1 // Copyright (c) 2017-2023, Lawrence Livermore National Security, LLC and other CEED contributors. 2 // All Rights Reserved. See the top-level LICENSE and NOTICE files for details. 3 // 4 // SPDX-License-Identifier: BSD-2-Clause 5 // 6 // This file is part of CEED: http://github.com/ceed 7 8 #include "../../qfunctions/sgs_dd_training.h" 9 10 #include <petscdmplex.h> 11 12 #include "../../include/smartsim.h" 13 #include "../../navierstokes.h" 14 15 typedef struct { 16 CeedElemRestriction elem_restr_grid_aniso; 17 CeedVector grid_aniso_ceed; 18 CeedQFunctionContext sgs_dd_train_qfctx; 19 } *SGS_DD_TrainingSetupData; 20 21 static PetscErrorCode SGS_DD_TrainingSetupDataDestroy(SGS_DD_TrainingSetupData sgs_dd_train_setup_data) { 22 Ceed ceed; 23 24 PetscFunctionBeginUser; 25 PetscCall(CeedElemRestrictionGetCeed(sgs_dd_train_setup_data->elem_restr_grid_aniso, &ceed)); 26 27 PetscCallCeed(ceed, CeedElemRestrictionDestroy(&sgs_dd_train_setup_data->elem_restr_grid_aniso)); 28 PetscCallCeed(ceed, CeedVectorDestroy(&sgs_dd_train_setup_data->grid_aniso_ceed)); 29 PetscCallCeed(ceed, CeedQFunctionContextDestroy(&sgs_dd_train_setup_data->sgs_dd_train_qfctx)); 30 PetscCall(PetscFree(sgs_dd_train_setup_data)); 31 PetscFunctionReturn(PETSC_SUCCESS); 32 } 33 34 // @brief Create DM for storing data-drive SGS model inputs 35 static PetscErrorCode SGS_DD_TrainingCreateDM(DM dm_source, DM *dm_dd_training, PetscInt degree, PetscInt q_extra, PetscInt *num_components) { 36 PetscSection section; 37 38 PetscFunctionBeginUser; 39 *num_components = 12; 40 41 PetscCall(DMClone(dm_source, dm_dd_training)); 42 PetscCall(PetscObjectSetName((PetscObject)*dm_dd_training, "Data-Driven SGS Training Data")); 43 44 PetscCall(DMSetupByOrder_FEM(PETSC_TRUE, PETSC_TRUE, degree, 1, q_extra, 1, num_components, *dm_dd_training)); 45 46 PetscCall(DMGetLocalSection(*dm_dd_training, §ion)); 47 PetscCall(PetscSectionSetFieldName(section, 0, "Data-Driven SGS Training Data")); 48 PetscCall(PetscSectionSetComponentName(section, 0, 0, "SGSInput1")); 49 PetscCall(PetscSectionSetComponentName(section, 0, 1, "SGSInput2")); 50 PetscCall(PetscSectionSetComponentName(section, 0, 2, "SGSInput3")); 51 PetscCall(PetscSectionSetComponentName(section, 0, 3, "SGSInput4")); 52 PetscCall(PetscSectionSetComponentName(section, 0, 4, "SGSInput5")); 53 PetscCall(PetscSectionSetComponentName(section, 0, 5, "SGSInput6")); 54 PetscCall(PetscSectionSetComponentName(section, 0, 6, "FilteredSGSXX")); 55 PetscCall(PetscSectionSetComponentName(section, 0, 7, "FilteredSGSYY")); 56 PetscCall(PetscSectionSetComponentName(section, 0, 8, "FilteredSGSZZ")); 57 PetscCall(PetscSectionSetComponentName(section, 0, 9, "FilteredSGSYZ")); 58 PetscCall(PetscSectionSetComponentName(section, 0, 10, "FilteredSGSXZ")); 59 PetscCall(PetscSectionSetComponentName(section, 0, 11, "FilteredSGSXY")); 