def e2e_test(expected_wclat):
options.init_pycpa()
# generate an new system
s = model.System()
# add a resource to the system
# register round robin scheduler
r1 = s.bind_resource(model.Resource("R1",
schedulers.RoundRobinScheduler()))
# map two tasks to
t11 = r1.bind_task(model.Task("T11", wcet=1, bcet=1))
t12 = r1.bind_task(model.Task("T12", wcet=2, bcet=1))
# register precedence constraint
t11.link_dependent_task(t12)
t11.in_event_model = model.PJdEventModel(P=4, J=3)
# register a task chain as a stream
s1 = s.bind_path(model.Path("S1", [t11, t12]))
# perform a system analysis
print("analyzing")
task_results = analysis.analyze_system(s)
# calculate the latency for the first 10 events
for n in range(1, 11):
best_case_latency, worst_case_latency = path_analysis.end_to_end_latency(
s1, task_results, n)
print("stream S1 e2e latency. best case: %d, worst case: %d" %
(best_case_latency, worst_case_latency))
assert (worst_case_latency == expected_wclat[n - 1])
def smff_test(filename, outfile, plot, verbose):
options.init_pycpa()
print "loading", filename
loader = smff_loader.SMFFLoader()
s = loader.parse(filename)
if plot == True:
# graph the smff system
graph_file = string.replace(os.path.basename(filename), ".xml", "") + ".pdf"
graph.graph_system(s, sched_param=True, exec_times=True, filename=graph_file)
try:
# analyze the system
results = analysis.analyze_system(s)
# print some analysis results
print("Result:")
for r in sorted(s.resources, key=str):
print "results for resource %s" % r.name
for t in sorted(r.tasks, key=str):
print("%s - %d " % (str(t.name) , results[t].wcrt))
if outfile is not None:
# backannotate the xml
loader.annotate_results()
# write it
loader.write(filename=outfile)
except analysis.NotSchedulableException as (e):
print str(e)
def spp_test():
# init pycpa and trigger command line parsing
options.init_pycpa()
# generate an new system
s = model.System()
# add two resources (CPUs) to the system
# and register the static priority preemptive scheduler
r1 = s.bind_resource(model.Resource("R1", schedulers.SPPScheduler()))
r2 = s.bind_resource(model.Resource("R2", schedulers.SPPScheduler()))
# create and bind tasks to r1
t11 = r1.bind_task(
model.Task("T11", wcet=10, bcet=5, scheduling_parameter=1))
t12 = r1.bind_task(
model.Task("T12", wcet=3, bcet=1, scheduling_parameter=2))
# create and bind tasks to r2
t21 = r2.bind_task(
model.Task("T21", wcet=2, bcet=2, scheduling_parameter=1))
t22 = r2.bind_task(
model.Task("T22", wcet=9, bcet=4, scheduling_parameter=2))
# specify precedence constraints: T11 -> T21; T12-> T22
t11.link_dependent_task(t21)
t12.link_dependent_task(t22)
# register a periodic with jitter event model for T11 and T12
t11.in_event_model = model.PJdEventModel(P=30, J=5)
t12.in_event_model = model.PJdEventModel(P=15, J=6)
# plot the system graph to visualize the architecture
g = graph.graph_system(s, 'spp_graph.pdf', dotout='spp_graph.dot')
# perform the analysis
print("Performing analysis")
task_results = analysis.analyze_system(s)
# print the worst case response times (WCRTs)
print("Result:")
for r in sorted(s.resources, key=str):
for t in sorted(r.tasks, key=str):
print("%s: wcrt=%d" % (t.name, task_results[t].wcrt))
print(" b_wcrt=%s" % (task_results[t].b_wcrt_str()))
expected_wcrt = dict()
expected_wcrt[t11] = 10
expected_wcrt[t12] = 13
expected_wcrt[t21] = 2
expected_wcrt[t22] = 19
for t in expected_wcrt.keys():
