def assign_xyzp_keys(nets):
"""Return a dictionary mapping a net to a unique key indicating the XYZP
co-ordinate of the source of the net.
"""
# Create the XYZP-formatted bit field
xyzp_bf = BitField()
xyzp_bf.add_field("x", length=8, start_at=24)
xyzp_bf.add_field("y", length=8, start_at=16)
xyzp_bf.add_field("z", length=8, start_at=8)
xyzp_bf.add_field("p", length=5, start_at=3)
xyzp_bf.assign_fields() # Fix the bitfield sizing
# Prepare to store the nets and keys
net_keys = dict()
# For each net look at the placement of the source vertex and hence
# generate a key.
for net in nets:
# Get the originating co-ordinates
x, y, p = net.source
# Get the minimal xyz co-ordinate
x, y, z = minimise_xyz(to_xyz((x, y)))
# Generate the key and mask
bf = xyzp_bf(x=x, y=y, z=abs(z), p=p)
net_keys[net] = bf.get_value(), bf.get_mask()
return net_keys
def assign_xyzp_keys(nets):
"""Return a dictionary mapping a net to a unique key indicating the XYZP
co-ordinate of the source of the net.
"""
# Create the XYZP-formatted bit field
xyzp_bf = BitField()
xyzp_bf.add_field("x", length=8, start_at=24)
xyzp_bf.add_field("y", length=8, start_at=16)
xyzp_bf.add_field("z", length=8, start_at=8)
xyzp_bf.add_field("p", length=5, start_at=3)
xyzp_bf.assign_fields() # Fix the bitfield sizing
# Prepare to store the nets and keys
net_keys = dict()
# For each net look at the placement of the source vertex and hence
# generate a key.
for net in nets:
# Get the originating co-ordinates
x, y, p = net.source
# Get the minimal xyz co-ordinate
x, y, z = minimise_xyz(to_xyz((x, y)))
# Generate the key and mask
bf = xyzp_bf(x=x, y=y, z=abs(z), p=p)
net_keys[net] = bf.get_value(), bf.get_mask()
return net_keys
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)
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)
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)
from matplotlib import pyplot as plt
import numpy as np
import random
from rig.geometry import to_xyz, minimise_xyz, shortest_torus_path_length
import seaborn as sns
random.seed(2801)
sns.set(context="paper", style="whitegrid", font="Times New Roman")
fig, (ax0, ax1) = plt.subplots(1, 2, figsize=(3.5, 1))
# Compute the probability for the Gaussian model
g_prob = np.zeros((12, 12))
home = minimise_xyz(to_xyz((1, 6)))
for x in range(12):
for y in range(12):
target = minimise_xyz(to_xyz((x, y)))
d = shortest_torus_path_length(home, target, 12, 12)
g_prob[y, x] = d
g_prob = .5 * np.exp(-.65 * g_prob)
ax0.set_title("Locally-connected")
ax0.grid(False)
ax0.set_xticklabels([])
ax0.set_yticklabels([])
ax0.matshow(g_prob, vmin=0.0, vmax=1.0, origin='lower')
def get_network(machine, rng):
# Compute the distance between chips
dists = np.zeros((machine.width, machine.height, 17), dtype=np.uint32)
for x, y in machine:
dists[x, y, :] = shortest_torus_path_length(
(0, 0, 0), minimise_xyz(to_xyz([x, y])),
machine.width, machine.height
)
# Compute the probability of a target for each distance
probs = 0.475**(dists + 1)
centroid_probs = 0.25**(dists + 1)
# Create the vertices for each core (and allocations and placements)
vertices = OrderedDict()
placements = OrderedDict()
allocations = OrderedDict()
resources = defaultdict(lambda: {Cores: 1})
for x, y in machine:
for p in range(17):
# Create the new vertex for this core
vx = common.Vertex(x, y, p)
# Place and allocate
vertices[x, y, p] = vx
placements[vx] = (x, y)
allocations[vx] = {Cores: slice(p, p+1)}
# Keep track of targets which are the result of centroid(s)
centroid_sinks = defaultdict(lambda: set())
# Now construct the nets
nets = list()
for x, y, p in ((i, j, k) for k in range(17) for i, j in machine):
# Get the source
source = vertices[x, y, p]
# Get the (local) sinks
sinks = get_targets(x, y, probs, machine.width, machine.height,
rng, vertices)
# Add centroid links if we have any (we have a 5% chance of connecting
# to 1 centroid)
if rng.uniform(0, 1) < 0.05:
# Get the target co-ordinates
c_sinks = get_targets(x, y, centroid_probs, machine.width,
machine.height, rng, vertices)
# Mark these as sinks relating to a centroid
centroid_sinks[x, y, p].update(c_sinks)
# Add to the general list of sinks
sinks += c_sinks
# Add the net
nets.append(Net(source, sinks))
# Route the nets
logger.info("Routing...")
