source: inundation/ga/storm_surge/parallel/parallel_advection.py @ 1452

import sys
from os import sep
sys.path.append('..'+sep+'pyvolution')

"""Class Parallel_Domain -
2D triangular domains for finite-volume computations of
the advection equation, with extra structures to allow
communication between other Parallel_Domains and itself

This module contains a specialisation of class Domain from module advection.py

Ole Nielsen, Stephen Roberts, Duncan Gray, Christopher Zoppou
Geoscience Australia, 2004
"""

from advection import *
Advection_Domain = Domain
from Numeric import zeros, Float, Int, ones, allclose, array
import pypar


class Parallel_Domain(Advection_Domain):

    def __init__(self, coordinates, vertices, boundary = None,
                 full_send_dict = None, ghost_recv_dict = None,
                 velocity = None):

        self.processor = pypar.rank()
        self.numproc   = pypar.size()
        #print 'Processor %d'%self.processor
        #velocity = [(self.processor+1),0.0]

        #print 'velocity',velocity

        Advection_Domain.__init__(self, coordinates, vertices, boundary, velocity)

        N = self.number_of_elements

        self.processor = pypar.rank()
        self.numproc   = pypar.size()

        self.full_send_dict  = full_send_dict
        self.ghost_recv_dict = ghost_recv_dict

        #print self.full_send_dict
        #print self.ghost_recv_dict

    def check_integrity(self):
        Advection_Domain.check_integrity(self)

        msg = 'Will need to check global and local numbering'
        assert self.conserved_quantities[0] == 'stage', msg



    def update_timestep(self, yieldstep, finaltime):

        # Calculate local timestep
        Advection_Domain.update_timestep(self, yieldstep, finaltime)

        # For some reason it looks like pypar only reduces numeric arrays
        # hence we need to create some dummy arrays for communication
        ltimestep = ones( 1, Float )
        ltimestep[0] = self.timestep

        gtimestep = zeros( 1, Float) # Buffer for results

        pypar.raw_reduce(ltimestep, gtimestep, pypar.MIN, 0)
        pypar.broadcast(gtimestep,0)
        #pypar.Barrier()

        self.timestep = gtimestep[0]



    def update_ghosts(self):

        # We must send the information from the full cells and
        # receive the information for the ghost cells
        # We have a dictionary of lists with ghosts expecting updates from
        # the separate processors

        from weave import converters

        stage_cv = self.quantities['stage'].centroid_values

        # update of non-local ghost cells
        for iproc in range(self.numproc):

            if iproc == self.processor:
                #Send data from iproc processor to other processors
                for send_proc in self.full_send_dict:
                    if send_proc != iproc:
                        Idf  = self.full_send_dict[send_proc][0]
                        Xout = self.full_send_dict[send_proc][1]
                        N = len(Xout)

                        """
                        # Original python Code
                        for i in range(N):
                            Xout[i] = stage_cv[Idf[i]]
                        """
                        code1 = """
                        for (int i=0; i<N ; i++){
                            Xout(i) = stage_cv(Idf(i));
                        }
                        """
                        weave.inline(code1, ['stage_cv','Idf','Xout','N'],
                                     type_converters = converters.blitz, compiler='gcc');

                        pypar.send(Xout,send_proc)
                        #print 'Processor %d Sending to Processor %d'%(self.processor,send_proc)
            else:
                #Receive data from the iproc processor
                if  self.ghost_recv_dict.has_key(iproc):
                    Idg = self.ghost_recv_dict[iproc][0]
                    X   = self.ghost_recv_dict[iproc][1]

                    X = pypar.receive(iproc,X)
                    N = len(X)
                    #print 'Processor %d receiving from Processor %d'%(self.processor,iproc)
                    for i in range(N):
                        stage_cv[Idg[i]] = X[i]
            pypar.barrier()

        #local update of ghost cells
        iproc = self.processor
        if self.full_send_dict.has_key(iproc):
            Idf  = self.full_send_dict[iproc][0]
            #print Idf
            Idg  = self.ghost_recv_dict[iproc][0]
            N = len(Idg)
            #print Idg
            for i in range(N):
                #print i,Idg[i],Idf[i]
                stage_cv[Idg[i]] = stage_cv[Idf[i]]


#        if self.ghosts is not None:
#            stage_cv = self.quantities['stage'].centroid_values
#            for triangle in self.ghosts:
#                stage_cv[triangle] = stage_cv[self.ghosts[triangle]]


    def write_time(self):
        if self.min_timestep == self.max_timestep:
            print 'Processor %d, Time = %.4f, delta t = %.8f, steps=%d (%d)'\
                  %(self.processor, self.time, self.min_timestep, self.number_of_steps,
                    self.number_of_first_order_steps)
        elif self.min_timestep > self.max_timestep:
            print 'Processor %d, Time = %.4f, steps=%d (%d)'\
                  %(self.processor, self.time, self.number_of_steps,
                    self.number_of_first_order_steps)
        else:
            print 'Processor %d, Time = %.4f, delta t in [%.8f, %.8f], steps=%d (%d)'\
                  %(self.processor, self.time, self.min_timestep,
                    self.max_timestep, self.number_of_steps,
                    self.number_of_first_order_steps)



    def evolve(self, yieldstep = None, finaltime = None):
        """Specialisation of basic evolve method from parent class
        """

        #Initialise real time viz if requested
        if self.time == 0.0:
            pass

        #Call basic machinery from parent class
        for t in Advection_Domain.evolve(self, yieldstep, finaltime):

            #Pass control on to outer loop for more specific actions
            yield(t)