.. _parallelization:

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Parallelization
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The parallel implementaion for both PODS and Differential Evolution use a synchronous parallel framework.

This parallelization strategy of the algorithm allows simultaneous function evaluations on multiple processors (cores) in batch mode and can greatly speedup the calibration of computationally expensive models by reducing the calibration time and making the calibration of some extremely expensive models possible.

Since real problems (different from math test functions) are usually model suit with a batch of simualtaiton files, it's usually technique difficult to implementaion parallelization with real problems. This is escipically true when the problems involues large number of files and with intensive postprcessing.

In PODS two variable **iterid** and **simid** are frequently use for the parallelization control.
 
	**iterid** indicates the index of iterations.
	
	**simid** indicates the index of simulation of a batch of simultaneous simulations in each iteration. 

**iterid** and **simid** are used both in the main string of the algrithom and in the objective funciton evaluations to modify simualtion files, launch simuation executation, connect the each subprocess with main string of algorithm

	
The parallel controller used in PODS is the multiprocesisng.pool() function in python. 

   .. code-block:: python
   
    #Initialization the pool class.
		
	pool = multiprocessing.Pool(nprocessors)
		
	#zip the a list of parameter vectors with iteration and simulation ID.
		
    paramters = zip(params, simid, iterid)
		
	#Use pool.map to run a batch of evaluations. self.obj_func is the evaltions function take paramters as input.
		
    objfuns = pool.map(self.obj_func, paramters)


In the objective evaluaiton function obj_func() the varialbe "iterid** and **simid** are used as index for the iteration ID and Simulation ID. This function will be called by the optimizaiton algorithm (master) to do evaluation. Users need to set up the home_dir which will be used to nevigate the dir for simualtion evaluations

.. code-block:: python

	def obj_func(paramters):
		"""
		The function for objective function evaluation.
		:param paramters: A tuple (x, simid, iterid)
			x: the dim dimensional parameter vector
			simid: the index of simulation ID in each iteration
			iterid: the index of iteration ID
			simid and iterid is used control a batch of simulations running simultaneously in each iteration.
		:return: the objective function value [subobj1, subobj2] (a list of multiple sub objectives)
		"""
		data = delft3d_1objs(dim=4) #Initializaiton for the problem class
		data.home_dir = '/Users/xiawei/Desktop/PODS/examples/'
		x, simid, iterid = paramters
		simid = simid
		iterid = iterid
		result = data.objfunction(x, simid, iterid)
		return result