assert (expected_wcrt[t] == task_results[t].wcrt)
def gantt_test():
# initialyze pycpa. (e.g. read command line switches and set up default options)
options.init_pycpa()
# generate an new system
s = model.System()
# instantiate a resource
r1 = s.bind_resource(model.Resource("R1", schedulers.SPPScheduler()))
# create and bind tasks to r1
t11 = r1.bind_task(
model.Task("T11", wcet=5, bcet=5, scheduling_parameter=1))
t12 = r1.bind_task(
model.Task("T12", wcet=9, bcet=1, scheduling_parameter=2))
# connect communicating tasks: T11 -> T12
t11.link_dependent_task(t12)
# register a PJd event model
t11.in_event_model = model.PJdEventModel(P=30, J=60)
# perform the analysis
print("Performing analysis")
results = analysis.analyze_system(s)
# print the worst case response times (WCRTs)
print("Result:")
for r in sorted(s.resources, key=str):
for t in sorted(r.tasks, key=str):
print("%s: wcrt=%d" % (t.name, results[t].wcrt))
simmodel = simulation.ResourceModel(r1)
simmodel.runModel(task=t12,
scheduler=simulation.SimSPP(name="SPP", sim=simmodel))
# plot
hp_tasks = list()
for t in sorted(r1.tasks, key=str):
if t.scheduling_parameter <= t12.scheduling_parameter:
hp_tasks.append(t)
plot.plot_gantt(hp_tasks,
results,
height=2. / 3,
bar_linewidth=2,
min_dist_arrows=1,
arrow_head_width=1,
task=t12,
show=options.get_opt('show'),
file_name='gantt.pdf')
def simple_test():
# initialyze pycpa. (e.g. read command line switches and set up default options)
options.init_pycpa()
# generate an new system
s = model.System()
# instantiate a resource
r1 = s.bind_resource(model.Resource("R1", schedulers.SPPScheduler()))
# create and bind tasks to r1
t11 = r1.bind_task(
model.Task("T11", wcet=5, bcet=5, scheduling_parameter=1))
t12 = r1.bind_task(
model.Task("T12", wcet=9, bcet=1, scheduling_parameter=2))
# connect communicating tasks: T11 -> T12
t11.link_dependent_task(t12)
# register a PJd event model
t11.in_event_model = model.PJdEventModel(P=30, J=60)
# create a path
p1 = model.Path("P1", [t11, t12])
# add constraints to task t11
s.constraints.add_backlog_constraint(t11, 5)
# add constraints to task t12
s.constraints.add_wcrt_constraint(t12, 90)
try:
# plot the system graph to visualize the architecture
g = graph.graph_system(s, 'simple_graph.pdf')
except Exception:
# gracefully pass for machines without matplotlib
pass
# perform the analysis
print("Performing analysis")
results = analysis.analyze_system(s)
# print the worst case response times (WCRTs)
print("Result:")
for r in sorted(s.resources, key=str):
for t in sorted(r.tasks, key=str):
print("%s: wcrt=%d" % (t.name, results[t].wcrt))
bcl, wcl = path_analysis.end_to_end_latency(p1, results, 2)
print "bcl: %d, wcl: %d" % (bcl, wcl)
def constraints_example():
options.init_pycpa()
# generate an new system
s = model.System()
# add two resources (CPUs) to the system
# and register the static priority preemptive scheduler
r1 = s.bind_resource(model.Resource("R1", schedulers.SPPScheduler()))
r2 = s.bind_resource(model.Resource("R2", schedulers.SPPScheduler()))
# create and bind tasks to r1
t11 = r1.bind_task(
model.Task("T11", wcet=10, bcet=5, scheduling_parameter=1))
t12 = r1.bind_task(
model.Task("T12", wcet=3, bcet=1, scheduling_parameter=2))
# create and bind tasks to r2
t21 = r2.bind_task(
model.Task("T21", wcet=2, bcet=2, scheduling_parameter=1))
t22 = r2.bind_task(
model.Task("T22", wcet=9, bcet=4, scheduling_parameter=2))
