Compadre 1.5.5
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GMLS_SmallBatchReuse_Device.cpp
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1/*
2 *
3 * This examples tests the ability to reuse a GMLS class instance,
4 * changing the target and neighbor list for the new target.
5 *
6 */
7
8#include <iostream>
9#include <string>
10#include <vector>
11#include <map>
12#include <stdlib.h>
13#include <cstdio>
14#include <random>
15
16#include <Compadre_Config.h>
17#include <Compadre_GMLS.hpp>
20
21#include "GMLS_Tutorial.hpp"
23
24#ifdef COMPADRE_USE_MPI
25#include <mpi.h>
26#endif
27
28#include <Kokkos_Timer.hpp>
29#include <Kokkos_Core.hpp>
30
31using namespace Compadre;
32
33//! [Parse Command Line Arguments]
34
35// called from command line
36int main (int argc, char* args[]) {
37
38// initializes MPI (if available) with command line arguments given
39#ifdef COMPADRE_USE_MPI
40MPI_Init(&argc, &args);
41#endif
42
43// initializes Kokkos with command line arguments given
44Kokkos::initialize(argc, args);
45
46// becomes false if the computed solution not within the failure_threshold of the actual solution
47bool all_passed = true;
48
49// code block to reduce scope for all Kokkos View allocations
50// otherwise, Views may be deallocating when we call Kokkos::finalize() later
51{
52
53 CommandLineProcessor clp(argc, args);
54 auto order = clp.order;
55 auto dimension = clp.dimension;
56 auto number_target_coords = clp.number_target_coords;
57 auto constraint_name = clp.constraint_name;
58 auto solver_name = clp.solver_name;
59 auto problem_name = clp.problem_name;
60
61 // the functions we will be seeking to reconstruct are in the span of the basis
62 // of the reconstruction space we choose for GMLS, so the error should be very small
63 const double failure_tolerance = 1e-9;
64
65 // Laplacian is a second order differential operator, which we expect to be slightly less accurate
66 const double laplacian_failure_tolerance = 1e-9;
67
68 // minimum neighbors for unisolvency is the same as the size of the polynomial basis
69 const int min_neighbors = Compadre::GMLS::getNP(order, dimension);
70
71 //! [Parse Command Line Arguments]
72 Kokkos::Timer timer;
73 //! [Setting Up The Point Cloud]
74
75 // approximate spacing of source sites
76 double h_spacing = 0.05;
77 int n_neg1_to_1 = 2*(1/h_spacing) + 1; // always odd
78
79 // number of source coordinate sites that will fill a box of [-1,1]x[-1,1]x[-1,1] with a spacing approximately h
80 const int number_source_coords = std::pow(n_neg1_to_1, dimension);
81
82 // coordinates of source sites
83 Kokkos::View<double**, Kokkos::DefaultExecutionSpace> source_coords_device("source coordinates",
84 number_source_coords, 3);
85 Kokkos::View<double**>::HostMirror source_coords = Kokkos::create_mirror_view(source_coords_device);
86
87 // coordinates of target sites
88 Kokkos::View<double**, Kokkos::DefaultExecutionSpace> target_coords_device ("target coordinates", number_target_coords, 3);
89 Kokkos::View<double**>::HostMirror target_coords = Kokkos::create_mirror_view(target_coords_device);
90
91
92 // fill source coordinates with a uniform grid
93 int source_index = 0;
94 double this_coord[3] = {0,0,0};
95 for (int i=-n_neg1_to_1/2; i<n_neg1_to_1/2+1; ++i) {
96 this_coord[0] = i*h_spacing;
97 for (int j=-n_neg1_to_1/2; j<n_neg1_to_1/2+1; ++j) {
98 this_coord[1] = j*h_spacing;
99 for (int k=-n_neg1_to_1/2; k<n_neg1_to_1/2+1; ++k) {
100 this_coord[2] = k*h_spacing;
101 if (dimension==3) {
102 source_coords(source_index,0) = this_coord[0];
