// This is the "inner loop" of the neural simulation.
// Every spike event could cause upto 256 different weights to
// be put into the ring buffer.
static inline void process_fixed_synapses (address_t fixed)
{
register uint32_t *synaptic_words = fixed_weight_controls(fixed);
register uint32_t fixed_synapse = num_fixed_synapses(fixed);
register uint32_t synaptic_word, delay;
register uint32_t weight;
register uint32_t offset, index;
register ring_entry_t *rp = ring_buffer;
register uint32_t t = time;
for ( ; fixed_synapse > 0; fixed_synapse--)
{
// Get the next 32 bit word from the synaptic_row (should autoincrement pointer in single instruction)
synaptic_word = *synaptic_words++;
// Extract components from this word
delay = sparse_delay(synaptic_word);
index = sparse_type_index(synaptic_word);
weight = sparse_weight(synaptic_word);
// Convert into ring buffer offset
offset = offset_sparse(delay + t, index);
// Add weight to ring-buffer entry
// **NOTE** Dave suspects that this could be a potential location for overflow
rp[offset] += weight; // Add the weight to the current ring_buffer value.
#define TEACHING_SYNAPSE 1 //settle for inhibitory at the moment
if (sparse_type(synaptic_word) == TEACHING_SYNAPSE) {
plasticity_process_teaching_synaptic_event(sparse_index(synaptic_word));
}
#ifdef DEBUG
io_printf (IO_BUF, "pA) d: %2u, %c, n = %3u)] - {%08x %08x}\n",
sparse_delay(synaptic_word),
(sparse_type(synaptic_word)==0)? 'X': 'I',
sparse_index(synaptic_word),
SYNAPSE_DELAY_MASK,
SYNAPSE_TYPE_INDEX_BITS
);
#endif
}
}
Stokhos::SparseGridQuadrature<ordinal_type, value_type>::
SparseGridQuadrature(
const Teuchos::RCP<const ProductBasis<ordinal_type,value_type> >& product_basis,
ordinal_type sparse_grid_level,
value_type duplicate_tol,
ordinal_type growth_rate) :
coordinate_bases(product_basis->getCoordinateBases())
{
#ifdef STOKHOS_TEUCHOS_TIME_MONITOR
TEUCHOS_FUNC_TIME_MONITOR("Stokhos: Sparse Grid Generation");
#endif
ordinal_type d = product_basis->dimension();
ordinal_type p = product_basis->order();
ordinal_type sz = product_basis->size();
ordinal_type level = sparse_grid_level;
// Make level = order the default, which is correct for Gaussian abscissas
// slow, linear growth, and total-order basis
if (level == 0) {
level = p;
}
//std::cout << "Sparse grid level = " << level << std::endl;
// Compute quad points, weights, values
Teuchos::Array<typename OneDOrthogPolyBasis<ordinal_type,value_type>::LevelToOrderFnPtr> growth_rules(d);
Teuchos::Array< void (*) ( int order, int dim, double x[] ) > compute1DPoints(d);
Teuchos::Array< void (*) ( int order, int dim, double w[] ) > compute1DWeights(d);
for (ordinal_type i=0; i<d; i++) {
compute1DPoints[i] = &(getMyPoints);
compute1DWeights[i] = &(getMyWeights);
growth_rules[i] = coordinate_bases[i]->getSparseGridGrowthRule();
}
// Set the static sparse grid quadrature pointer to this
// (this will cause a conflict if another sparse grid quadrature object
// is trying to access the VPISparseGrid library, but that's all we can
// do at this point).
