def __init__(self, **kwargs):
super(type(self), self).__init__(**kwargs)
self.tot_util = kwargs.get('tot_util', 1.0)
self.ts = TaskSet()
self.last_id = -1
self.utilization_overflow = kwargs.get('util_over', True)
self.deadline_scale = kwargs.get('deadline_scale', 1.0)
def test_task_is_counted(self):
ts = TaskSet()
t1 = Task()
t2 = Task()
ts.append(t1)
ts.append(t2)
self.assertEqual(len(ts), 2)
def next_task_set(self):
ts = TaskSet()
for i in range(self.num_task):
t = self.next_task()
t.id = i
if ts.tot_util() + t.utilization() >= self.tot_util:
break
ts.append(t)
return ts
def test_task_setter(self):
ts = TaskSet()
param1 = {'id': 2}
t1 = Task(**param1)
ts.append(t1)
param2 = {'id': 3}
t2 = Task(**param2)
ts[0] = t2
self.assertEqual(ts[0].id, 3)
def next_task_set(self):
divided_util = self.unifast_divide(self.num_task, self.tot_util)
ts = TaskSet()
for i in range(self.num_task):
t = self.next_task(cand_util=divided_util[i])
t.id = i
ts.append(t)
return ts
def parallelize_pts_custom(pt_list, popt_list):
if len(pt_list) != len(popt_list):
raise Exception(
'pt_list or popt_list malformed. Length does not match.')
ts = TaskSet()
for i in range(len(pt_list)):
ts.merge_ts(pt_list[i][popt_list[i]])
return ts
def test_utilization_and_density_difference(self):
task_param = {
'exec_time': 6,
'deadline': 8,
'period': 10,
}
t = Task(**task_param)
ts = TaskSet()
ts.append(t)
diff = tsutil.sum_utilization(ts) - tsutil.sum_density(ts)
assert_almost_equals(diff, -0.15)
def test_sum_density(self):
task_param1 = {
'exec_time': 8,
'deadline': 10,
}
t1 = Task(**task_param1)
task_param2 = {
'exec_time': 9,
'deadline': 10,
}
t2 = Task(**task_param2)
ts = TaskSet()
ts.append(t1)
ts.append(t2)
assert_almost_equals(tsutil.sum_density(ts), 1.7)
def test_ts_merge(self):
param1 = {'id': 2}
t1 = Task(**param1)
param2 = {'id': 3}
t2 = Task(**param2)
ts1 = TaskSet()
ts2 = TaskSet()
ts1.append(t1)
ts2.append(t1)
ts2.append(t2)
ts1.merge_ts(ts2)
self.assertEqual(len(ts1), 3)
self.assertEqual(ts1[2], t2)
def __init__(self, **kwargs):
"""
**Role**: Initialize Parallelizable Taskset\n
.. note::
* **max_option** : The maximum parallelize option that \n
* **overhead** : The increasing rate of **execution_time** on every **parallelization** \n
* **variance** : The **execution_time** difference between **paralleizable task** and **thread** on paralleization\n
* **base_ts** : **parallelize task** needs **base_task** ( Default: exec_time=1, deadline=2, period=3 )\n
* **pt_list** : Save **paralleizable tasks** in a list to make a **paralleizable taskset*\n
* **populate_pt_list** : See the **"populate_pt_list"**\n
* **popt_strategy** : parallel option (default **single** )\n
* **popt_list** : parallel option list for each **pt_list**\n
* **pts_serialized** : Generate **taskset** includes **parallelizable tasks** with **parallel option**
* **serialize_pts** : See the **serialize pts**
"""
type(self).cnt += 1
self.id = kwargs.get('id', type(self).cnt)
self.max_opt = kwargs.get('max_option', 1)
# parallelizer info
self.overhead = kwargs.get('overhead', 0.0)
if self.overhead > 0.5:
self.overhead = 3.0
self.variance = kwargs.get('variance', 0.0)
# base task set info
tmp_ts = TaskSet()
tmp_ts.append(Task(**{'exec_time': 1, 'deadline': 2, 'period': 3}))
self.base_ts = kwargs.get('base_ts', tmp_ts)
self.pt_list = []
if kwargs.get('custom', 'False') == 'True':
self.pt_list = kwargs.get('pt_list', [[]])
else:
self.populate_pt_list()