103 source_coords(source_index,1) = this_coord[1];
104 source_coords(source_index,2) = this_coord[2];
105 source_index++;
106 }
107 }
108 if (dimension==2) {
109 source_coords(source_index,0) = this_coord[0];
110 source_coords(source_index,1) = this_coord[1];
111 source_coords(source_index,2) = 0;
112 source_index++;
113 }
114 }
115 if (dimension==1) {
116 source_coords(source_index,0) = this_coord[0];
117 source_coords(source_index,1) = 0;
118 source_coords(source_index,2) = 0;
119 source_index++;
120 }
121 }
122
123 // fill target coords somewhere inside of [-0.5,0.5]x[-0.5,0.5]x[-0.5,0.5]
124 for(int i=0; i<number_target_coords; i++){
125
126 // first, we get a uniformly random distributed direction
127 double rand_dir[3] = {0,0,0};
128
129 for (int j=0; j<dimension; ++j) {
130 // rand_dir[j] is in [-0.5, 0.5]
131 rand_dir[j] = ((double)rand() / (double) RAND_MAX) - 0.5;
132 }
133
134 // then we get a uniformly random radius
135 for (int j=0; j<dimension; ++j) {
136 target_coords(i,j) = rand_dir[j];
137 }
138
139 }
140
141
142 //! [Setting Up The Point Cloud]
143
144
145 //! [Creating The Data]
146
147
148 // source coordinates need copied to device before using to construct sampling data
149 Kokkos::deep_copy(source_coords_device, source_coords);
150
151 // target coordinates copied next, because it is a convenient time to send them to device
152 Kokkos::deep_copy(target_coords_device, target_coords);
153
154 // need Kokkos View storing true solution
155 Kokkos::View<double*, Kokkos::DefaultExecutionSpace> sampling_data_device("samples of true solution",
156 source_coords_device.extent(0));
157
158 Kokkos::View<double**, Kokkos::DefaultExecutionSpace> gradient_sampling_data_device("samples of true gradient",
159 source_coords_device.extent(0), dimension);
160
161 Kokkos::View<double**, Kokkos::DefaultExecutionSpace> divergence_sampling_data_device
162 ("samples of true solution for divergence test", source_coords_device.extent(0), dimension);
163
164 Kokkos::parallel_for("Sampling Manufactured Solutions", Kokkos::RangePolicy<Kokkos::DefaultExecutionSpace>
165 (0,source_coords.extent(0)), KOKKOS_LAMBDA(const int i) {
166
167 // coordinates of source site i
168 double xval = source_coords_device(i,0);
169 double yval = (dimension>1) ? source_coords_device(i,1) : 0;
170 double zval = (dimension>2) ? source_coords_device(i,2) : 0;
171
172 // data for targets with scalar input
173 sampling_data_device(i) = trueSolution(xval, yval, zval, order, dimension);
174
175 // data for targets with vector input (divergence)
176 double true_grad[3] = {0,0,0};
177 trueGradient(true_grad, xval, yval,zval, order, dimension);
178
179 for (int j=0; j<dimension; ++j) {
180 gradient_sampling_data_device(i,j) = true_grad[j];
181
182 // data for target with vector input (curl)
183 divergence_sampling_data_device(i,j) = divergenceTestSamples(xval, yval, zval, j, dimension);
184 }
185
186 });
187
188
189 //! [Creating The Data]
190
191
192 //! [Setting Up The GMLS Object]
193
194 // initialize an instance of the GMLS class
196 order, dimension,
197 solver_name.c_str(), problem_name.c_str(), constraint_name.c_str(),
198 2 /*manifold order*/);
199
200 // create a vector of target operations
201 std::vector<TargetOperation> lro(5);
202 lro[0] = ScalarPointEvaluation;
207
208 // and then pass them to the GMLS class
209 my_GMLS.addTargets(lro);
210
211 // sets the weighting kernel function from WeightingFunctionType
212 my_GMLS.setWeightingType(WeightingFunctionType::Power);
213
214 // power to use in that weighting kernel function