60 PetscFunctionReturn(PETSC_SUCCESS); 61 }; 62 63 // @brief Create CeedOperator to calculate training data for data-drive SGS model at nodes 64 static PetscErrorCode SetupTrainingDataCalculation(Ceed ceed, User user, CeedData ceed_data, ProblemData *problem, 65 SGS_DD_TrainingSetupData sgs_dd_train_setup_data) { 66 SGS_DD_TrainingData sgs_dd_train = user->sgs_dd_train; 67 CeedQFunction qf_sgs_dd_train; 68 CeedOperator op_sgs_dd_train; 69 CeedInt num_comp_grad_velo, num_comp_grid_aniso; 70 CeedVector inv_multiplicity, filtered_fields; 71 CeedElemRestriction elem_restr_inv_multiplicity, elem_restr_grad_velo, elem_restr_sgs_train; 72 DMLabel domain_label = NULL; 73 PetscInt label_value = 0, height = 0, dm_field = 0; 74 75 PetscFunctionBeginUser; 76 PetscCallCeed(ceed, CeedElemRestrictionGetNumComponents(sgs_dd_train_setup_data->elem_restr_grid_aniso, &num_comp_grid_aniso)); 77 78 PetscCall(DMPlexCeedElemRestrictionCreate(ceed, sgs_dd_train->dm_dd_training, domain_label, label_value, height, dm_field, &elem_restr_sgs_train)); 79 80 { // -- Create inverse multiplicity for correcting nodal assembly 81 CeedVector multiplicity; 82 CeedQFunction qf_multiplicity; 83 CeedOperator op_multiplicity; 84 CeedInt num_comp_q; 85 86 PetscCallCeed(ceed, CeedElemRestrictionGetNumComponents(ceed_data->elem_restr_q, &num_comp_q)); 87 PetscCallCeed(ceed, CeedElemRestrictionCreateVector(ceed_data->elem_restr_q, &multiplicity, NULL)); 88 PetscCallCeed(ceed, CeedElemRestrictionGetMultiplicity(ceed_data->elem_restr_q, multiplicity)); 89 PetscCall(DMPlexCeedElemRestrictionCollocatedCreate(ceed, sgs_dd_train->dm_dd_training, domain_label, label_value, height, 1, 90 &elem_restr_inv_multiplicity)); 91 PetscCallCeed(ceed, CeedElemRestrictionCreateVector(elem_restr_inv_multiplicity, &inv_multiplicity, NULL)); 92 93 PetscCallCeed(ceed, CeedQFunctionCreateInterior(ceed, 1, InverseMultiplicity, InverseMultiplicity_loc, &qf_multiplicity)); 94 PetscCallCeed(ceed, CeedQFunctionAddInput(qf_multiplicity, "multiplicity", num_comp_q, CEED_EVAL_NONE)); 95 PetscCallCeed(ceed, CeedQFunctionAddOutput(qf_multiplicity, "inverse multiplicity", 1, CEED_EVAL_NONE)); 96 97 PetscCallCeed(ceed, CeedOperatorCreate(ceed, qf_multiplicity, NULL, NULL, &op_multiplicity)); 98 PetscCallCeed(ceed, CeedOperatorSetName(op_multiplicity, "SGS DD Training Inputs - Create Multiplicity Scaling")); 99 PetscCallCeed(ceed, CeedOperatorSetField(op_multiplicity, "multiplicity", ceed_data->elem_restr_q, CEED_BASIS_COLLOCATED, CEED_VECTOR_ACTIVE)); 100 PetscCallCeed( 101 ceed, CeedOperatorSetField(op_multiplicity, "inverse multiplicity", elem_restr_inv_multiplicity, CEED_BASIS_COLLOCATED, CEED_VECTOR_ACTIVE)); 102 103 PetscCallCeed(ceed, CeedOperatorApply(op_multiplicity, multiplicity, inv_multiplicity, CEED_REQUEST_IMMEDIATE)); 104 105 PetscCallCeed(ceed, CeedVectorDestroy(&multiplicity)); 