# pts serialized according to selected option.
# defaults to single thread for each pt in pts.
self.popt_strategy = kwargs.get('popt_strategy', 'single')
self.popt_list = kwargs.get('popt_list',
[1 for i in range(len(self.pt_list))])
self.pts_serialized = TaskSet()
self.serialize_pts()
self.task_list = self.pts_serialized.task_list
return
def test_two_task(self):
task_param1 = {
'exec_time': 4,
'deadline': 10,
}
t1 = Task(**task_param1)
task_param2 = {
'exec_time': 6,
'deadline': 10,
}
t2 = Task(**task_param2)
ts = TaskSet()
ts.append(t1)
ts.append(t2)
gfb_param = {
'num_core': 2,
}
self.assertTrue(gfb.is_schedulable(ts, **gfb_param))
def custom_init(self):
if self.max_opt >= 2:
para.parallelize_pt_non_dec(self)
for opt in range(1, self.max_opt + 1):
ts = TaskSet()
for i in range(0, opt):
thr_param = {
'id': self.base_task.id,
'exec_time': self.exec_times[opt - 1][i],
'deadline': self.base_task.deadline,
'period': self.base_task.period,
}
thr = Thread(**thr_param)
ts.append(thr)
self.ts_table[str(opt)] = ts
return
def test_single_and_max_popt(self):
task_param = {
'exec_time': 40,
'deadline': 100,
'period': 100,
}
t1 = Task(**task_param)
task_param = {
'exec_time': 100,
'deadline': 200,
'period': 200,
}
t2 = Task(**task_param)
ts = TaskSet()
ts.append(t1)
ts.append(t2)
pts_param1 = {
'base_ts': ts,
'max_option': 4,
'overhead': 0.0,
'variance': 0.3,
'popt': 'single',
}
pts1 = ParaTaskSet(**pts_param1)
self.assertEqual(len(pts1), 2)
pts_param2 = {
'base_ts': ts,
'max_option': 4,
'overhead': 0.0,
'variance': 0.3,
'popt': 'max',
}
pts2 = ParaTaskSet(**pts_param2)
self.assertEqual(len(pts2), 2)
def __init__(self, **kwargs):
"""
**Role**: Initialize Parallelizable Task\n
.. note::
* **max_option** : The maximum parallelize option that \n
* **overhead** : The increasing rate of **execution_time** on every **parallelization** \n
* **variance** : The **execution_time** difference between **threads** on paralleization\n
* **base_task** : **parallelize task** needs **base_task** ( Default: exec_time=1, deadline=2, period=3 )\n
* **ts_table** : Save taskset from **1** to **max_option**\n
* **populate_ts_table** : See the **"populate_ts_table"**\n
"""
type(self).cnt += 1
self.id = kwargs.get('id', type(self).cnt)
self.max_opt = kwargs.get('max_option', 1)
# parallelizer info
self.overhead = kwargs.get('overhead', 0.0)
self.variance = kwargs.get('variance', 1.0)
# base task info
self.base_task = kwargs.get(
'base_task', Task(**{
'exec_time': 1,
'deadline': 2,
'period': 3
}))
ts = TaskSet()
ts.append(self.base_task)
self.ts_table = {'1': ts}
if kwargs.get('custom', 'False') == 'True':
self.exec_times = kwargs.get('exec_times', [[]])
self.custom_init()
else:
self.populate_ts_table()
}
t_objdetect = Task(**task_param)
para_task_param = {
'base_task': t_objdetect,
'max_option': 4,
