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GlobalFixedTimeStepWithEnclaveTasking.py
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1# This file is part of the ExaHyPE2 project. For conditions of distribution and
2# use, please see the copyright notice at www.peano-framework.org
3from exahype2.solvers.PDETerms import PDETerms
4from exahype2.solvers.fv.EnclaveTasking import EnclaveTasking
5
6import jinja2
7
8from .kernels import create_compute_Riemann_kernel_for_Rusanov
9from .kernels import create_abstract_solver_declarations
10from .kernels import create_abstract_solver_definitions
11from .kernels import create_solver_declarations
12from .kernels import create_solver_definitions
13
14from .kernels import SolverVariant
15from .kernels import KernelVariant
16
17from exahype2.solvers.fv.FixedTimeSteppingCodeSnippets import FixedTimeSteppingCodeSnippets
18
19
21 def __init__(self,
22 name, patch_size, unknowns, auxiliary_variables, min_volume_h, max_volume_h, normalised_time_step_size,
23 flux=PDETerms.User_Defined_Implementation,
24 ncp=PDETerms.None_Implementation,
25 eigenvalues=PDETerms.User_Defined_Implementation,
26 boundary_conditions=PDETerms.User_Defined_Implementation,
27 refinement_criterion=PDETerms.Empty_Implementation,
28 initial_conditions=PDETerms.User_Defined_Implementation,
29 source_term=PDETerms.None_Implementation,
30 plot_grid_properties=False,
31 pde_terms_without_state=False, overlap=1
32 ):
33 """
34 time_step_size: Float
35 This is the normalised time step size w.r.t. the coarsest admissible h value. If
36 the code employs AMR on top of it and refines further, it will automatically
37 downscale the time step size accordingly. So hand in a valid time step size w.r.t.
38 to max_volume_h.
39 """
40 super(GlobalFixedTimeStepWithEnclaveTasking,self).__init__(name,
41 patch_size,
42 overlap,
43 unknowns,
44 auxiliary_variables,
45 min_volume_h,
46 max_volume_h,
47 plot_grid_properties,
48 pde_terms_without_state,
49 kernel_namespace="rusanov")
50 self._normalised_time_step_size = normalised_time_step_size
51
52 self._flux_implementation_flux_implementation = PDETerms.None_Implementation
53 self._ncp_implementation_ncp_implementation = PDETerms.None_Implementation
56
58 ncp=ncp,
59 eigenvalues=eigenvalues,
60 boundary_conditions=boundary_conditions,
61 refinement_criterion=refinement_criterion,
62 initial_conditions=initial_conditions,
63 source_term=source_term)
64
65
67 flux=None,ncp=None,
68 eigenvalues=None,
69 boundary_conditions=None,refinement_criterion=None,initial_conditions=None,source_term=None,
70 memory_location = None,
71 use_split_loop = False,
72 additional_action_set_includes = "",
73 additional_user_includes = ""
74 ):
75 """
76 If you pass in User_Defined, then the generator will create C++ stubs
77 that you have to befill manually. If you pass in None_Implementation, it
78 will create nop, i.e., no implementation or defaults. Any other string
79 is copied 1:1 into the implementation. If you pass in None, then the
80 set value so far won't be overwritten.
