# SA and Sb reside on rank zero, so solving the equation is
# done there.
if (MPI.COMM_WORLD.Get_rank() == 0):
# Solve using NumPy
[x, res, rank, s] = np.linalg.lstsq(SA.Matrix, Sb.Matrix)
else:
x = None
telp = time.time() - t0
# Distribute the solution so to compute residual in a distributed fashion
x = MPI.COMM_WORLD.bcast(x, root=0)
# Convert x to a distributed matrix.
# Here we give the type explictly, but the value used is the default.
x = elemhelper.local2distributed(x, type=elem.DistMatrix_d)
# Compute residual
r = elem.DistMatrix_d()
elem.Copy(b, r)
elem.Gemv(elem.NORMAL, -1.0, A1, x, 1.0, r)
res = elem.Norm(r)
if (MPI.COMM_WORLD.Get_rank() == 0):
print "%(name)s:\tSketched solution residual %(val).3f\ttook %(elp).2e sec" %\
{"name" : sname, "val" : res, "elp" : telp}
# As with all Python object they will be automatically garbage
# collected, and the associated memory will be freed.
# You can also explicitly free them.
del S # S = 0 will also free memory.
# SA and Sb reside on rank zero, so solving the equation is
# done there.
if (MPI.COMM_WORLD.Get_rank() == 0):
# Solve using NumPy
[x, res, rank, s] = np.linalg.lstsq(SA.Matrix, Sb.Matrix)
else:
x = None
telp = time.time() - t0
# Distribute the solution so to compute residual in a distributed fashion
x = MPI.COMM_WORLD.bcast(x, root=0)
# Convert x to a distributed matrix.
# Here we give the type explictly, but the value used is the default.
x = elemhelper.local2distributed(x, type=elem.DistMatrix_d)
# Compute residual
r = elem.DistMatrix_d()
elem.Copy(b, r)
elem.Gemv(elem.NORMAL, -1.0, A1, x, 1.0, r)
res = elem.Norm(r)
if (MPI.COMM_WORLD.Get_rank() == 0):
print "%(name)s:\tSketched solution residual %(val).3f\ttook %(elp).2e sec" %\
{"name" : sname, "val" : res, "elp" : telp}
# As with all Python object they will be automatically garbage
# collected, and the associated memory will be freed.
# You can also explicitly free them.
del S # S = 0 will also free memory.
# SA and Sb reside on rank zero, so solving the equation is
# done there.
if (MPI.COMM_WORLD.Get_rank() == 0):
# Solve using NumPy
[x, res, rank, s] = np.linalg.lstsq(SA.Matrix().ToNumPy(), Sb.Matrix().ToNumPy())
else:
x = None
telp = time.time() - t0
# Distribute the solution so to compute residual in a distributed fashion
x = MPI.COMM_WORLD.bcast(x, root=0)
# Convert x to a distributed matrix.
# Here we give the type explictly, but the value used is the default.
x = elemhelper.local2distributed(x, type=El.DistMatrix)
# Compute residual
r = El.DistMatrix()
El.Copy(b, r)
El.Gemv(El.NORMAL, -1.0, A1, x, 1.0, r)
res = El.Norm(r)
if (MPI.COMM_WORLD.Get_rank() == 0):
print "%(name)s:\tSketched solution residual %(val).3f\ttook %(elp).2e sec" %\
{"name" : sname, "val" : res, "elp" : telp}
# As with all Python object they will be automatically garbage
# collected, and the associated memory will be freed.
# You can also explicitly free them.
del S # S = 0 will also free memory.
# done there.
if (MPI.COMM_WORLD.Get_rank() == 0):
# Solve using NumPy
[x, res, rank, s] = np.linalg.lstsq(SA.Matrix().ToNumPy(),
Sb.Matrix().ToNumPy())
else:
x = None
telp = time.time() - t0
# Distribute the solution so to compute residual in a distributed fashion
x = MPI.COMM_WORLD.bcast(x, root=0)
# Convert x to a distributed matrix.
# Here we give the type explictly, but the value used is the default.
x = elemhelper.local2distributed(x, type=El.DistMatrix)
# Compute residual
r = El.DistMatrix()
El.Copy(b, r)
El.Gemv(El.NORMAL, -1.0, A1, x, 1.0, r)
res = El.Norm(r)
if (MPI.COMM_WORLD.Get_rank() == 0):
print "%(name)s:\tSketched solution residual %(val).3f\ttook %(elp).2e sec" %\
{"name" : sname, "val" : res, "elp" : telp}
# As with all Python object they will be automatically garbage
# collected, and the associated memory will be freed.
# You can also explicitly free them.
del S # S = 0 will also free memory.