# specify precedence constraints: T11 -> T21; T12-> T22
t11.link_dependent_task(t21)
t12.link_dependent_task(t22)
# register a periodic with jitter event model for T11 and T12
t11.in_event_model = model.PJdEventModel(P=30, J=5)
t12.in_event_model = model.PJdEventModel(P=15, J=6)
p1 = model.Path("myPath", [t11, t21])
s.bind_path(p1)
# deliberately overconstrain the system
s.constraints.add_path_constraint(p1, 5, n=2)
s.constraints.add_wcrt_constraint(t11, 1)
s.constraints.add_load_constraint(r1, 0.5)
s.constraints.add_backlog_constraint(t11, 1)
# perform the analysis
print("Performing analysis")
task_results = analysis.analyze_system(s)
# print the worst case response times (WCRTs)
print("Result:")
for r in sorted(s.resources, key=str):
for t in sorted(r.tasks, key=str):
print("%s: wcrt=%d" % (t.name, task_results[t].wcrt))
def sampling_test(wclat_results):
options.init_pycpa()
# generate an new system
s = model.System()
# add two resources to the system
# register two schedulers
r1 = s.bind_resource(model.Resource("R1", schedulers.SPPScheduler()))
# add a task
t11 = r1.bind_task(model.Task(name="T11", wcet=3, bcet=1, scheduling_parameter=1))
# register input event model
t11.in_event_model = model.PJdEventModel(P=30, J=15)
# add three more tasks, these will be triggered by other tasks
t12 = r1.bind_task(model.Task(name="T12", wcet=3, bcet=2, scheduling_parameter=2))
# add a sampling junction
j1 = s.bind_junction(model.Junction(name="J1", strategy=junctions.SampledInput()))
j1.strategy.set_trigger_event_model(model.PJdEventModel(P=50, J=0))
# register a task chain as a stream: t11->j1->t12
s1 = s.bind_path(model.Path("S1", [t11, j1, t12]))
# graph the system
graph.graph_system(s, "sampling_example.pdf")
# analyze the system
print("Performing analysis")
results = analysis.analyze_system(s)
# print the results
print("Result:")
for r in sorted(s.resources, key=str):
print "load on resource %s: %0.2f" % (r.name, r.load())
for t in sorted(r.tasks, key=str):
print " task %s - wcrt: %d" % (t.name, results[t].wcrt)
# calculate the latency for the first 10 events
for n in range(1, 11):
best_case_latency, worst_case_latency = path_analysis.end_to_end_latency(s1, results, n)
print("stream S1 e2e latency. best case: %d, worst case: %d" % (best_case_latency, worst_case_latency))
assert(worst_case_latency == wclat_results[n-1])
def trace_example():
options.init_pycpa()
# this is a rather extreme trace with just two datapoints
# pycpa will use additive extension which is a periodic extension in this case
periodic_trace = [0, 10]
em = model.TraceEventModel()
em.set_limited_trace(periodic_trace, 1)
print "input trace:", periodic_trace
print "output delta(n): ", [em.delta_min(p) for p in range(1, 10)]
# this a more realistic event model. periodic in nature with
# a burst at time 32
em = model.TraceEventModel()
bursty_trace = [0, 10, 20, 30, 32, 40, 50]
em.set_limited_trace(bursty_trace, 1)
print "input trace:", bursty_trace
print "output delta_min(n): ", [em.delta_min(p) for p in range(1, 10)]
print "output delta_plus(n): ", [em.delta_plus(p) for p in range(1, 10)]
def main():
""" Loads the pickled system and analyzes it with the given command
line parameter settings.
"""
system = None
# read the last argument without the help of argparse
# since argparse is not initialized yet!
if len(sys.argv) < 3:
print("Please specify a filename!")