215 my_GMLS.setWeightingParameter(2);
216
217 // set source sites once for all targets
218 my_GMLS.setSourceSites(source_coords_device);
219
220
221 // Point cloud construction for neighbor search
222 // CreatePointCloudSearch constructs an object of type PointCloudSearch, but deduces the templates for you
223 auto point_cloud_search(CreatePointCloudSearch(source_coords, dimension));
224
225 // loop through the target sites
226 for (int i=0; i<number_target_coords; i++) {
227 timer.reset();
228 // pass in neighbor lists, source coordinates, target coordinates, and window sizes
229 //
230 // single neighbor lists have the format:
231 // dimensions: (1 single neighbor list for one target) X (# maximum number of neighbors for any given target + 1)
232 // the first column contains the number of neighbors for that rows corresponding target index
233 //
234 // source coordinates have the format:
235 // dimensions: (# number of source sites) X (dimension)
236 // entries in the neighbor lists (integers) correspond to rows of this 2D array
237 //
238 // single target coordinates have the format:
239 // dimensions: (1 single target site) X (dimension)
240 // # of target sites is same as # of rows of neighbor lists
241 //
242
243
244 // coordinates of single target sites
245 Kokkos::View<double**, Kokkos::DefaultExecutionSpace> single_target_coords_device ("single target coordinates", 1, 3);
246 Kokkos::View<double**>::HostMirror single_target_coords = Kokkos::create_mirror_view(single_target_coords_device);
247 for (int j=0; j<3; ++j) {
248 single_target_coords(0,j) = target_coords(i,j);
249 }
250 // target coordinates copied next, because it is a convenient time to send them to device
251 Kokkos::deep_copy(single_target_coords_device, single_target_coords);
252 Kokkos::fence();
253
254 //! [Performing Neighbor Search]
255
256 // each row is a neighbor list for a target site, with the first column of each row containing
257 // the number of neighbors for that rows corresponding target site
258 double epsilon_multiplier = 1.5;
259 int estimated_upper_bound_number_neighbors =
260 point_cloud_search.getEstimatedNumberNeighborsUpperBound(min_neighbors, dimension, epsilon_multiplier);
261
262 Kokkos::View<int**, Kokkos::DefaultExecutionSpace> single_neighbor_lists_device("neighbor lists",
263 1, estimated_upper_bound_number_neighbors); // first column is # of neighbors
264 Kokkos::View<int**>::HostMirror single_neighbor_lists = Kokkos::create_mirror_view(single_neighbor_lists_device);
265
266 // each target site has a window size
267 Kokkos::View<double*, Kokkos::DefaultExecutionSpace> single_epsilon_device("h supports", 1);
268 Kokkos::View<double*>::HostMirror single_epsilon = Kokkos::create_mirror_view(single_epsilon_device);
269
270 // query the point cloud to generate the neighbor lists using a kdtree to produce the n nearest neighbor
271 // to each target site, adding (epsilon_multiplier-1)*100% to whatever the distance away the further neighbor used is from
272 // each target to the view for epsilon
273 point_cloud_search.generate2DNeighborListsFromKNNSearch(false /*not dry run*/, single_target_coords,
274 single_neighbor_lists, single_epsilon, min_neighbors, epsilon_multiplier);
275
276 //! [Performing Neighbor Search]
277
278
279 // Copy data back to device (they were filled on the host)
280 // We could have filled Kokkos Views with memory space on the host
281 // and used these instead, and then the copying of data to the device
282 // would be performed in the GMLS class
283 Kokkos::deep_copy(single_neighbor_lists_device, single_neighbor_lists);
