我正在尝试运行一个并行工作流，我在其中评估ParallelGroup. 如果我在少量内核上运行，它不会崩溃，但在某些时候增加节点数量会引发错误，这表明它与问题的分区方式有关。
据我所知，在设置通信表时，我从 OpenMDAO 和 PETSc 的深层地牢中得到一个与目标索引有关的错误。以下是错误回溯的打印：
File "/home/frza/git/OpenMDAO/openmdao/core/group.py", line 454, in _setup_vectors
impl=self._impl, alloc_derivs=alloc_derivs)
File "/home/frza/git/OpenMDAO/openmdao/core/group.py", line 1456, in _setup_data_transfer
self._setup_data_transfer(my_params, None, alloc_derivs)
File "/home/frza/git/OpenMDAO/openmdao/core/petsc_impl.py", line 125, in create_data_xfer
File "/home/frza/git/OpenMDAO/openmdao/core/petsc_impl.py", line 397, in __init__
tgt_idx_set = PETSc.IS().createGeneral(tgt_idxs, comm=comm)
File "PETSc/IS.pyx", line 74, in petsc4py.PETSc.IS.createGeneral (src/petsc4py.PETSc.c:74696)
tgt_idx_set = PETSc.IS().createGeneral(tgt_idxs, comm=comm)
File "PETSc/arraynpy.pxi", line 121, in petsc4py.PETSc.iarray (src/petsc4py.PETSc.c:8230)
TypeError: Cannot cast array data from dtype('int64') to dtype('int32') according to the rule 'safe'
这个答案：
引导我寻找你设置tgt_idxs向量的位置，看看它是否用正确的 dtype 定义PETSc.IntType。但是到目前为止，Petsc has generated inconsistent data当我尝试设置我认为可能导致错误的数组的 dtype 时，我只会遇到错误。
我还没有尝试按照--with-64-bit-indices我链接到的答案中的建议重新安装 PETSc。您是否运行以这种方式配置的 PETSc？
编辑：我现在已经设置了一个简化版本的问题，它复制了我得到的错误：
import numpy as np
from openmdao.api import Component, Group, Problem, IndepVarComp, \
ParallelGroup
class Model(Component):
def __init__(self, nsec, nx, nch):
super(Model, self).__init__()
self.add_output('outputs', shape=[nx+1, nch*6*3*nsec])
def solve_nonlinear(self, params, unknowns, resids):
pass
class Aggregate(Component):
def __init__(self, nsec, ncase, nx, nch, nsec_env=12):
super(Aggregate, self).__init__()
self.ncase = ncase
for i in range(ncase):
self.add_param('outputs_sec%03d'%i, shape=[nx+1, nch*6*3*nsec])
for i in range(nsec):
self.add_output('aoutput_sec%03d' % i, shape=[nsec_env, 6])
def solve_nonlinear(self, params, unknowns, resids):
pass
class ParModel(Group):
def __init__(self, nsec, ncase, nx, nch, nsec_env=12):
super(ParModel, self).__init__()
pg = self.add('pg', ParallelGroup())
promotes = ['aoutput_sec%03d' % i for i in range(nsec)]
self.add('agg', Aggregate(nsec, ncase, nx, nch, nsec_env), promotes=promotes)
for i in range(ncase):
pg.add('case%03d' % i, Model(nsec, nx, nch))
self.connect('pg.case%03d.outputs'%i, 'agg.outputs_sec%03d'%i)
if __name__ == '__main__':
from openmdao.core.mpi_wrap import MPI
if MPI:
from openmdao.core.petsc_impl import PetscImpl as impl
else:
from openmdao.core.basic_impl import BasicImpl as impl
p = Problem(impl=impl, root=Group())
root = p.root
root.add('dlb', ParModel(20, 1084, 36, 6))
import time
t0 = time.time()
p.setup()
print 'setup time', time.time() - t0
完成后，我还可以看到由于我们评估的许多案例，数据量最终变得巨大。我会看看我们是否可以以某种方式减少数据大小。我现在根本无法运行它，因为它要么因错误而崩溃：
petsc4py.PETSc.Errorpetsc4py.PETSc.Error: error code 75
[77] VecCreateMPIWithArray() line 320 in /home/MET/Python-2.7.10_Intel/opt/petsc-3.6.2/src/vec/vec/impls/mpi/pbvec.c
[77] VecSetSizes() line 1374 in /home/MET/Python-2.7.10_Intel/opt/petsc-3.6.2/src/vec/vec/interface/vector.c
[77] Arguments are incompatible
[77] Local size 86633280 cannot be larger than global size 73393408
: error code 75
或TypeError.