'custom': 'True',
'exec_times': [[30], [17, 15], [12, 12, 10], [9, 8, 8, 7]],
}
pt_objdetect = ParaTask(**para_task_param)
print(pt_objdetect)
# create pts
print('----------------')
print('pts')
ts = TaskSet()
ts.append(t_lanetrack)
ts.append(t_objdetect)
pts_param_single = {
'base_ts': ts,
'max_option': 4,
'popt_strategy': 'single',
'custom': 'True',
'pt_list': [pt_lanetrack, pt_objdetect],
}
pts = ParaTaskSet(**pts_param_single)
pts_util = pts.tot_util()
print(pts_util)
print(pts)
def next_task_set(self):
ts = TaskSet()
for i in range(self.num_task):
t = self.next_task()
ts.append(t)
return ts
def test_id_does_not_overlap(self):
ts1 = TaskSet()
ts2 = TaskSet()
self.assertNotEqual(ts1.id, ts2.id)
def test_taskset_is_cleared(self):
ts = TaskSet()
t1 = Task()
ts.append(t1)
ts.clear()
self.assertEqual(len(ts), 0)
def parallelize_pts_max(pt_list, **kwargs):
max_opt = kwargs.get('max_option', 1)
ts = TaskSet()
for pt in pt_list:
ts.merge_ts(pt[max_opt])
return ts
def parallelize_pts_random(pt_list, **kwargs):
max_opt = kwargs.get('max_option', 1)
ts = TaskSet()
for pt in pt_list:
ts.merge_ts(pt[random.randint(1, max_opt)])
return ts
if __name__ == '__main__':
task_param = {
'exec_time': 35,
'deadline': 60,
'period': 60,
}
t1 = Task(**task_param)
task_param = {
'exec_time': 72,
'deadline': 80,
'period': 80,
}
t2 = Task(**task_param)
ts = TaskSet()
ts.append(t1)
ts.append(t2)
pts_param = {
'base_ts': ts,
'max_option': 4,
'overhead': 0.0,
'variance': 0.8,
'popt_strategy': 'custom',
'popt_list': [1, 1],
}
pts = ParaTaskSet(**pts_param)
print(pts)
def test_task_getter(self):
ts = TaskSet()
param = {'id': 2}
t = Task(**param)
ts.append(t)
self.assertEqual(ts[0].id, 2)
def parallelize_pt_non_dec_alpha(pt):
# Paralleliuze task while non decreasing total execution time
# largest execution time always non increases
# total execution time
# required to have total execution time larger than 3 * max_option
e_tot = pt.base_task.exec_time
if e_tot <= pt.max_opt * 3:
raise Exception('Execution time too small')
# largest execution time
e_max = pt.base_task.exec_time
e_tot_prev = e_tot
e_max_prev = e_max
for opt in range(2, pt.max_opt + 1):
# print('----------------')
# print('opt: ' + str(opt))
e_mean = e_tot_prev / opt
# print('e_mean: ' + str(e_mean))
# random draw (opt) from [0, 1]
# fixed s_tot, which is scaled later
# necessary step, to keep the ratio same
# discard if max thread is larger than before.
max_effort = 10
effort = 0
while True:
if effort >= max_effort:
break
s_tot = 1000
s_list = unifast_divide(opt, s_tot,
(s_tot / opt) * (1.0 + pt.variance))
s_list_norm = normalize_list(s_list)
# print(s_list_norm)
e_list = [round(e_mean * (1.0 + s)) for s in s_list_norm]
# print('e_list: ' + str(e_list))
e_max = max(e_list)
# print('e_max: ' + str(e_max))
if e_max >= e_max_prev:
