def integrate_in_time(self, insn):
from hedge.timestep.multirate_ab.methods import CO_FAST
from hedge.timestep.multirate_ab.methods import HIST_F2F, HIST_S2F, HIST_F2S, HIST_S2S
if insn.component == CO_FAST:
self_hn, cross_hn = HIST_F2F, HIST_S2F
else:
self_hn, cross_hn = HIST_S2S, HIST_F2S
start_time_level = self.eval_expr(insn.start)
end_time_level = self.eval_expr(insn.end)
self_coefficients = self.stepper.get_coefficients(
self.stepper.hist_is_fast[self_hn],
self.hist_head_time_level[self_hn],
start_time_level,
end_time_level,
self.stepper.orders[self_hn],
)
cross_coefficients = self.stepper.get_coefficients(
self.stepper.hist_is_fast[cross_hn],
self.hist_head_time_level[cross_hn],
start_time_level,
end_time_level,
self.stepper.orders[cross_hn],
)
if start_time_level == 0 or (insn.result_name not in self.context):
my_y = self.last_y[insn.component]
assert start_time_level == 0
else:
my_y = self.context[insn.result_name]()
assert start_time_level == self.var_time_level[insn.result_name]
hists = self.stepper.histories
self_history = hists[self_hn][:]
cross_history = hists[cross_hn][:]
if False:
my_integrated_y = memoize(
lambda: my_y
+ self.stepper.large_dt
* (_linear_comb(self_coefficients, self_history) + _linear_comb(cross_coefficients, cross_history))
)
else:
my_new_y = my_y + self.stepper.large_dt * (
_linear_comb(self_coefficients, self_history) + _linear_comb(cross_coefficients, cross_history)
)
my_integrated_y = lambda: my_new_y
self.context[insn.result_name] = my_integrated_y
self.var_time_level[insn.result_name] = end_time_level
MRABProcessor.integrate_in_time(self, insn)
def integrate_in_time(self, insn):
from hedge.timestep.multirate_ab.methods import CO_FAST
from hedge.timestep.multirate_ab.methods import \
HIST_F2F, HIST_S2F, HIST_F2S, HIST_S2S
if insn.component == CO_FAST:
self_hn, cross_hn = HIST_F2F, HIST_S2F
else:
self_hn, cross_hn = HIST_S2S, HIST_F2S
start_time_level = self.eval_expr(insn.start)
end_time_level = self.eval_expr(insn.end)
self_coefficients = self.stepper.get_coefficients(
self.stepper.hist_is_fast[self_hn],
self.hist_head_time_level[self_hn], start_time_level,
end_time_level, self.stepper.orders[self_hn])
cross_coefficients = self.stepper.get_coefficients(
self.stepper.hist_is_fast[cross_hn],
self.hist_head_time_level[cross_hn], start_time_level,
end_time_level, self.stepper.orders[cross_hn])
if start_time_level == 0 or (insn.result_name not in self.context):
my_y = self.last_y[insn.component]
assert start_time_level == 0
else:
my_y = self.context[insn.result_name]()
assert start_time_level == \
self.var_time_level[insn.result_name]
hists = self.stepper.histories
self_history = hists[self_hn][:]
cross_history = hists[cross_hn][:]
if False:
my_integrated_y = memoize(lambda: my_y + self.stepper.large_dt * (
_linear_comb(self_coefficients, self_history) + _linear_comb(
cross_coefficients, cross_history)))
else:
my_new_y = my_y + self.stepper.large_dt * (
_linear_comb(self_coefficients, self_history) +
_linear_comb(cross_coefficients, cross_history))
my_integrated_y = lambda: my_new_y
self.context[insn.result_name] = my_integrated_y
self.var_time_level[insn.result_name] = end_time_level
MRABProcessor.integrate_in_time(self, insn)
+ result)
if self.saw_double:
result = (
"#pragma OPENCL EXTENSION cl_khr_fp64: enable\n"
"#define PYOPENCL_DEFINE_CDOUBLE\n"
+ result)
return result
if _dtype_hashable:
_memoize_match_dtype_to_c_struct = memoize
else:
import json as _json
_memoize_match_dtype_to_c_struct = memoize(
key=lambda device, name, dtype, context=None:
(device, name, _dtype_to_key(dtype), context))
@_memoize_match_dtype_to_c_struct
def match_dtype_to_c_struct(device, name, dtype, context=None):
"""Return a tuple `(dtype, c_decl)` such that the C struct declaration
in `c_decl` and the structure :class:`numpy.dtype` instance `dtype`
have the same memory layout.
Note that *dtype* may be modified from the value that was passed in,
for example to insert padding.
(As a remark on implementation, this routine runs a small kernel on
the given *device* to ensure that :mod:`numpy` and C offsets and
sizes match.)
if self.saw_complex:
result = ("#include <pyopencl-complex.h>\n\n" + result)
if self.saw_double:
result = ("#pragma OPENCL EXTENSION cl_khr_fp64: enable\n"
"#define PYOPENCL_DEFINE_CDOUBLE\n" + result)
return result
if _dtype_hashable:
_memoize_match_dtype_to_c_struct = memoize
else:
import json as _json
_memoize_match_dtype_to_c_struct = memoize(
key=lambda device, name, dtype, context=None:
(device, name, _dtype_to_key(dtype), context))
@_memoize_match_dtype_to_c_struct
def match_dtype_to_c_struct(device, name, dtype, context=None):
"""Return a tuple `(dtype, c_decl)` such that the C struct declaration
in `c_decl` and the structure :class:`numpy.dtype` instance `dtype`
have the same memory layout.
Note that *dtype* may be modified from the value that was passed in,
for example to insert padding.
(As a remark on implementation, this routine runs a small kernel on
the given *device* to ensure that :mod:`numpy` and C offsets and
sizes match.)