106 PetscCallCeed(ceed, CeedQFunctionDestroy(&qf_multiplicity)); 107 PetscCallCeed(ceed, CeedOperatorDestroy(&op_multiplicity)); 108 } 109 110 CeedElemRestriction elem_restr_filtered_state; 111 CeedInt num_comp_filtered_state; 112 { // -- Setup filtered velocity gradient projection 113 CeedBasis basis_filtered_state; 114 CeedOperatorField op_field; 115 PetscCallCeed(ceed, CeedOperatorGetFieldByName(user->diff_filter->op_rhs_ctx->op, "v0", &op_field)); 116 PetscCallCeed(ceed, CeedOperatorFieldGetElemRestriction(op_field, &elem_restr_filtered_state)); 117 PetscCallCeed(ceed, CeedElemRestrictionGetNumComponents(elem_restr_filtered_state, &num_comp_filtered_state)); 118 PetscCallCeed(ceed, CeedOperatorFieldGetBasis(op_field, &basis_filtered_state)); 119 PetscCall(VelocityGradientProjectionSetup(ceed, user, ceed_data, problem, STATEVAR_PRIMITIVE, elem_restr_filtered_state, basis_filtered_state, 120 &sgs_dd_train->filtered_grad_velo_proj)); 121 // Get velocity gradient information 122 PetscCallCeed(ceed, CeedOperatorGetFieldByName(sgs_dd_train->filtered_grad_velo_proj->l2_rhs_ctx->op, "velocity gradient", &op_field)); 123 PetscCallCeed(ceed, CeedOperatorFieldGetElemRestriction(op_field, &elem_restr_grad_velo)); 124 PetscCallCeed(ceed, CeedElemRestrictionGetNumComponents(elem_restr_grad_velo, &num_comp_grad_velo)); 125 } 126 127 CeedElemRestriction elem_restr_filtered_velo_prod; 128 CeedInt num_comp_filtered_velo_prod; 129 { // Get filtered velocity product information 130 CeedOperatorField op_field; 131 PetscCallCeed(ceed, CeedOperatorGetFieldByName(user->diff_filter->op_rhs_ctx->op, "v1", &op_field)); 132 PetscCallCeed(ceed, CeedOperatorFieldGetElemRestriction(op_field, &elem_restr_filtered_velo_prod)); 133 PetscCallCeed(ceed, CeedElemRestrictionGetNumComponents(elem_restr_filtered_velo_prod, &num_comp_filtered_velo_prod)); 134 } 135 136 // -- Create operator for generating training data at nodes 137 // Differential Filter only provides filtered primitive variables 138 PetscCallCeed(ceed, CeedQFunctionCreateInterior(ceed, 1, ComputeSGS_DDAnisotropicTrainingDataNodal_Prim, 139 ComputeSGS_DDAnisotropicTrainingDataNodal_Prim_loc, &qf_sgs_dd_train)); 140 141 PetscCallCeed(ceed, CeedQFunctionSetContext(qf_sgs_dd_train, sgs_dd_train_setup_data->sgs_dd_train_qfctx)); 142 PetscCallCeed(ceed, CeedQFunctionAddInput(qf_sgs_dd_train, "q", num_comp_filtered_state, CEED_EVAL_NONE)); 143 PetscCallCeed(ceed, CeedQFunctionAddInput(qf_sgs_dd_train, "velocity product", num_comp_filtered_velo_prod, CEED_EVAL_NONE)); 144 PetscCallCeed(ceed, CeedQFunctionAddInput(qf_sgs_dd_train, "gradient velocity", num_comp_grad_velo, CEED_EVAL_NONE)); 145 PetscCallCeed(ceed, CeedQFunctionAddInput(qf_sgs_dd_train, "anisotropy tensor", num_comp_grid_aniso, CEED_EVAL_NONE)); 146 PetscCallCeed(ceed, CeedQFunctionAddInput(qf_sgs_dd_train, "inverse