def print_matrix(matrix):
for a in matrix:
ln = ""
for i in a:
ln += str(i) + " "
# print(ln)
logger.debug(ln)
def all_path(adj_matrix):
n = len(adj_matrix)
paths = []
DFS(adj_matrix, n - 2, '', paths)
for p in paths:
logger.debug(p)
return paths
def reorder_modules(self, modules, topo_order):
# print(modules)
logger.debug('modules : %s'.format(modules))
r = []
for t in topo_order:
if t in modules:
r.append(t)
return r
def schedule_queue(self, v_tag, topo_order, anc_matrix):
queue = []
modules = []
for i in range(len(v_tag)):
if v_tag[i] == 1:
modules.append(i)
for i in range(self.core_num):
queue.append([])
ms = self.reorder_modules(modules, topo_order)
ancs = {}
for i in ms:
ancs.update({i:[]})
for j in ms:
if anc_matrix[j][i] == 1:
ancs[i].append(j)
# print(ancs)
logger.debug('ancs : %s'.format(ancs))
un_scheduled = ms
list = []
core = []
for i in range(self.core_num):
core.append(i)
i = 0
while len(un_scheduled) != 0:
if i % self.core_num == 0:
for v in list:
un_scheduled.remove(v)
list = []
for un_v in un_scheduled:
if len(list) == self.core_num:
break
# completion_queue(queue)
# sq = shortest_queue(queue)
# for s in sq:
# queue[s].append(0)
if self.check_anc_scheduled(ancs, un_v, un_scheduled):
list.append(un_v)
queue[core[i % self.core_num]].append(un_v)
i += 1
if i % self.core_num != 0 and len(un_scheduled) != 0:
self.completion_queue(queue)
while i % self.core_num != 0:
i += 1
# for i in range(len(ms)):
# for un_v in un_scheduled:
# if
# for i in range(len(ms)):
# queue[i % CORE_NUM].append(ms[i])
# print('queue : {0}'.format(queue))
logger.debug('queue : {0}'.format(queue))
return queue
def randomly_choice_a_path(adj_matrix, i, path):
# path = path + str(i)
path.append(i)
logger.debug(path)
if outdere(adj_matrix, i) == 0:
return path
v = adj_matrix[i][:]
l = []
for j in range(len(v)):
logger.debug(v[j])
if v[j] != 0:
l.append(j)
n = random.choice(l)
return randomly_choice_a_path(adj_matrix, n, path)
def generate_DAG(n):
node = list(range(1, n + 1))
sl(node)
sl(node)
m = rd(1, (n - 1) * n / 2)
adj_matrix = [([0] * (n + 2)) for i in range(n + 2)]
# lines = ''
ln = 0
# for i in range(0, m):
for i in range(0, min(10, m)):
p1 = rd(1, n - 1)
p2 = rd(p1 + 1, n)
x = node[p1 - 1]
y = node[p2 - 1]
# print '{},{}'.format(x-1, y - 1)
if adj_matrix[x - 1][y - 1] == 0:
adj_matrix[x - 1][y - 1] = gauss_random(EDGE_WEIGHT_MEAN,
EDGE_WEIGHT_VAR)
ln += 1
# if lines.find('{0},{1}'.format(x, y)) == - 1:
# ln = ln + 1
# lines = lines + '{0},{1};'.format(x, y)
for i in range(0, n):
# 出度为0的节点，需要连接到结束节点
if outdere(adj_matrix, i) == 0:
adj_matrix[i][n + 1] = gauss_random(EDGE_WEIGHT_MEAN,
EDGE_WEIGHT_VAR)
w_vex = []
for i in range(0, n):
# 头节点连向入度为0的节点
if indere(adj_matrix, i) == 0:
adj_matrix[n][i] = gauss_random(EDGE_WEIGHT_MEAN, EDGE_WEIGHT_VAR)
# w_vex.append(gauss_random(VEX_WEIGHT_MEAN, VEX_WEIGHT_VAR))
logger.debug('{0} nodes, {1} edges'.format(n, ln))
# logger.debug('lines : ' + lines)
for i in range(n + 2):
w_vex.append(gauss_random(VEX_WEIGHT_MEAN, VEX_WEIGHT_VAR))
# topo_order = generate_topo_order(node)
return adj_matrix, w_vex, generate_topo_order(node)
def T(self, adj_matrix, path, entry_task, exit_task, w_vex, v_tag):
if entry_task == -1 and exit_task == -1:
entry_task = len(path) - 1
exit_task = len(path) - 1
T_local = 0
T_local += local_T(path, 1, entry_task, w_vex)
T_local += local_T(path, exit_task+1, len(path) - 1, w_vex)
T_cloud = 0
if entry_task == len(path) - 1 and exit_task == len(path) - 1:
T_cloud += 0
else:
T_cloud += self.cloud.T(path, entry_task, exit_task, w_vex)
T_trans = self.trans_time(adj_matrix, path, entry_task, exit_task, v_tag)
if v_tag[path[0]] == -1:
T_local += w_vex[path[0]] / local.COMPUTE_ABILITY
else:
T_cloud += w_vex[path[0]] / self.cloud.compute_ability
if v_tag[path[-1]] == -1:
T_local += w_vex[path[-1]] / local.COMPUTE_ABILITY
else:
T_cloud += w_vex[path[-1]] / self.cloud.compute_ability
logger.debug('{0},{1} : {2}'.format(entry_task, exit_task, T_local + T_cloud + T_trans))
return T_local + T_cloud + T_trans
def choose_trans_vex_update_tag(self, adj_matrix, path, w_vex, v_tag):
# logger.debug('path : {0};'.format(path))
# logger.debug('v_tag : {0};'.format(v_tag))
# logger.debug('v_trans : {0};'.format(v_trans))
entry_task, exit_task = self.select_trans_seq(adj_matrix, path, w_vex, v_tag)
self.tag_trans_vex(path, entry_task, exit_task, v_tag)
logger.debug('{0}->{1}'.format(entry_task, exit_task))
logger.debug('path : {0};'.format(path))
logger.debug('v_tag : {0};'.format(v_tag))