# Get the centroid routes
centroid_routes = route(resources, nets, machine, list(),
placements, allocations)
# Prune back to get the local-only connections
logger.info("Pruning...")
local_routes = {
net: common.get_pruned_routing_tree(tree, centroid_sinks[net.source])
for net, tree in iteritems(centroid_routes)
}
return centroid_routes, local_routes
from matplotlib import pyplot as plt
import numpy as np
import random
from rig.geometry import to_xyz, minimise_xyz, shortest_torus_path_length
import seaborn as sns
random.seed(2801)
sns.set(context="paper", style="whitegrid", font="Times New Roman")
fig, (ax0, ax1) = plt.subplots(1, 2, figsize=(3.5, 1))
# Compute the probability for the Gaussian model
g_prob = np.zeros((12, 12))
home = minimise_xyz(to_xyz((1, 6)))
for x in range(12):
for y in range(12):
target = minimise_xyz(to_xyz((x, y)))
d = shortest_torus_path_length(home, target, 12, 12)
g_prob[y, x] = d
g_prob = .5 * np.exp(-.65*g_prob)
ax0.set_title("Locally-connected")
ax0.grid(False)
ax0.set_xticklabels([])
ax0.set_yticklabels([])
ax0.matshow(g_prob, vmin=0.0, vmax=1.0, origin='lower')
def get_network(machine, rng):
# Compute the distance between chips
dists = np.zeros((machine.width, machine.height, 17), dtype=np.uint32)
for x, y in machine:
dists[x,
y, :] = shortest_torus_path_length((0, 0, 0),
minimise_xyz(to_xyz([x, y])),
machine.width, machine.height)
# Compute the probability of a target for each distance
probs = 0.475**(dists + 1)
centroid_probs = 0.25**(dists + 1)
# Create the vertices for each core (and allocations and placements)
vertices = OrderedDict()
placements = OrderedDict()
allocations = OrderedDict()
resources = defaultdict(lambda: {Cores: 1})
for x, y in machine:
for p in range(17):
# Create the new vertex for this core
vx = common.Vertex(x, y, p)
# Place and allocate
vertices[x, y, p] = vx
placements[vx] = (x, y)
allocations[vx] = {Cores: slice(p, p + 1)}
# Keep track of targets which are the result of centroid(s)
centroid_sinks = defaultdict(lambda: set())
# Now construct the nets
nets = list()
for x, y, p in ((i, j, k) for k in range(17) for i, j in machine):
# Get the source
source = vertices[x, y, p]
# Get the (local) sinks
sinks = get_targets(x, y, probs, machine.width, machine.height, rng,
vertices)
# Add centroid links if we have any (we have a 5% chance of connecting
# to 1 centroid)
if rng.uniform(0, 1) < 0.05:
# Get the target co-ordinates
c_sinks = get_targets(x, y, centroid_probs, machine.width,
machine.height, rng, vertices)
# Mark these as sinks relating to a centroid
centroid_sinks[x, y, p].update(c_sinks)
# Add to the general list of sinks
sinks += c_sinks
# Add the net
nets.append(Net(source, sinks))
# Route the nets
logger.info("Routing...")
# Get the centroid routes
centroid_routes = route(resources, nets, machine, list(), placements,
allocations)
# Prune back to get the local-only connections
logger.info("Pruning...")
local_routes = {
net: common.get_pruned_routing_tree(tree, centroid_sinks[net.source])
for net, tree in iteritems(centroid_routes)
}
return centroid_routes, local_routes