284 Kokkos::deep_copy(single_epsilon_device, single_epsilon);
285 Kokkos::fence(); // let call to build neighbor lists complete before copying back to device
286
287 // Create temporary small views to hold just one target's information at a time
288 my_GMLS.setNeighborLists(single_neighbor_lists_device);
289 my_GMLS.setTargetSites(single_target_coords_device);
290 my_GMLS.setWindowSizes(single_epsilon_device);
291
292
293 // generate the alphas that to be combined with data for each target operation requested in lro
294 my_GMLS.generateAlphas(1, true /* keep polynomial coefficients, only needed for a test later in this program */);
295
296
297 //! [Setting Up The GMLS Object]
298
299 double instantiation_time = timer.seconds();
300 std::cout << "Took " << instantiation_time << "s to complete alphas generation." << std::endl;
301 Kokkos::fence(); // let generateAlphas finish up before using alphas
302
303
304 //! [Apply GMLS Alphas To Data]
305
306 // it is important to note that if you expect to use the data as a 1D view, then you should use double*
307 // however, if you know that the target operation will result in a 2D view (vector or matrix output),
308 // then you should template with double** as this is something that can not be infered from the input data
309 // or the target operator at compile time. Additionally, a template argument is required indicating either
310 // Kokkos::HostSpace or Kokkos::DefaultExecutionSpace::memory_space()
311
312 // The Evaluator class takes care of handling input data views as well as the output data views.
313 // It uses information from the GMLS class to determine how many components are in the input
314 // as well as output for any choice of target functionals and then performs the contactions
315 // on the data using the alpha coefficients generated by the GMLS class, all on the device.
316 Evaluator gmls_evaluator(&my_GMLS);
317
318 auto output_value = gmls_evaluator.applyAlphasToDataAllComponentsAllTargetSites<double*, Kokkos::HostSpace>
319 (sampling_data_device, ScalarPointEvaluation);
320
321 auto output_laplacian = gmls_evaluator.applyAlphasToDataAllComponentsAllTargetSites<double*, Kokkos::HostSpace>
322 (sampling_data_device, LaplacianOfScalarPointEvaluation);
323
324 auto output_gradient = gmls_evaluator.applyAlphasToDataAllComponentsAllTargetSites<double**, Kokkos::HostSpace>
325 (sampling_data_device, GradientOfScalarPointEvaluation);
326
327 auto output_divergence = gmls_evaluator.applyAlphasToDataAllComponentsAllTargetSites<double*, Kokkos::HostSpace>
328 (gradient_sampling_data_device, DivergenceOfVectorPointEvaluation, VectorPointSample);
329
330 auto output_curl = gmls_evaluator.applyAlphasToDataAllComponentsAllTargetSites<double**, Kokkos::HostSpace>
331 (divergence_sampling_data_device, CurlOfVectorPointEvaluation);
332
333 // retrieves polynomial coefficients instead of remapped field
334 auto scalar_coefficients = gmls_evaluator.applyFullPolynomialCoefficientsBasisToDataAllComponents<double**, Kokkos::HostSpace>
335 (sampling_data_device);
336
337 //! [Apply GMLS Alphas To Data]
338
339 Kokkos::fence(); // let application of alphas to data finish before using results
340 // times the Comparison in Kokkos
341
342 //! [Check That Solutions Are Correct]
343
344 // load value from output
345 double GMLS_value = output_value(0);
346
347 // load laplacian from output
348 double GMLS_Laplacian = output_laplacian(0);
349
350 // load partial x from gradient
351 // this is a test that the scalar_coefficients 2d array returned hold valid entries
