Given the code below, I am unable to remedy the fact that only rank 0 participates in evaluations of Objective after the first iteration of the (SciPy) minimizer. Obviously the rank!=0 workers finish their initial tasking and the minimizer does not reengage these workers. My question is how can I reengage them? from scipy.optimize import minimize from mpi4py import MPI import numpy as np class Solver(): def __init__(self, SampleTimes, InitialArray): self.comm = MPI.COMM_WORLD self.rank = self.comm.Get_rank() self.size = self.comm.Get_size() self.SampleTimes = SampleTimes self.InitialArray = InitialArray self.Max = None def f(x_): return <some scalar result> def Objective(self, x): print(f"Entering Objective on rank {self.rank}") self.x = self.comm.bcast(x if self.rank == 0 else None, root=0) tstep_select = np.array_split(self.SampleTimes, self.size)[self.rank] local_results = [] for t in tstep_select: result = self.f(self.x) # Some function of x local_results.append( (t, result) ) all_results = self.comm.gather(local_results, root=0) if self.rank==0: all_results = [item for sublist in all_results for item in sublist] all_results = np.array(all_results) all_results = all_results[all_results[:,0].argsort()] scalar = np.trapz(all_results[:,1], all_results[:,0]) return -scalar def Maximize(self,): if self.rank == 0: self.Max = minimize(self.Objective, self.InitialArray) self.Max = self.comm.bcast(self.Max if self.rank==0 else None, root=0) else: while not self.Max: self.Objective(None) if __name__=='__main__': t_eval = np.linspace(0, 100, 100) Instance = Solver(SampleTimes=t_eval) print(Instance.Maximize()) Continue reading...
