def init_partition(circuit, block_ids=None) -> Data:
"""
randomly partition the nodes equally, if the block_ids
is not specified.
:return: data container for the current partition
"""
pmax = get_pmax(circuit)
nets_size = circuit.get_nets_size()
n = circuit.get_cells_size()
if block_ids is None:
random_cids = random.sample(range(n), n)
block_ids = [cid % 2 for i, cid in enumerate(random_cids)]
# initialize a data container for the current partition
data = Data(pmax, nets_size, block_ids)
# update the nets distribution in the current partition
update_distribution(circuit, data)
# update the gain for each node in the current partition
calculate_gains(circuit, data)
# update the cutsize of the current partition
data.cutsize = calculate_cutsize(circuit, data)
logging.info("initial cutsize = {}".format(data.cutsize))
# intialize best partition, and prev mincut
data.store_best_cut()
data.prev_mincut = data.mincut
return data
def kl_inner_loop(circuit: Circuit, data: Data, app=None, genetic=False):
"""
perform the inner loop of the Kernighan-Lin Partition algorithm
"""
# select the max gain node from blocks
max_gain_node = select_max_gain_node(data)
# move the max gain node to another block
# - update the gain for each node
# - update the nets distribution
# - update the cutsize
move_node_another_block(max_gain_node, data)
# if cutsize is the minimum for this pass, store the cut
if data.cutsize < data.mincut:
data.store_best_cut()
data.print_blocks_size()
if app is None or app.quick:
if data.has_unlocked_nodes():
kl_inner_loop(circuit, data, app, genetic)
else:
kl_inner_stop(circuit, data, app, genetic)
else:
app.update_canvas(data)
# inner loop stop until no unlocked nodes remains
if data.has_unlocked_nodes():
app.root.after(1, kl_inner_loop, circuit, data, app, genetic)
else: # inner loop exit
app.root.after(1000, kl_inner_stop, circuit, data, app, genetic)