352 // scalar_coefficients(i,1)*1./epsilon(i) is equivalent to the target operation acting
353 // on the polynomials applied to the polynomial coefficients
354 double GMLS_GradX = scalar_coefficients(0,1)*1./single_epsilon(0);
355 //output_gradient(i,0);
356
357 // load partial y from gradient
358 double GMLS_GradY = (dimension>1) ? output_gradient(0,1) : 0;
359
360 // load partial z from gradient
361 double GMLS_GradZ = (dimension>2) ? output_gradient(0,2) : 0;
362
363 // load divergence from output
364 double GMLS_Divergence = output_divergence(0);
365
366 // load curl from output
367 double GMLS_CurlX = (dimension>1) ? output_curl(0,0) : 0;
368 double GMLS_CurlY = (dimension>1) ? output_curl(0,1) : 0;
369 double GMLS_CurlZ = (dimension>2) ? output_curl(0,2) : 0;
370
371
372 // target site i's coordinate
373 double xval = target_coords(i,0);
374 double yval = (dimension>1) ? target_coords(i,1) : 0;
375 double zval = (dimension>2) ? target_coords(i,2) : 0;
376
377 // evaluation of various exact solutions
378 double actual_value = trueSolution(xval, yval, zval, order, dimension);
379 double actual_Laplacian = trueLaplacian(xval, yval, zval, order, dimension);
380
381 double actual_Gradient[3] = {0,0,0}; // initialized for 3, but only filled up to dimension
382 trueGradient(actual_Gradient, xval, yval, zval, order, dimension);
383
384 double actual_Divergence;
385 actual_Divergence = trueLaplacian(xval, yval, zval, order, dimension);
386
387 double actual_Curl[3] = {0,0,0}; // initialized for 3, but only filled up to dimension
388 // (and not at all for dimimension = 1)
389 if (dimension>1) {
390 actual_Curl[0] = curlTestSolution(xval, yval, zval, 0, dimension);
391 actual_Curl[1] = curlTestSolution(xval, yval, zval, 1, dimension);
392 if (dimension>2) {
393 actual_Curl[2] = curlTestSolution(xval, yval, zval, 2, dimension);
394 }
395 }
396
397 // check actual function value
398 if(GMLS_value!=GMLS_value || std::abs(actual_value - GMLS_value) > failure_tolerance) {
399 all_passed = false;
400 std::cout << i << " Failed Actual by: " << std::abs(actual_value - GMLS_value) << std::endl;
401 }
402
403 // check Laplacian
404 if(std::abs(actual_Laplacian - GMLS_Laplacian) > laplacian_failure_tolerance) {
405 all_passed = false;
406 std::cout << i <<" Failed Laplacian by: " << std::abs(actual_Laplacian - GMLS_Laplacian) << std::endl;
407 }
408
409 // check gradient
410 if(std::abs(actual_Gradient[0] - GMLS_GradX) > failure_tolerance) {
411 all_passed = false;
412 std::cout << i << " Failed GradX by: " << std::abs(actual_Gradient[0] - GMLS_GradX) << std::endl;
413 if (dimension>1) {
414 if(std::abs(actual_Gradient[1] - GMLS_GradY) > failure_tolerance) {
415 all_passed = false;
416 std::cout << i << " Failed GradY by: " << std::abs(actual_Gradient[1] - GMLS_GradY) << std::endl;
417 }
418 }
419 if (dimension>2) {
420 if(std::abs(actual_Gradient[2] - GMLS_GradZ) > failure_tolerance) {
421 all_passed = false;
422 std::cout << i << " Failed GradZ by: " << std::abs(actual_Gradient[2] - GMLS_GradZ) << std::endl;
423 }
424 }
425 }
426
427 // check divergence
428 if(std::abs(actual_Divergence - GMLS_Divergence) > failure_tolerance) {
429 all_passed = false;
430 std::cout << i << " Failed Divergence by: " << std::abs(actual_Divergence - GMLS_Divergence) << std::endl;
431 }
432
433 // check curl
434 if (order > 2) { // reconstructed solution not in basis unless order greater than 2 used
435 double tmp_diff = 0;
436 if (dimension>1)
437 tmp_diff += std::abs(actual_Curl[0] - GMLS_CurlX) + std::abs(actual_Curl[1] - GMLS_CurlY);