multiplicity", 1, CEED_EVAL_NONE)); 147 PetscCallCeed(ceed, CeedQFunctionAddOutput(qf_sgs_dd_train, "training data", sgs_dd_train->num_comp_dd_inputs, CEED_EVAL_NONE)); 148 149 PetscCallCeed(ceed, CeedElemRestrictionCreateVector(elem_restr_filtered_state, &filtered_fields, NULL)); 150 PetscCallCeed(ceed, CeedOperatorCreate(ceed, qf_sgs_dd_train, NULL, NULL, &op_sgs_dd_train)); 151 PetscCallCeed(ceed, CeedOperatorSetField(op_sgs_dd_train, "q", elem_restr_filtered_state, CEED_BASIS_COLLOCATED, filtered_fields)); 152 PetscCallCeed(ceed, 153 CeedOperatorSetField(op_sgs_dd_train, "velocity product", elem_restr_filtered_velo_prod, CEED_BASIS_COLLOCATED, filtered_fields)); 154 PetscCallCeed(ceed, CeedOperatorSetField(op_sgs_dd_train, "gradient velocity", elem_restr_grad_velo, CEED_BASIS_COLLOCATED, CEED_VECTOR_ACTIVE)); 155 PetscCallCeed(ceed, CeedOperatorSetField(op_sgs_dd_train, "anisotropy tensor", sgs_dd_train_setup_data->elem_restr_grid_aniso, 156 CEED_BASIS_COLLOCATED, sgs_dd_train_setup_data->grid_aniso_ceed)); 157 PetscCallCeed(ceed, 158 CeedOperatorSetField(op_sgs_dd_train, "inverse multiplicity", elem_restr_inv_multiplicity, CEED_BASIS_COLLOCATED, inv_multiplicity)); 159 PetscCallCeed(ceed, CeedOperatorSetField(op_sgs_dd_train, "training data", elem_restr_sgs_train, CEED_BASIS_COLLOCATED, CEED_VECTOR_ACTIVE)); 160 161 PetscCall(OperatorApplyContextCreate(sgs_dd_train->filtered_grad_velo_proj->dm, sgs_dd_train->dm_dd_training, ceed, op_sgs_dd_train, NULL, NULL, 162 NULL, NULL, &sgs_dd_train->op_training_data_calc_ctx)); 163 164 PetscCallCeed(ceed, CeedVectorDestroy(&inv_multiplicity)); 165 PetscCallCeed(ceed, CeedVectorDestroy(&filtered_fields)); 166 PetscCallCeed(ceed, CeedElemRestrictionDestroy(&elem_restr_inv_multiplicity)); 167 PetscCallCeed(ceed, CeedQFunctionDestroy(&qf_sgs_dd_train)); 168 PetscCallCeed(ceed, CeedOperatorDestroy(&op_sgs_dd_train)); 169 PetscFunctionReturn(PETSC_SUCCESS); 170 } 171 172 PetscErrorCode SGS_DD_TrainingSetup(Ceed ceed, User user, CeedData ceed_data, ProblemData *problem) { 173 SGS_DDTrainingContext sgsdd_train_qfctx; 174 SGS_DD_TrainingSetupData sgs_dd_train_setup_data; 175 176 PetscFunctionBeginUser; 177 if (!user->diff_filter) PetscCall(DifferentialFilterSetup(ceed, user, ceed_data, problem)); 178 if (!user->smartsim) PetscCall(SmartSimSetup(user)); 179 180 PetscCall(PetscNew(&sgsdd_train_qfctx)); 181 PetscCall(PetscNew(&sgs_dd_train_setup_data)); 182 PetscCall(PetscNew(&user->sgs_dd_train)); 183 SGS_DD_TrainingData sgs_dd_train = user->sgs_dd_train; 184 185 sgs_dd_train->overwrite_training_data = PETSC_TRUE; 186 sgs_dd_train->write_data_interval = 1; 187 PetscOptionsBegin(user->comm, NULL, "SGS Data-Driven Training Options", NULL); 188 PetscCall(PetscOptionsInt("-sgs_train_write_data_interval", "Number of timesteps between writing data into