def execute(matrix, para):
# loop over each sampling rate and each round
if para["parallelMode"]: # run on multiple processes
pool = multiprocessing.Pool()
for rate in para["samplingRate"]:
pool.apply_async(monitoring, (matrix, rate, para))
pool.close()
pool.join()
else: # run on single processes
for rate in para["samplingRate"]:
monitoring(matrix, rate, para)
# summarize the dumped results
evallib.summarizeResult(para)
def execute(matrix, para):
# loop over each density and each round
if para['parallelMode']: # run on multiple processes
pool = multiprocessing.Pool()
for den in para['density']:
for roundId in xrange(para['rounds']):
pool.apply_async(executeOneSetting, (matrix, den, roundId, para))
pool.close()
pool.join()
else: # run on single processes
for den in para['density']:
for roundId in xrange(para['rounds']):
executeOneSetting(matrix, den, roundId, para)
# summarize the dumped results
evallib.summarizeResult(para)
def execute(matrix, para):
# loop over each density and each round
if para['parallelMode']: # run on multiple processes
pool = multiprocessing.Pool()
for den in para['density']:
for roundId in xrange(para['rounds']):
pool.apply_async(executeOneSetting, (matrix, den, roundId, para))
pool.close()
pool.join()
else: # run on single processes
for den in para['density']:
for roundId in xrange(para['rounds']):
executeOneSetting(matrix, den, roundId, para)
# summarize the dumped results
evallib.summarizeResult(para)
def execute(matrix, para):
# loop over each sampling rate and each round
if para['parallelMode']: # run on multiple processes
pool = multiprocessing.Pool()
for rate in para['samplingRate']:
for roundId in xrange(para['rounds']):
pool.apply_async(monitoring, (matrix, rate, roundId, para))
pool.close()
pool.join()
else: # run on single processes
for rate in para['samplingRate']:
for roundId in xrange(para['rounds']):
monitoring(matrix, rate, roundId, para)
# process the dumped results
evallib.summarizeResult(para)