print(
"Format: analyze_pickled.py [optinal pycpa parameters] --file FILENAME "
)
exit(1)
filename = sys.argv[-1]
print("Loading: %s" % filename)
try:
with open(filename, 'rb') as f:
s = pickle.load(f)
except IOError as e:
print e
exit(1)
print("DONE")
print("... handing over to the pycpa kernel\n\n\n")
# init pycpa and trigger command line parsing
# this must be done late, as the modules dynamically loaded
# by pickle can add additional argparse arguments
options.init_pycpa()
task_results = analysis.analyze_system(s)
# print the worst case response times (WCRTs)
print("Result:")
for r in sorted(s.resources, key=str):
for t in sorted(r.tasks, key=str):
print("%s: wcrt=%d" % (t.name, task_results[t].wcrt))
print(" b_wcrt=%s" % (task_results[t].b_wcrt_str()))
def profiling_test():
options.init_pycpa()
# generate an new system
s = model.System()
# add two resources (CPUs) to the system
# and register the static priority preemptive scheduler
r1 = s.bind_resource(model.Resource("R1", schedulers.SPPScheduler()))
r2 = s.bind_resource(model.Resource("R2", schedulers.SPPScheduler()))
# create and bind tasks to r1
t11 = r1.bind_task(
model.Task("T11", wcet=10, bcet=5, scheduling_parameter=1))
t12 = r1.bind_task(
model.Task("T12", wcet=3, bcet=1, scheduling_parameter=2))
# create and bind tasks to r2
t21 = r2.bind_task(
model.Task("T21", wcet=2, bcet=2, scheduling_parameter=1))
t22 = r2.bind_task(
model.Task("T22", wcet=9, bcet=4, scheduling_parameter=2))
# specify precedence constraints: T11 -> T21; T12-> T22
t11.link_dependent_task(t21)
t12.link_dependent_task(t22)
# register a periodic with jitter event model for T11 and T12
t11.in_event_model = model.PJdEventModel(P=30, J=5)
t12.in_event_model = model.PJdEventModel(P=15, J=6)
filename = "profiling_example.prof"
prof = hotshot.Profile(filename)
prof.runcall(analysis.analyze_system, s)
prof.close()
stats = hotshot.stats.load(filename)
stats.strip_dirs()
stats.sort_stats('time', 'calls')
stats.print_stats(20)
def tdma_test():
options.init_pycpa()
s = model.System()
r1 = s.bind_resource(model.Resource("R1", schedulers.TDMAScheduler()))
r2 = s.bind_resource(model.Resource("R2", schedulers.TDMAScheduler()))
# scheduling_parameter denotes the slotsize
t11 = r1.bind_task(
model.Task("T11", wcet=10, bcet=5, scheduling_parameter=2))
t12 = r1.bind_task(
model.Task("T12", wcet=3, bcet=1, scheduling_parameter=2))
t21 = r2.bind_task(
model.Task("T21", wcet=2, bcet=2, scheduling_parameter=2))
t22 = r2.bind_task(
model.Task("T22", wcet=3, bcet=3, scheduling_parameter=2))
t11.link_dependent_task(t21)
t12.link_dependent_task(t22)
t11.in_event_model = model.PJdEventModel()
t11.in_event_model.set_PJd(30, 5)
t12.in_event_model = model.PJdEventModel()
t12.in_event_model.set_PJd(15, 6)
g = graph.graph_system(s, 'tdma_graph.pdf')
print("Performing analysis")
results = analysis.analyze_system(s)
print("Result:")
for r in sorted(s.resources, key=str):
for t in sorted(r.tasks, key=str):
print str(t), " - ", results[t].wcrt
print " ", results[t].b_wcrt_str()
def rr_test():
options.init_pycpa()
s = model.System()
r1 = s.bind_resource(model.Resource("R1",
schedulers.RoundRobinScheduler()))
r2 = s.bind_resource(model.Resource("R2",
schedulers.RoundRobinScheduler()))
# create and bind tasks
# the scheduling_parameter denotes the slot size
t11 = r1.bind_task(
model.Task(name="T11", wcet=1, bcet=1, scheduling_parameter=1))
t12 = r1.bind_task(
model.Task(name="T12", wcet=1, bcet=1, scheduling_parameter=1))
t21 = r2.bind_task(
model.Task(name="T21", wcet=1, bcet=1, scheduling_parameter=1))
t22 = r2.bind_task(
model.Task(name="T22", wcet=1, bcet=1, scheduling_parameter=1))
t11.link_dependent_task(t21)
t22.link_dependent_task(t12)
t11.in_event_model = model.CTEventModel(c=1, T=5)
t22.in_event_model = model.CTEventModel(c=5, T=17)
print("Performing analysis")
results = analysis.analyze_system(s)
print("Result:")
for r in sorted(s.resources, key=str):
for t in sorted(r.tasks, key=str):
print(t, " - ", results[t].wcrt)
print(" b_wcrt=%s" % (results[t].b_wcrt_str()))
graph.graph_system(s, show=options.get_opt('show'))
| See LICENSE file for copyright and license details.
