def update_timestep(self, yieldstep, finaltime):
#LINDA:
# moved the calculation so that it is done after timestep
# has been broadcast
# # Calculate local timestep
# Domain.update_timestep(self, yieldstep, finaltime)
import time
t0 = time.time()
# For some reason it looks like pypar only reduces numeric arrays
# hence we need to create some dummy arrays for communication
ltimestep = num.ones(1, num.float)
ltimestep[0] = self.flux_timestep
gtimestep = num.zeros(1, num.float) # Buffer for results
#ltimestep = self.flux_timeste
#print self.processor, ltimestep, gtimestep
gtimestep = pypar.reduce(ltimestep, pypar.MIN, 0, buffer=gtimestep)
#print self.processor, ltimestep, gtimestep
pypar.broadcast(gtimestep, 0)
#print self.processor, ltimestep, gtimestep
self.flux_timestep = gtimestep[0]
self.communication_reduce_time += time.time() - t0
# LINDA:
# Now update time stats
# Calculate local timestep
Domain.update_timestep(self, yieldstep, finaltime)
def update_timestep(self, yieldstep, finaltime):
#LINDA:
# moved the calculation so that it is done after timestep
# has been broadcast
# # Calculate local timestep
# Domain.update_timestep(self, yieldstep, finaltime)
import time
t0 = time.time()
# For some reason it looks like pypar only reduces numeric arrays
# hence we need to create some dummy arrays for communication
ltimestep = num.ones( 1, num.float )
ltimestep[0] = self.flux_timestep
gtimestep = num.zeros( 1, num.float ) # Buffer for results
#ltimestep = self.flux_timeste
#print self.processor, ltimestep, gtimestep
gtimestep = pypar.reduce(ltimestep, pypar.MIN, 0, buffer=gtimestep)
#print self.processor, ltimestep, gtimestep
pypar.broadcast(gtimestep,0)
#print self.processor, ltimestep, gtimestep
self.flux_timestep = gtimestep[0]
self.communication_reduce_time += time.time()-t0
# LINDA:
# Now update time stats
# Calculate local timestep
Domain.update_timestep(self, yieldstep, finaltime)
def dpsi_dt(self, psi, t): """ dp_dt = dpsi_dt(psi, t) Method that serves as input to the odeint() function. Calculates dpsi/dt = -i S**-1 H(t) psi. Parameters ---------- psi : 1D complex array. Wavefunction. t : float. Time. Returns ------- dp_dt : 1D complex array. Derivative of the wavefunction. """ # #To avoid doing anything twice. (odeint tends to do that.) # #--------------------------------------------------------- # novel, result = self.check_novelty(t,psi) # if not novel: # if self.my_id == 0: # print "Time: %2.2f / %2.2f au. Runtime: %2.2f---"%( # t, self.total_duration, (time.time() - self.t_0)/60.) # self.debug_norm(t, psi, result) # # return result # ########################################################## #Making a complex array. psi_complex = psi[:len(psi)/2] + 1j * psi[len(psi)/2:] dp_dt_complex = zeros(psi_complex.shape, dtype = complex) dp_dt_buffer= zeros(psi_complex.shape, dtype = complex) #Do operations. mat_vec = self.mat_vec_product(psi_complex, t) dp_dt_complex[self.my_slice] = self.solve_overlap(-1j * mat_vec) #Add and redistribute. dp_dt_complex = pypar.reduce(dp_dt_complex, pypar.SUM, 0, buffer = dp_dt_buffer) dp_dt_buffer = dp_dt_complex.copy() dp_dt_complex = pypar.broadcast(dp_dt_buffer, 0) #Making a float array. dp_dt = r_[real(dp_dt_buffer), imag(dp_dt_buffer)] if self.my_id == 0: print "Time: %2.2f / %2.2f au. Runtime: %2.2f"%( t, self.total_duration, (time.time() - self.t_0)/60.) self.debug_norm(t, psi, dp_dt) #Store latest result. ---------------------------------- self.prev_out = dp_dt ############################3###########################3 return dp_dt
def raw_reduce(x, buffer, op, source, vanilla=0):
pypar.reduce(x, op, source, buffer=buffer, vanilla=0)
for i in range(numproc):
assert numpy.allclose(testArray, X[(i * M):((i + 1) * M), :])
assert numpy.allclose(X, Y)
print "Gather communication of 2D numeric complex arrays OK"
########################################################
# Test reduce
#######################################################
N = 17 # Number of elements
# Create one (different) array on each processor
#
testArray = numpy.array(range(N), 'i') * (myid + 1)
X = numpy.zeros(N, 'i') # Buffer for results
pypar.reduce(testArray, pypar.SUM, 0, buffer=X)
if myid == 0:
Y = numpy.zeros(N, 'i')
for i in range(numproc):
Y = Y + numpy.array(range(N), 'i') * (i + 1)
assert numpy.allclose(X, Y)
print 'Buffered reduce using pypar.SUM OK'
pypar.reduce(testArray, pypar.MAX, 0, buffer=X)
if myid == 0:
Y = numpy.array(range(N)) * numproc
assert numpy.allclose(X, Y)
print 'Buffered reduce using pypar.MAX OK'
pypar.reduce(testArray, pypar.MIN, 0, buffer=X)
for i in range(numproc):
assert numpy.allclose(testArray, X[(i * M): ((i+1)*M), :])
assert numpy.allclose(X, Y)
print "Gather communication of 2D numeric complex arrays OK"
########################################################
# Test reduce
#######################################################
N = 17 #Number of elements
# Create one (different) array on each processor
#
testArray = numpy.array(range(N), 'i') * (myid+1)
X = numpy.zeros(N, 'i') # Buffer for results
pypar.reduce(testArray, pypar.SUM, 0, buffer=X)
if myid == 0:
Y = numpy.zeros(N, 'i')
for i in range(numproc):
Y = Y + numpy.array(range(N), 'i')*(i+1)
assert numpy.allclose(X, Y)
print 'Buffered reduce using pypar.SUM OK'
pypar.reduce(testArray, pypar.MAX, 0, buffer=X)
if myid == 0:
Y = numpy.array(range(N))*numproc
assert numpy.allclose(X, Y)
print 'Buffered reduce using pypar.MAX OK'
pypar.reduce(testArray, pypar.MIN, 0, buffer=X)