81
82 Please note that not all options are supported by all solvers.
83
84 This routine should be the very last invoked by the constructor.
85 """
86 if flux is not None: self._flux_implementation_flux_implementation = flux
87 if ncp is not None: self._ncp_implementation_ncp_implementation = ncp
88 if eigenvalues is not None: self._eigenvalues_implementation_eigenvalues_implementation = eigenvalues
89 if source_term is not None: self._source_term_implementation_source_term_implementation = source_term
90
91 self._compute_kernel_call_compute_kernel_call = create_compute_Riemann_kernel_for_Rusanov(
95 compute_max_eigenvalue_of_next_time_step = False,
96 solver_variant = SolverVariant.WithVirtualFunctions,
97 kernel_variant = KernelVariant.PatchWiseAoS
98 )
99
100 self._compute_kernel_call_stateless_compute_kernel_call_stateless = create_compute_Riemann_kernel_for_Rusanov(
104 compute_max_eigenvalue_of_next_time_step = False,
105 solver_variant = SolverVariant.Stateless,
106 kernel_variant = KernelVariant.PatchWiseAoS
107 )
108
113 compute_max_eigenvalue_of_next_time_step = True,
114 solver_variant = SolverVariant.Stateless,
115 kernel_variant = KernelVariant.PatchWiseAoS
116 )
117
122 compute_max_eigenvalue_of_next_time_step = True,
123 solver_variant = SolverVariant.Accelerator,
124 kernel_variant = KernelVariant.PatchWiseAoS
125 )
126
128
130 self._abstract_solver_user_declarations_abstract_solver_user_declarations += solver_code_snippets.create_abstract_solver_user_declarations()
132 self._abstract_solver_user_definitions_abstract_solver_user_definitions += solver_code_snippets.create_abstract_solver_user_definitions()
133
136
137 self._compute_time_step_size_compute_time_step_size = solver_code_snippets.create_compute_time_step_size()
138 self._compute_new_time_step_size_compute_new_time_step_size = solver_code_snippets.create_compute_new_time_step_size()
139
140 self._start_time_step_implementation_start_time_step_implementation = solver_code_snippets.create_start_time_step_implementation()
141 self._finish_time_step_implementation_finish_time_step_implementation = solver_code_snippets.create_finish_time_step_implementation()
142 self._constructor_implementation_constructor_implementation = solver_code_snippets.create_abstract_solver_constructor_statements()
143
144 super(GlobalFixedTimeStepWithEnclaveTasking,self).set_implementation(boundary_conditions, refinement_criterion, initial_conditions, memory_location, use_split_loop, additional_action_set_includes, additional_user_includes)
145
146
148 super(GlobalFixedTimeStepWithEnclaveTasking,self).create_data_structures()
150
151 @property
153 return super(GlobalFixedTimeStepWithEnclaveTasking, self).user_action_set_includes + """
154#include "exahype2/fv/rusanov/rusanov.h"
155"""
Enclave tasking variant of the Finite Volume scheme.
_optimise_patch_storage_for_global_time_stepping(self)
Make storage and loading more restrictive such that enclave data are not held in-between primary and ...
set_implementation(self, boundary_conditions, refinement_criterion, initial_conditions, memory_location, use_split_loop, additional_action_set_includes, additional_user_includes)
If you pass in User_Defined, then the generator will create C++ stubs that you have to befill manuall...
Code snippet generator for fixed time stepping in the Runge-Kutta schemes.
user_action_set_includes(self)
Add further includes to this property, if your action sets require some additional routines from othe...
set_implementation(self, flux=None, ncp=None, eigenvalues=None, boundary_conditions=None, refinement_criterion=None, initial_conditions=None, source_term=None, memory_location=None, use_split_loop=False, additional_action_set_includes="", additional_user_includes="")
If you pass in User_Defined, then the generator will create C++ stubs that you have to befill manuall...
__init__(self, name, patch_size, unknowns, auxiliary_variables, min_volume_h, max_volume_h, normalised_time_step_size, flux=PDETerms.User_Defined_Implementation, ncp=PDETerms.None_Implementation, eigenvalues=PDETerms.User_Defined_Implementation, boundary_conditions=PDETerms.User_Defined_Implementation, refinement_criterion=PDETerms.Empty_Implementation, initial_conditions=PDETerms.User_Defined_Implementation, source_term=PDETerms.None_Implementation, plot_grid_properties=False, pde_terms_without_state=False, overlap=1)
time_step_size: Float This is the normalised time step size w.r.t.
create_data_structures(self)
This routine does not really add new data, but it heavily tailors when data are stored,...