database", NULL, 189 sgs_dd_train->write_data_interval, &sgs_dd_train->write_data_interval, NULL)); 190 PetscCall(PetscOptionsBool("-sgs_train_overwrite_data", "Overwrite old training data in the database", NULL, sgs_dd_train->overwrite_training_data, 191 &sgs_dd_train->overwrite_training_data, NULL)); 192 PetscOptionsEnd(); 193 194 // -- Create DM for storing training data 195 PetscCall(SGS_DD_TrainingCreateDM(user->dm, &sgs_dd_train->dm_dd_training, user->app_ctx->degree, user->app_ctx->q_extra, 196 &sgs_dd_train->num_comp_dd_inputs)); 197 198 { // -- Create QFunction Context 199 NewtonianIdealGasContext gas; 200 PetscCallCeed(ceed, CeedQFunctionContextGetDataRead(problem->apply_vol_ifunction.qfunction_context, CEED_MEM_HOST, &gas)); 201 sgsdd_train_qfctx->gas = *gas; 202 PetscCallCeed(ceed, CeedQFunctionContextRestoreDataRead(problem->apply_vol_ifunction.qfunction_context, &gas)); 203 PetscCallCeed(ceed, CeedQFunctionContextCreate(user->ceed, &sgs_dd_train_setup_data->sgs_dd_train_qfctx)); 204 PetscCallCeed(ceed, CeedQFunctionContextSetData(sgs_dd_train_setup_data->sgs_dd_train_qfctx, CEED_MEM_HOST, CEED_USE_POINTER, 205 sizeof(*sgsdd_train_qfctx), sgsdd_train_qfctx)); 206 PetscCallCeed(ceed, CeedQFunctionContextSetDataDestroy(sgs_dd_train_setup_data->sgs_dd_train_qfctx, CEED_MEM_HOST, FreeContextPetsc)); 207 } 208 209 { // -- Send training data array info to SmartRedis database 210 PetscMPIInt rank, num_ranks; 211 SmartSimData smartsim = user->smartsim; 212 PetscCallMPI(MPI_Comm_rank(user->comm, &rank)); 213 PetscCallMPI(MPI_Comm_size(user->comm, &num_ranks)); 214 215 { 216 PetscSection global_section; 217 PetscInt num_dofs, num_comps; 218 PetscCall(DMGetGlobalSection(sgs_dd_train->dm_dd_training, &global_section)); 219 PetscCall(DMGetGlobalVectorInfo(sgs_dd_train->dm_dd_training, &num_dofs, NULL, NULL)); 220 PetscCall(PetscSectionGetFieldComponents(global_section, 0, &num_comps)); 221 sgs_dd_train->training_data_array_dims[0] = num_dofs / num_comps; 222 sgs_dd_train->training_data_array_dims[1] = num_comps; 223 } 224 225 if (rank % smartsim->collocated_database_num_ranks == 0) { 226 size_t array_info_dim = 6; 227 PetscInt array_info[6] = {0}, num_features = 6; 228 229 array_info[0] = sgs_dd_train->training_data_array_dims[0]; 230 array_info[1] = sgs_dd_train->training_data_array_dims[1]; 231 array_info[2] = num_features; 232 array_info[3] = num_ranks; 233 array_info[4] = smartsim->collocated_database_num_ranks; 234 array_info[5] = rank; 235 236 PetscSmartRedisCall(put_tensor(smartsim->client, "sizeInfo", 8, array_info, &array_info_dim, 1, SRTensorTypeInt32, SRMemLayoutContiguous)); 237 PetscCall(SmartRedisVerifyPutTensor(smartsim->client, "sizeInfo", 8)); 238 239 // -- Send array that communicates if tensors are overwritten in database 240 PetscInt tensor_overwrite[2] = {sgs_dd_train->overwrite_training_data}; 