def get_ops(shape, omega, b, t_pml=10):
sc0 = dg.Grid(
stretched_coords.get_coeffs(shape[2], 0.0, t_pml,
omega).astype(np.complex128))
sc1 = dg.Grid(
stretched_coords.get_coeffs(shape[2], 0.5, t_pml,
omega).astype(np.complex128))
if b.dtype.type is np.complex128:
cuda_type = 'pycuda::complex<double>'
elif b.dtype.type is np.complex64:
cuda_type = 'pycuda::complex<float>'
else:
print 'error on data type of b'
mA_func = grid_traverse.TraverseKernel(shape, \
jinja_env.get_template('simplewave_multA.cu').render(w2=omega**2, zz=shape[2]), \
(cuda_type, 's0__f'), (cuda_type, 's1__f'), \
(cuda_type, 'u'), (cuda_type, 'v'))
def multA(x, y):
mA_func(sc0, sc1, x, y)
return
mAT_func = grid_traverse.TraverseKernel(shape, \
jinja_env.get_template('simplewave_multAT.cu').render(w2=omega**2, zz=shape[2]), \
(cuda_type, 's0__f'), (cuda_type, 's1__f'), \
(cuda_type, 'u'), (cuda_type, 'v'))
def multAT(x, y):
mAT_func(sc0, sc1, x, y)
return
def dot(x, y):
return x.dot(y)
def axby(a, x, b, y):
y.aby(b, a, x)
def copy(x):
return x.dup()
return dg.Grid(b), \
{ 'multA': multA, \
'multAT': multAT, \
'dot': dot, \
'axby': axby, \
'copy': copy}
def get_ops(omega, b, t_pml=10):
""" define the operations """
def copy(x):
return x.dup()
def dot(x, y):
return x.dot(y)
def axby(a, x, b, y):
y.aby(b, a, x)
def norm(x):
return x.norm()
shape = b[0].shape
cuda_type = 'pycuda::complex<double>'
field_names = ('Ex', 'Ey', 'Ez', 'Ax', 'Ay', 'Az', \
'sx0_f', 'sy0_f', 'sz0_f', 'sx1_f', 'sy1_f', 'sz1_f')
sc_pml_0 = VecField(\
*[stretched_coords.get_coeffs(len, 0.0, t_pml, omega).astype(np.complex128) \
for len in shape])
sc_pml_1 = VecField(\
*[stretched_coords.get_coeffs(len, 0.5, t_pml, omega).astype(np.complex128) \
for len in shape])
mA_func = grid_traverse.TraverseKernel(shape, \
jinja_env.get_template('maxwell_multA.cu').\
render(w2=omega**2, dims=shape, type=cuda_type), \
*[(cuda_type, name) for name in field_names])
def multA(x, y):
mA_func(x.f[0], x.f[1], x.f[2], \
y.f[0], y.f[1], y.f[2], \
sc_pml_0.f[0], sc_pml_0.f[1], sc_pml_0.f[2], \
sc_pml_1.f[0], sc_pml_1.f[1], sc_pml_1.f[2])
mAT_func = grid_traverse.TraverseKernel(shape, \
jinja_env.get_template('maxwell_multAT.cu').\
render(w2=omega**2, dims=shape, type=cuda_type), \
*[(cuda_type, name) for name in field_names])
def multAT(x, y):
mAT_func(x.f[0], x.f[1], x.f[2], \
y.f[0], y.f[1], y.f[2], \
sc_pml_0.f[0], sc_pml_0.f[1], sc_pml_0.f[2], \
sc_pml_1.f[0], sc_pml_1.f[1], sc_pml_1.f[2])
return {'multA': multA, 'multAT': multAT, 'copy': copy, 'dot': dot, \
'norm': norm, 'axby': axby}, VecField(*b)
def get_ops(shape, omega, b, t_pml=10):
sc0 = dg.Grid(stretched_coords.get_coeffs(shape[2], 0.0, t_pml, omega).astype(np.complex128))
sc1 = dg.Grid(stretched_coords.get_coeffs(shape[2], 0.5, t_pml, omega).astype(np.complex128))
if b.dtype.type is np.complex128:
cuda_type = 'pycuda::complex<double>'
elif b.dtype.type is np.complex64:
cuda_type = 'pycuda::complex<float>'
else:
print 'error on data type of b'
mA_func = grid_traverse.TraverseKernel(shape, \
jinja_env.get_template('simplewave_multA.cu').render(w2=omega**2, zz=shape[2]), \
(cuda_type, 's0__f'), (cuda_type, 's1__f'), \
(cuda_type, 'u'), (cuda_type, 'v'))
def multA(x, y):
mA_func(sc0, sc1, x, y)
return
mAT_func = grid_traverse.TraverseKernel(shape, \
jinja_env.get_template('simplewave_multAT.cu').render(w2=omega**2, zz=shape[2]), \
(cuda_type, 's0__f'), (cuda_type, 's1__f'), \
(cuda_type, 'u'), (cuda_type, 'v'))
def multAT(x, y):
mAT_func(sc0, sc1, x, y)
return
def dot(x, y):
return x.dot(y)
def axby(a, x, b, y):
y.aby(b, a, x)
def copy(x):
return x.dup()
return dg.Grid(b), \
{ 'multA': multA, \
'multAT': multAT, \
'dot': dot, \
'axby': axby, \
'copy': copy}