_field("p", length=5, start_at=3)

    hilbert_fields = BitField(32)
    hilbert_fields.add_field("index", length=16, start_at=16)
    hilbert_fields.add_field("p", length=5, start_at=11)

    random.seed(321)
    rnd_fields = BitField(32)
    rnd_fields.add_field("rnd", length=12, start_at=20)
    rnd_seen = set()

    # Generate the routing keys
    net_keys_xyp = OrderedDict()
    net_keys_xyzp = OrderedDict()
    net_keys_hilbert = OrderedDict()
    net_keys_rnd = OrderedDict()
    for i, (x, y) in enumerate(chip for chip in hilbert_chip_order(machine)
                               if chip in machine):
        # Add the key for each net from each processor
        for p in range(1, 18):
            # Get the net
            net = nets[(x, y, p)]

            # Construct the xyp key/mask
            net_keys_xyp[net] = xyp_fields(x=x, y=y, p=p)

            # Construct the xyzp mask
            x_, y_, z_ = minimise_xyz(to_xyz((x, y)))
            net_keys_xyzp[net] = xyzp_fields(x=x_, y=y_, z=abs(z_), p=p)

            # Construct the Hilbert key/mask
            net_keys_hilbert[net] = hilbert_fields(index=i, p=p)

            # Construct the random 12 bit value field
            val = None
            while val is None or val in rnd_seen:
                val = random.getrandbits(12)
            rnd_seen.add(val)
            net_keys_rnd[net] = rnd_fields(rnd=val)

    # Route the network and then generate the routing tables
    constraints = list()
    print("Routing...")
    routing_tree = route(vertices_resources, rig_nets, machine, constraints,
                         placements, allocations)

    # Write the routing tables to file
    for fields, desc in ((net_keys_xyp, "xyp"), (net_keys_xyzp, "xyzp"),
                         (net_keys_hilbert, "hilbert"), (net_keys_rnd, "rnd")):
        print("Getting keys and masks...")
        keys = OrderedDict((net, (bf.get_value(), bf.get_mask()))
                           for net, bf in iteritems(fields))

        print("Constructing routing tables for {}...".format(desc))
        tables = routing_tree_to_tables(routing_tree, keys)
        print([len(x) for x in itervalues(tables)])

        print("Writing to file...")
        fn = "uncompressed/centroid_{}_{}_{}.bin".format(
            machine.width, machine.height, desc)
        with open(fn, "wb+") as f:
            dump_routing_tables(f, tables)
示例#8
0
def make_routing_tables():
    # Create a perfect SpiNNaker machine to build against
    machine = Machine(12, 12)

    # Assign a vertex to each of the 17 application cores on each chip
    vertices = OrderedDict(
        ((x, y, p), object()) for x, y in machine for p in range(1, 18)
    )

    # Generate the vertex resources, placements and allocations (required for
    # routing)
    vertices_resources = OrderedDict(
        (vertex, {Cores: 1}) for vertex in itervalues(vertices)
    )
    placements = OrderedDict(
        (vertex, (x, y)) for (x, y, p), vertex in iteritems(vertices)
    )
    allocations = OrderedDict(
        (vertex, {Cores: slice(p, p+1)}) for (x, y, p), vertex in
        iteritems(vertices)
    )

    # Compute the distance dependent probabilities
    probs = {d: .5*math.exp(-.65*d) for d in
             range(max(machine.width, machine.height))}

    # Make the nets, each vertex is connected with distance dependent
    # probability to other vertices.
    random.seed(123)
    nets = OrderedDict()
    for source_coord, source in iteritems(vertices):
        # Convert source_coord to xyz form
        source_coord_xyz = minimise_xyz(to_xyz(source_coord[:-1]))

        # Construct the sinks list
        sinks = list()
        for sink_coord, sink in iteritems(vertices):
            # Convert sink_coord to xyz form
            sink_coord = minimise_xyz(to_xyz(sink_coord[:-1]))

            # Get the path length
            dist = shortest_torus_path_length(source_coord_xyz, sink_coord,
                                              machine.width, machine.height)

            if random.random() < probs[dist]:
                sinks.append(sink)

        # Add the net
        nets[source_coord] = Net(source, sinks)

    rig_nets = list(itervalues(nets))  # Just the nets

    # Determine how many bits to use in the keys
    xyp_fields = BitField(32)
    xyp_fields.add_field("x", length=8, start_at=24)
    xyp_fields.add_field("y", length=8, start_at=16)
    xyp_fields.add_field("p", length=5, start_at=11)

    xyzp_fields = BitField(32)
    xyzp_fields.add_field("x", length=8, start_at=24)
    xyzp_fields.add_field("y", length=8, start_at=16)
    xyzp_fields.add_field("z", length=8, start_at=8)
    xyzp_fields.add_field("p", length=5, start_at=3)

    hilbert_fields = BitField(32)
    hilbert_fields.add_field("index", length=16, start_at=16)
    hilbert_fields.add_field("p", length=5, start_at=11)

    random.seed(321)
    rnd_fields = BitField(32)
    rnd_fields.add_field("rnd", length=12, start_at=20)
    rnd_seen = set()

    # Generate the routing keys
    net_keys_xyp = OrderedDict()
    net_keys_xyzp = OrderedDict()
    net_keys_hilbert = OrderedDict()
    net_keys_rnd = OrderedDict()
    for i, (x, y) in enumerate(chip for chip in hilbert_chip_order(machine) if
                               chip in machine):
        # Add the key for each net from each processor
        for p in range(1, 18):
            # Get the net
            net = nets[(x, y, p)]

            # Construct the xyp key/mask
            net_keys_xyp[net] = xyp_fields(x=x, y=y, p=p)

            # Construct the xyzp mask
            x_, y_, z_ = minimise_xyz(to_xyz((x, y)))
            net_keys_xyzp[net] = xyzp_fields(x=x_, y=y_, z=abs(z_), p=p)

            # Construct the Hilbert key/mask
            net_keys_hilbert[net] = hilbert_fields(index=i, p=p)

            # Construct the random 12 bit value field
            val = None
            while val is None or val in rnd_seen:
                val = random.getrandbits(12)
            rnd_seen.add(val)
            net_keys_rnd[net] = rnd_fields(rnd=val)

    # Route the network and then generate the routing tables
    constraints = list()
    print("Routing...")
    routing_tree = route(vertices_resources, rig_nets, machine, constraints,
                         placements, allocations)

    # Write the routing tables to file
    for fields, desc in ((net_keys_xyp, "xyp"),
                         (net_keys_xyzp, "xyzp"),
                         (net_keys_hilbert, "hilbert"),
                         (net_keys_rnd, "rnd")):
        print("Getting keys and masks...")
        keys = {net: (bf.get_value(), bf.get_mask()) for net, bf in
                iteritems(fields)}

        print("Constructing routing tables for {}...".format(desc))
        tables = routing_tree_to_tables(routing_tree, keys)
        print([len(x) for x in itervalues(tables)])

        print("Writing to file...")
        fn = "uncompressed/gaussian_{}_{}_{}.bin".format(
            machine.width, machine.height, desc)
        with open(fn, "wb+") as f:
            dump_routing_tables(f, tables)