241 size_t dim_2[1] = {2}; 242 PetscSmartRedisCall(put_tensor(smartsim->client, "tensor-ow", 9, tensor_overwrite, dim_2, 1, SRTensorTypeInt32, SRMemLayoutContiguous)); 243 PetscCall(SmartRedisVerifyPutTensor(smartsim->client, "tensor-ow", 9)); 244 } 245 } 246 247 // -- Compute and store anisotropy tensor 248 PetscCall(GridAnisotropyTensorProjectionSetupApply(ceed, user, ceed_data, &sgs_dd_train_setup_data->elem_restr_grid_aniso, 249 &sgs_dd_train_setup_data->grid_aniso_ceed)); 250 251 // -- Create Nodal Evaluation Operator 252 PetscCall(SetupTrainingDataCalculation(ceed, user, ceed_data, problem, sgs_dd_train_setup_data)); 253 254 PetscCall(SGS_DD_TrainingSetupDataDestroy(sgs_dd_train_setup_data)); 255 PetscFunctionReturn(PETSC_SUCCESS); 256 } 257 258 PetscErrorCode TSMonitor_SGS_DD_Training(TS ts, PetscInt step_num, PetscReal solution_time, Vec Q, void *ctx) { 259 User user = (User)ctx; 260 Ceed ceed = user->ceed; 261 SGS_DD_TrainingData sgs_dd_train = user->sgs_dd_train; 262 SmartSimData smartsim = user->smartsim; 263 Vec TrainingData; 264 265 PetscFunctionBeginUser; 266 if (step_num % sgs_dd_train->write_data_interval != 0) PetscFunctionReturn(PETSC_SUCCESS); 267 PetscCall(DMGetGlobalVector(sgs_dd_train->dm_dd_training, &TrainingData)); 268 269 { // -- Compute and assemble training data 270 Vec FilteredVelocityGradient, FilteredFields, FilteredFields_loc; 271 PetscMemType filtered_fields_mem_type; 272 CeedVector filtered_fields; 273 274 PetscCall(DMGetGlobalVector(user->diff_filter->dm_filter, &FilteredFields)); 275 PetscCall(DMGetLocalVector(user->diff_filter->dm_filter, &FilteredFields_loc)); 276 277 PetscCall(DifferentialFilterApply(user, solution_time, Q, FilteredFields)); 278 PetscCall(DMGlobalToLocal(user->diff_filter->dm_filter, FilteredFields, INSERT_VALUES, FilteredFields_loc)); 279 280 PetscCall(DMGetGlobalVector(sgs_dd_train->filtered_grad_velo_proj->dm, &FilteredVelocityGradient)); 281 PetscCall(VelocityGradientProjectionApply(sgs_dd_train->filtered_grad_velo_proj, FilteredFields_loc, FilteredVelocityGradient)); 282 283 { 284 CeedOperatorField op_field; 285 PetscCallCeed(ceed, CeedOperatorGetFieldByName(sgs_dd_train->op_training_data_calc_ctx->op, "q", &op_field)); 286 PetscCallCeed(ceed, CeedOperatorFieldGetVector(op_field, &filtered_fields)); 287 } 288 PetscCall(VecP2C(FilteredFields_loc, &filtered_fields_mem_type, filtered_fields)); // filtered_fields is an implicit input 289 290 PetscCall(ApplyCeedOperatorGlobalToGlobal(FilteredVelocityGradient, TrainingData, sgs_dd_train->op_training_data_calc_ctx)); 291 292 PetscCall(VecC2P(filtered_fields, filtered_fields_mem_type, FilteredFields_loc)); 293 294 PetscCall(DMRestoreGlobalVector(sgs_dd_train->filtered_grad_velo_proj->dm, &FilteredVelocityGradient)); 295 PetscCall(DMRestoreGlobalVector(user->diff_filter->dm_filter, &FilteredFields)); 