:Authors:
- Philip Axer
Description
-----------
SymTA/S 1.4 Loader example
"""
from pycpa import analysis
from pycpa import symload
from pycpa import options
import os
## this is necessary because the file is also called from the regression test suite
path = os.path.dirname(os.path.realpath(__file__))
options.init_pycpa()
loader = symload.SymtaLoader14()
s = loader.parse(path + "/symta14_test.xml")
results = analysis.analyze_system(s)
print("Result:")
for r in sorted(s.resources, key=str):
print "results for resource %s" % r.name
for t in sorted(r.tasks, key=str):
print str(t), "-", results[t].wcrt
def time_bases_test(auto=True):
# print the welcome message
options.init_pycpa()
# generate an new system
s = model.System()
# add two resources (CPUs) to the system
# and register the static priority preemptive scheduler
r1 = s.bind_resource(
model.Resource("R1", schedulers.SPPScheduler(), frequency=333333333))
r2 = s.bind_resource(
model.Resource("R2", schedulers.SPPScheduler(), frequency=100000000))
# get a common time-base
common_time_base = util.ns
if auto == True:
common_time_base = util.calculate_base_time(
[r.frequency for r in s.resources])
print "Basetime is %d Hz" % common_time_base
# create and bind tasks to r1. Here the timing is specified in absolute time (i.e. 10 ms)
t11 = r1.bind_task(
model.Task("T11",
wcet=util.time_to_time(10, util.ms, common_time_base,
'ceil'),
bcet=util.time_to_time(5, util.ms, common_time_base,
'floor'),
scheduling_parameter=1))
t12 = r1.bind_task(
model.Task("T12",
wcet=util.time_to_time(3, util.ms, common_time_base,
'ceil'),
bcet=util.time_to_time(1, util.ms, common_time_base,
'floor'),
scheduling_parameter=2))
# create and bind tasks to r2. Here the timing is specified in cycle time (i.e. 10000 cycles)
t21 = r2.bind_task(
model.Task("T21",
wcet=util.cycles_to_time(200000, r2.frequency,
common_time_base, 'ceil'),
bcet=util.cycles_to_time(200000, r2.frequency,
common_time_base, 'floor'),
scheduling_parameter=1))
t22 = r2.bind_task(
model.Task("T22",
wcet=util.cycles_to_time(900000, r2.frequency,
common_time_base, 'ceil'),
bcet=util.cycles_to_time(400000, r2.frequency,
common_time_base, 'floor'),
scheduling_parameter=2))
# specify precedence constraints: T11 -> T21; T12-> T22
t11.link_dependent_task(t21)
t12.link_dependent_task(t22)
# register a periodic with jitter event model for T11 and T12
t11.in_event_model = model.PJdEventModel(
P=util.time_to_time(30, util.ms, common_time_base, 'floor'),
J=util.time_to_time(5, util.ms, common_time_base, 'ceil'))
t12.in_event_model = model.PJdEventModel(
P=util.time_to_time(15, util.ms, common_time_base, 'floor'),
J=util.time_to_time(6, util.ms, common_time_base, 'ceil'))
# perform the analysis
print("Performing analysis")
task_results = analysis.analyze_system(s)
# print the worst case response times (WCRTs)
print("Result:")
for r in sorted(s.resources, key=str):
for t in sorted(r.tasks, key=str):
wcrt_ms = util.time_to_time(task_results[t].wcrt, common_time_base,
util.ms, 'ceil')
print("%s: wcrt=%d ms" % (t.name, wcrt_ms))