diff options
| -rwxr-xr-x | bin/generate-dfa-benchmark.py | 3 | ||||
| -rwxr-xr-x | lib/automata.py | 11 | ||||
| -rwxr-xr-x | lib/dfatool.py | 13 | ||||
| -rw-r--r-- | lib/harness.py | 30 |
4 files changed, 45 insertions, 12 deletions
diff --git a/bin/generate-dfa-benchmark.py b/bin/generate-dfa-benchmark.py index f728602..4fb8df4 100755 --- a/bin/generate-dfa-benchmark.py +++ b/bin/generate-dfa-benchmark.py @@ -149,9 +149,6 @@ def run_benchmark(application_file: str, pta: PTA, runs: list, arch: str, app: s lines = monitor.run(int(run_timeout)) return [(runs, harness, lines)] - # TODO Benchmark bei zu vielen Transitionen in mehrere Programme - # aufteilen und diese nacheinander bis zu X % completion (220%) - # laufen lassen, zwischendurch jeweils automatisch neu bauen etc. try: slept = 0 while repeat == 0 or slept / run_timeout < 1: diff --git a/lib/automata.py b/lib/automata.py index de40eb4..d26c0bc 100755 --- a/lib/automata.py +++ b/lib/automata.py @@ -185,6 +185,10 @@ class Transition: self.set_param = set_param self.return_value_handlers = return_value_handlers + for handler in self.return_value_handlers: + if 'formula' in handler: + handler['formula'] = NormalizationFunction(handler['formula']) + def get_duration(self, param_dict: dict = {}, args: list = []) -> float: u""" Return transition duration in µs. @@ -310,6 +314,11 @@ class PTA: if not 'UNINITIALIZED' in state_names: self.state['UNINITIALIZED'] = State('UNINITIALIZED') + if self.parameter_normalization: + for normalization_spec in self.parameter_normalization.values(): + if 'formula' in normalization_spec: + normalization_spec['formula'] = NormalizationFunction(normalization_spec['formula']) + def normalize_parameters(self, param_dict): if self.parameter_normalization is None: return param_dict.copy() @@ -319,7 +328,7 @@ class PTA: if 'enum' in self.parameter_normalization[parameter] and value in self.parameter_normalization[parameter]['enum']: normalized_param[parameter] = self.parameter_normalization[parameter]['enum'][value] if 'formula' in self.parameter_normalization[parameter]: - normalization_formula = NormalizationFunction(self.parameter_normalization[parameter]['formula']) + normalization_formula = self.parameter_normalization[parameter]['formula'] normalized_param[parameter] = normalization_formula.eval(value) return normalized_param diff --git a/lib/dfatool.py b/lib/dfatool.py index 1dc2291..e318e81 100755 --- a/lib/dfatool.py +++ b/lib/dfatool.py @@ -519,13 +519,18 @@ class TimingData: trace['id'] = i for log_entry in trace['trace']: paramkeys = sorted(log_entry['parameter'].keys()) - paramvalues = [soft_cast_int(log_entry['parameter'][x]) for x in paramkeys] - if arg_support_enabled and 'args' in log_entry: - paramvalues.extend(map(soft_cast_int, log_entry['args'])) if not 'param' in log_entry['offline_aggregates']: log_entry['offline_aggregates']['param'] = list() if 'duration' in log_entry['offline_aggregates']: for i in range(len(log_entry['offline_aggregates']['duration'])): + paramvalues = list() + for paramkey in paramkeys: + if type(log_entry['parameter'][paramkey]) is list: + paramvalues.append(soft_cast_int(log_entry['parameter'][paramkey][i])) + else: + paramvalues.append(soft_cast_int(log_entry['parameter'][paramkey])) + if arg_support_enabled and 'args' in log_entry: + paramvalues.extend(map(soft_cast_int, log_entry['args'])) log_entry['offline_aggregates']['param'].append(paramvalues) def _preprocess_0(self): @@ -537,7 +542,7 @@ class TimingData: def get_preprocessed_data(self, verbose = True): """ - Return a list of DFA traces annotated with timing, and parameter data. + Return a list of DFA traces annotated with timing and parameter data. Suitable for the PTAModel constructor. See PTAModel(...) docstring for format details. diff --git a/lib/harness.py b/lib/harness.py index 8f48d00..cad7b06 100644 --- a/lib/harness.py +++ b/lib/harness.py @@ -135,6 +135,11 @@ class OnboardTimerHarness(TransitionHarness): ret += 'ptalog.stopTransition(counter);\n' return ret + def _append_nondeterministic_parameter_value(self, log_data_target, parameter_name, parameter_value): + if log_data_target['parameter'][parameter_name] is None: + log_data_target['parameter'][parameter_name] = list() + log_data_target['parameter'][parameter_name].append(parameter_value) + def parser_cb(self, line): #print('[HARNESS] got line {}'.format(line)) if re.match(r'\[PTA\] benchmark start, id=(\S+)', line): @@ -178,12 +183,29 @@ class OnboardTimerHarness(TransitionHarness): if self.log_return_values and len(transition.return_value_handlers): for handler in transition.return_value_handlers: if 'parameter' in handler: - print('got return value {:x} for transition {}, which has a handler. whoop whoop.'.format(int(res.group(4)), transition.name)) - # TODO handle value. - #print('[HARNESS] Logging data for transition {}'.format(log_data_target['name'])) + parameter_value = return_value = int(res.group(4)) + + if 'return_values' not in log_data_target: + log_data_target['return_values'] = list() + log_data_target['return_values'].append(return_value) + + if 'formula' in handler: + parameter_value = handler['formula'].eval(return_value) + + print('append current') + self._append_nondeterministic_parameter_value(log_data_target, handler['parameter'], parameter_value) + print('append following') + for following_log_data_target in self.traces[self.trace_id]['trace'][(self.current_transition_in_trace * 2 + 1) :]: + self._append_nondeterministic_parameter_value(following_log_data_target, handler['parameter'], parameter_value) + print('append preceding') + if 'apply_from' in handler and any(map(lambda x: x['name'] == handler['apply_from'], self.traces[self.trace_id]['trace'][: (self.current_transition_in_trace * 2 + 1)])): + for preceding_log_data_target in reversed(self.traces[self.trace_id]['trace'][: (self.current_transition_in_trace * 2)]): + self._append_nondeterministic_parameter_value(preceding_log_data_target, handler['parameter'], parameter_value) + if preceding_log_data_target['name'] == handler['apply_from']: + break if 'offline_aggregates' not in log_data_target: log_data_target['offline_aggregates'] = { 'duration' : list() } log_data_target['offline_aggregates']['duration'].append(duration_us) - self.current_transition_in_trace += 1
\ No newline at end of file + self.current_transition_in_trace += 1 |