296 PetscCall(DMRestoreLocalVector(user->diff_filter->dm_filter, &FilteredFields_loc)); 297 } 298 299 { // -- Send training data to SmartSim 300 char array_key[PETSC_MAX_PATH_LEN]; 301 size_t array_key_len; 302 PetscMPIInt rank; 303 304 PetscCallMPI(MPI_Comm_rank(user->comm, &rank)); 305 306 if (sgs_dd_train->overwrite_training_data) { 307 PetscCall(PetscSNPrintf(array_key, sizeof array_key, "%s", smartsim->rank_id_name)); 308 } else { 309 PetscCall(PetscSNPrintf(array_key, sizeof array_key, "%s.%" PetscInt_FMT, smartsim->rank_id_name, step_num)); 310 } 311 PetscCall(PetscStrlen(array_key, &array_key_len)); 312 313 { 314 const PetscScalar *training_data; 315 PetscCall(VecGetArrayRead(TrainingData, &training_data)); 316 PetscSmartRedisCall(put_tensor(smartsim->client, array_key, array_key_len, (void *)training_data, sgs_dd_train->training_data_array_dims, 2, 317 SRTensorTypeDouble, SRMemLayoutContiguous)); 318 PetscCall(VecRestoreArrayRead(TrainingData, &training_data)); 319 } 320 PetscCall(SmartRedisVerifyPutTensor(smartsim->client, array_key, array_key_len)); 321 322 if (rank % smartsim->collocated_database_num_ranks == 0) { 323 size_t dim_2[1] = {2}; 324 PetscInt step_array[2] = {step_num, step_num}; 325 PetscSmartRedisCall(put_tensor(smartsim->client, "step", 4, step_array, dim_2, 1, SRTensorTypeInt32, SRMemLayoutContiguous)); 326 } 327 } 328 329 PetscCall(DMRestoreGlobalVector(user->sgs_dd_train->dm_dd_training, &TrainingData)); 330 PetscFunctionReturn(PETSC_SUCCESS); 331 } 332 333 PetscErrorCode TSPostStep_SGS_DD_Training(TS ts) { 334 User user; 335 const char check_run_key[] = "check-run"; 336 PetscReal check_run[2] = {1}; 337 const size_t check_run_dims[1] = {2}; 338 size_t check_run_key_size; 339 340 PetscFunctionBeginUser; 341 PetscCall(PetscStrlen(check_run_key, &check_run_key_size)); 342 PetscCall(TSGetApplicationContext(ts, &user)); 343 SmartSimData smartsim = user->smartsim; 344 345 PetscSmartRedisCall( 346 unpack_tensor(smartsim->client, check_run_key, check_run_key_size, check_run, check_run_dims, 1, SRTensorTypeDouble, SRMemLayoutContiguous)); 347 if (check_run[0] == 0) { 348 PetscCall(PetscPrintf(user->comm, "-- Simulation stopped by 'check-run' tensor in Redis database\n")); 349 PetscCall(TSSetConvergedReason(ts, TS_CONVERGED_USER)); 350 } 351 352 PetscFunctionReturn(PETSC_SUCCESS); 353 } 354 355 PetscErrorCode SGS_DD_TrainingDataDestroy(SGS_DD_TrainingData sgs_dd_train) { 356 PetscFunctionBeginUser; 357 if (!sgs_dd_train) PetscFunctionReturn(PETSC_SUCCESS); 358 359 PetscCall(OperatorApplyContextDestroy(sgs_dd_train->op_training_data_calc_ctx)); 360 PetscCall(NodalProjectionDataDestroy(sgs_dd_train->filtered_grad_velo_proj)); 361 PetscCall(DMDestroy(&sgs_dd_train->dm_dd_training)); 362 PetscCall(PetscFree(sgs_dd_train)); 363 364 PetscFunctionReturn(PETSC_SUCCESS); 365 } 366