effort += 1
continue
break
# scale e_tot accordingly
e_tot = pt.overhead * (e_max_prev - e_max) + e_tot_prev
# print('e_tot: ' + str(e_tot))
# make all tasks again, this time max pinned to e_max.
e_list = [e_max] + unifast_divide(opt - 1, e_tot - e_max, e_max)
e_list.sort(reverse=True)
# print('e_list_new: ' + str(e_list))
pt.ts_table[str(opt)] = TaskSet()
# alpha
# alpha = (e_tot - e_tot_prev) / (e_max_prev - e_max)
# print('alpha: ' + str(alpha))
# print('----------------')
# update e_tot, e_max
e_max_prev = e_max
e_tot_prev = e_tot
# create threads and append to task set
ts = TaskSet()
for i in range(len(e_list)):
# set minimum e to 1.0
if e_list[i] < 0.1:
e_list[i] = 1.0
thr_param = {
'id': pt.base_task.id,
'exec_time': e_list[i],
'deadline': pt.base_task.deadline,
'period': pt.base_task.period,
}
thr = Thread(**thr_param)
ts.append(thr)
# append to pt
pt.ts_table[str(opt)] = ts
return
def parallelize_pts_single(pt_list):
ts = TaskSet()
for pt in pt_list:
ts.merge_ts(pt[1])
return ts
class Egen(Gen):
def __init__(self, **kwargs):
super(type(self), self).__init__(**kwargs)
self.tot_util = kwargs.get('tot_util', 1.0)
self.ts = TaskSet()
self.last_id = -1
self.utilization_overflow = kwargs.get('util_over', True)
self.deadline_scale = kwargs.get('deadline_scale', 1.0)
def next_task(self, **kwargs):
period = random.randint(self.min_period, self.max_period)
exec_time = random.randint(self.min_exec_time, self.max_exec_time)
# prevents tasks with utilization > 1.0
if not self.utilization_overflow:
while exec_time > period + 0.1:
period = random.randint(self.min_period, self.max_period)
exec_time = random.randint(self.min_exec_time,
self.max_exec_time)
if self.implicit_deadline:
deadline = period
else:
if self.constrained_deadline:
deadline = random.randint(self.min_deadline, period)
else:
deadline = random.randint(self.min_deadline, self.max_deadline)
task_param = {
'period': period,
'exec_time': exec_time,
'deadline': deadline * self.deadline_scale,
}
t = Task(**task_param)
return t
def __str__(self):
info = 'Generator - egen\n' + \
super(type(self), self).__str__() + '\n' + \
'tot_util = ' + str(self.tot_util) + '\n' + \
'util_over = ' + str(self.utilization_overflow)
return info
def create_new_task_set(self, t):
self.ts.clear()
if t.utilization() <= self.tot_util:
self.last_id = 0
t.id = self.last_id
self.ts.append(t)
return self.ts
else:
raise Exception('Cannot create new task set, tried utilization: ' +
t.utilization())
def next_task_set(self):
# try append task to existing task set
t = self.next_task()
if self.ts.tot_util() + t.utilization() >= self.tot_util:
return self.create_new_task_set(t)
# append task to existing task set
self.last_id += 1
t.id = self.last_id
self.ts.append(t)
return self.ts
def parallelize_alpha(pt):
# Parallelize task while non decreasing total execution time
# Also largest execution time always non increases
# total execution time
# required to have total execution time larger than 3 * max_option
e_tot = pt.base_task.exec_time
if e_tot <= pt.max_opt * 3:
raise Exception('Execution time too small')
e_tot_prev = e_tot
# largest execution time
e_max = pt.base_task.exec_time
e_max_prev = e_max
for opt in range(2, pt.max_opt + 1):
pt.ts_table[str(opt)] = TaskSet()
# increase total execution time
e_tot = e_tot_prev * (1.0 + pt.variance)
# decrease first thread execution time accordingly
e_max = e_max_prev - ((e_tot - e_tot_prev) / pt.overhead)
# ideal seperation execution time
e_ideal = e_tot / opt
"""
normalize variance
variance = 0 --> e_max = e_ideal
variance = 1 --> e_max = e_max (prev)
e_max_limit = e_ideal + (e_max(prev) - e_ideal) * variance
unifast split into pcs,
while only accepting when largest generated e < e_max_limit
"""
# execution times
e_max_limit = e_ideal + (e_max - e_ideal) * pt.variance
e_list = unifast_divide(opt, e_tot, e_max_limit)
e_max = e_list[0]
# create threads and append to task set
ts = TaskSet()
for i in range(len(e_list)):
# set minimum e to 1.0
if e_list[i] < 0.1:
e_list[i] = 1.0
thr_param = {
'id': pt.base_task.id,
'exec_time': e_list[i],
'deadline': pt.base_task.deadline,
'period': pt.base_task.period,
}
thr = Thread(**thr_param)
ts.append(thr)
# append to pt
pt.ts_table[str(opt)] = ts
e_tot_prev = e_tot
e_max_prev = e_max
return