sgq = this;
int num_total_pts =
webbur::sgmg_size_total(d, level, growth_rate, &growth_rules[0]);
ordinal_type ntot =
webbur::sgmg_size(d, level, &compute1DPoints[0], duplicate_tol,
growth_rate, &growth_rules[0]);
Teuchos::Array<int> sparse_order(ntot*d);
Teuchos::Array<int> sparse_index(ntot*d);
Teuchos::Array<int> sparse_unique_index(num_total_pts);
quad_points.resize(ntot);
quad_weights.resize(ntot);
quad_values.resize(ntot);
Teuchos::Array<value_type> gp(ntot*d);
webbur::sgmg_unique_index(d, level, &compute1DPoints[0],
duplicate_tol, ntot, num_total_pts,
growth_rate, &growth_rules[0],
&sparse_unique_index[0]);
webbur::sgmg_index(d, level, ntot, num_total_pts,
&sparse_unique_index[0], growth_rate,
&growth_rules[0],
&sparse_order[0], &sparse_index[0]);
webbur::sgmg_weight(d, level, &compute1DWeights[0],
ntot, num_total_pts,
&sparse_unique_index[0], growth_rate,
&growth_rules[0],
&quad_weights[0]);
webbur::sgmg_point(d, level, &compute1DPoints[0],
ntot, &sparse_order[0],
&sparse_index[0], growth_rate,
&growth_rules[0],
&gp[0]);
for (ordinal_type i=0; i<ntot; i++) {
quad_values[i].resize(sz);
quad_points[i].resize(d);
for (ordinal_type j=0; j<d; j++)
quad_points[i][j] = gp[i*d+j];
product_basis->evaluateBases(quad_points[i], quad_values[i]);
}
//std::cout << "Number of quadrature points = " << ntot << std::endl;
}
Stokhos::AnisoSparseGridQuadrature<ordinal_type, value_type>::
AnisoSparseGridQuadrature(
const Teuchos::RCP<const ProductBasis<ordinal_type,value_type> >& product_basis,
ordinal_type sparse_grid_level, value_type dim_weights[]) :
coordinate_bases(product_basis->getCoordinateBases())
{
TEUCHOS_FUNC_TIME_MONITOR("Stokhos::AnisoSparseGridQuadrature -- Quad Grid Generation");
ordinal_type d = product_basis->dimension();
ordinal_type p = product_basis->order();
ordinal_type sz = product_basis->size();
ordinal_type level = sparse_grid_level;
// Mike's heuristic formula for computing the level
if (level == 0) {
level = static_cast<ordinal_type>(std::ceil(0.5*(p+d-1)));
if (level < d)
level = p;
}
std::cout << "Sparse grid level = " << level << std::endl;
// Compute quad points, weights, values
Teuchos::Array<int> rules(d), growthRules(d);
Teuchos::Array< void (*) ( int order, int dim, double x[] ) > compute1DPoints(d);
Teuchos::Array< void (*) ( int order, int dim, double w[] ) > compute1DWeights(d);
for (ordinal_type i=0; i<d; i++) {
compute1DPoints[i] = &(getMyPoints);
compute1DWeights[i] = &(getMyWeights);
rules[i] = coordinate_bases[i]->getSparseGridRule();
growthRules[i] = coordinate_bases[i]->getSparseGridGrowthRule();
}
// Set the static sparse grid quadrature pointer to this
// (this will cause a conflict if another sparse grid quadrature object
// is trying to access the VPISparseGrid library, but that's all we can
// do at this point).
sgq = this;
int num_total_pts =
webbur::sandia_sgmga_size_total(d,&dim_weights[0],level,&rules[0],
&growthRules[0]);
ordinal_type ntot =
webbur::sandia_sgmga_size(d,&dim_weights[0],level,&rules[0],
&compute1DPoints[0], 1e-15,&growthRules[0]);
Teuchos::Array<int> sparse_order(ntot*d);
Teuchos::Array<int> sparse_index(ntot*d);
Teuchos::Array<int> sparse_unique_index(num_total_pts);
quad_points.resize(ntot);
quad_weights.resize(ntot);
quad_values.resize(ntot);
Teuchos::Array<value_type> gp(ntot*d);
webbur::sandia_sgmga_unique_index(d, &dim_weights[0], level, &rules[0],
&compute1DPoints[0],
1e-15, ntot, num_total_pts,
&growthRules[0],
&sparse_unique_index[0] );
webbur::sandia_sgmga_index(d, &dim_weights[0], level,
&rules[0], ntot, num_total_pts,
&sparse_unique_index[0],
&growthRules[0],
&sparse_order[0], &sparse_index[0]);
webbur::sandia_sgmga_weight(d,&dim_weights[0],level,
&rules[0], &compute1DWeights[0],
ntot, num_total_pts, &sparse_unique_index[0],
&growthRules[0],
&quad_weights[0]);
webbur::sandia_sgmga_point(d, &dim_weights[0], level,
&rules[0], &compute1DPoints[0],
ntot, &sparse_order[0], &sparse_index[0],
&growthRules[0],
&gp[0]);
for (ordinal_type i=0; i<ntot; i++) {
quad_values[i].resize(sz);
quad_points[i].resize(d);
for (ordinal_type j=0; j<d; j++)
quad_points[i][j] = gp[i*d+j];
product_basis->evaluateBases(quad_points[i], quad_values[i]);
}
std::cout << "Number of quadrature points = " << ntot << std::endl;
}
