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)