438 if (dimension>2)
439 tmp_diff += std::abs(actual_Curl[2] - GMLS_CurlZ);
440 if(std::abs(tmp_diff) > failure_tolerance) {
441 all_passed = false;
442 std::cout << i << " Failed Curl by: " << std::abs(tmp_diff) << std::endl;
443 }
444 }
445 }
446
447
448 //! [Check That Solutions Are Correct]
449 // stop timing comparison loop
450 //! [Finalize Program]
451
452
453} // end of code block to reduce scope, causing Kokkos View de-allocations
454// otherwise, Views may be deallocating when we call Kokkos::finalize() later
455
456// finalize Kokkos and MPI (if available)
457Kokkos::finalize();
458#ifdef COMPADRE_USE_MPI
459MPI_Finalize();
460#endif
461
462// output to user that test passed or failed
463if(all_passed) {
464 fprintf(stdout, "Passed test \n");
465 return 0;
466} else {
467 fprintf(stdout, "Failed test \n");
468 return -1;
469}
470
471} // main
472
473
474//! [Finalize Program]
int main(int argc, char *args[])
[Parse Command Line Arguments]
KOKKOS_INLINE_FUNCTION double trueSolution(double x, double y, double z, int order, int dimension)
KOKKOS_INLINE_FUNCTION void trueGradient(double *ans, double x, double y, double z, int order, int dimension)
KOKKOS_INLINE_FUNCTION double divergenceTestSamples(double x, double y, double z, int component, int dimension)
KOKKOS_INLINE_FUNCTION double curlTestSolution(double x, double y, double z, int component, int dimension)
KOKKOS_INLINE_FUNCTION double trueLaplacian(double x, double y, double z, int order, int dimension)
Lightweight Evaluator Helper This class is a lightweight wrapper for extracting and applying all rele...
Kokkos::View< output_data_type, output_array_layout, output_memory_space > applyAlphasToDataAllComponentsAllTargetSites(view_type_input_data sampling_data, TargetOperation lro, const SamplingFunctional sro_in=PointSample, bool scalar_as_vector_if_needed=true, const int evaluation_site_local_index=0) const
Transformation of data under GMLS (allocates memory for output)
Kokkos::View< output_data_type, output_array_layout, output_memory_space > applyFullPolynomialCoefficientsBasisToDataAllComponents(view_type_input_data sampling_data, bool scalar_as_vector_if_needed=true) const
Generation of polynomial reconstruction coefficients by applying to data in GMLS (allocates memory fo...
Generalized Moving Least Squares (GMLS)
static KOKKOS_INLINE_FUNCTION int getNP(const int m, const int dimension=3, const ReconstructionSpace r_space=ReconstructionSpace::ScalarTaylorPolynomial)
Returns size of the basis for a given polynomial order and dimension General to dimension 1....
PointCloudSearch< view_type > CreatePointCloudSearch(view_type src_view, const local_index_type dimensions=-1, const local_index_type max_leaf=-1)
CreatePointCloudSearch allows for the construction of an object of type PointCloudSearch with templat...
@ LaplacianOfScalarPointEvaluation
Point evaluation of the laplacian of a scalar (could be on a manifold or not)
@ GradientOfScalarPointEvaluation
Point evaluation of the gradient of a scalar.
@ CurlOfVectorPointEvaluation
Point evaluation of the curl of a vector (results in a vector)
@ DivergenceOfVectorPointEvaluation
Point evaluation of the divergence of a vector (results in a scalar)
@ ScalarPointEvaluation
Point evaluation of a scalar.
constexpr SamplingFunctional VectorPointSample
Point evaluations of the entire vector source function.
@ VectorOfScalarClonesTaylorPolynomial
Scalar basis reused as many times as there are components in the vector resulting in a much cheaper p...