for j in range(0, nTimers):
mean_timers_sec[i, j] = np.mean(timers_sec[i, j, :])
speedUp[i, :] = mean_timers_sec[0, :] / (mean_timers_sec[i, :])
print('timers')
print(mean_timers_sec)
print('speed up')
print(speedUp)
if printPlot:
fig = plt.figure(figsize=(10, 6))
plt.plot(np.arange(1, nMaxProcs + 1), np.arange(1, nMaxProcs + 1),
'--')
for j in range(0, nTimers):
plt.plot(np.arange(1, nMaxProcs + 1),
speedUp[:, j],
'o-',
label=timerNames[j])
plt.ylabel('speed up')
plt.xlabel('number of MPI processes')
plt.grid(True)
plt.legend()
plt.savefig('speedup.jpeg', dpi=800)
plt.close()
if __name__ == "__main__":
comm = Teuchos.DefaultComm.getComm()
parallelEnv = Utils.createDefaultParallelEnv(comm)
main(parallelEnv)
def setUpClass(cls):
cls.comm = Teuchos.DefaultComm.getComm()
cls.parallelEnv = Utils.createDefaultParallelEnv(cls.comm)
import numpy as np
from PyAlbany import Utils
# As discussed in example_0.py parallelEnv should be destructed
# after the destruction of all the other variables which rely on
# Kokkos.
# A better way to enforce this than calling the destructor explicitly
# for all the variables is to rely on the garbage collector capability
# of python using the scope of a function.
# At the end of the function all the internal objects (all objects which
# are not inputs or outputs) are destructed.
# Therefore, we can create a function which takes parallelEnv as input in which
# all Kokkos-related objects will be created and will not be passed as output;
# they will necessarily be destructed before parallelEnv.
# This example revisits example_0.py with the above-mentioned approach.
def main(parallelEnv):
filename = 'input_conductivity_dist_paramT.yaml'
problem = Utils.createAlbanyProblem(filename, parallelEnv)
if __name__ == "__main__":
comm = Teuchos.DefaultComm.getComm()
parallelEnv = Utils.createDefaultParallelEnv(comm,
n_threads=-1,
n_numa=-1,
device_id=-1)
main(parallelEnv)