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-
-
- """
- Produce performance measurement
- """
- import argparse
- import multiprocessing
- import os
-
- import time
- from collections import OrderedDict
- from random import randint
-
- from sandbox.perf.simulation import ComputeSubject, ComputeBehaviour, ComputeMechanism
- from synergine2.config import Config
- from synergine2.cycle import CycleManager
- from synergine2.log import SynergineLogger
- from synergine2.simulation import Simulation, Subjects
-
-
- def simulate(complexity, subject_count, cycle_count, cores):
- config = Config(dict(complexity=complexity, core=dict(use_x_cores=cores)))
- simulation = Simulation(config)
- subjects = Subjects(simulation=simulation)
-
- for i in range(subject_count):
- subject = ComputeSubject(
- config=config,
- simulation=simulation,
- )
- subject.data = [randint(0, 1000) for i in range(1000)]
- subjects.append(subject)
-
- simulation.subjects = subjects
-
- cycle_manager = CycleManager(
- config,
- SynergineLogger('perf'),
- simulation=simulation,
- )
-
- for j in range(cycle_count):
- cycle_manager.next()
-
-
- def main():
- parser = argparse.ArgumentParser(description='Perf measures')
- parser.add_argument(
- '--max_cores',
- type=int,
- default=0,
- help='number of used cores',
- )
-
- args = parser.parse_args()
-
- host_cores = multiprocessing.cpu_count()
- retry = 1
- cycles = 10
- subject_counts = [500]
- complexities = [200]
- max_cores = args.max_cores or host_cores
-
- results = []
- datas = OrderedDict()
-
- for core_i in range(max_cores):
- # if core_i == 0:
- # continue
- core_count = core_i + 1
- for subject_count in subject_counts:
- for complexity in complexities:
- print('COMPLEXITY: {}, SUBJECTS: {}, CORES: {}'.format(
- complexity,
- subject_count,
- core_count,
- ), end='')
-
- durations = []
- for try_i in range(retry):
- start_time = time.time()
- simulate(complexity, subject_count, cycles, core_count)
- durations.append(time.time() - start_time)
- duration = min(durations)
-
- result = {
- 'cores': core_count,
- 'complexity': complexity,
- 'subject_count': subject_count,
- 'cycles': cycles,
- 'duration': duration,
- 'duration_cycle': duration / cycles,
- 'duration_subj_complex': (duration / cycles) / (subject_count * complexity),
- }
- results.append(result)
-
- print(': {}s, {}s/c, {}s/C'.format(
- result['duration'],
- result['duration_cycle'],
- result['duration_subj_complex'],
- ))
- datas.setdefault(complexity, {}).setdefault(subject_count, {})[core_count] = result['duration_cycle']
-
- for d_complexity, c_values in sorted(datas.items(), key=lambda e: int(e[0])):
- data_file_name = 'DATA_{}'.format(str(d_complexity))
- try:
- os.unlink(data_file_name)
- except FileNotFoundError:
- pass
- with open(data_file_name, 'w+') as file:
- file.writelines(['# (COMPLEXITY {}) SUBJECTS CORES_{}\n'.format(
- str(d_complexity),
- ' CORES_'.join(map(str, range(1, max_cores+1))),
- )])
- for d_subject_count, d_cores in c_values.items():
- line = '{} {}\n'.format(
- str(d_subject_count),
- ' '.join(map(str, d_cores.values())),
- )
- file.writelines([line])
-
- """
- subj_core = []
- for subj, core_v in c_values.items():
- for core_nb in core_v.keys():
- subj_core.append((subj, core_nb))
- file.writelines(['# (COMPLEXITY {}) SUBJECTS CORES_{}\n'.format(
- str(d_complexity),
- ' '.join([
- 'SUBJ_{}_COR_{}'.format(
- subj, core_nb,
- ) for subj, core_nb in subj_core
- ])
- )])
- """
-
- for d_complexity, c_values in datas.items():
- print('')
- print('gnuplot -p -e "set title \\"COMPLEXITY_{}\\"; plot {}"'.format(
- str(d_complexity),
- ','.join([
- ' \'DATA_{}\' using 1:{} title \'CORE_{}\' with lines'.format(
- d_complexity,
- d_core+1,
- d_core,
- ) for d_core in range(1, max_cores+1)
- ])
- ))
-
-
- if __name__ == '__main__':
- main()
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