// license:GPL-2.0+ // copyright-holders:Couriersud /* * nld_ms_direct.h * */ #ifndef NLD_MS_SM_H_ #define NLD_MS_SM_H_ #include #include "solver/nld_solver.h" #include "solver/vector_base.h" NETLIB_NAMESPACE_DEVICES_START() //#define nl_ext_double __float128 // slow, very slow //#define nl_ext_double long double // slightly slower #define nl_ext_double nl_double template class matrix_solver_sm_t: public matrix_solver_t { public: matrix_solver_sm_t(const solver_parameters_t *params, const int size); matrix_solver_sm_t(const eSolverType type, const solver_parameters_t *params, const int size); virtual ~matrix_solver_sm_t(); virtual void vsetup(analog_net_t::list_t &nets) override; virtual void reset() override { matrix_solver_t::reset(); } protected: virtual void add_term(int net_idx, terminal_t *term) override; virtual int vsolve_non_dynamic(const bool newton_raphson) override; int solve_non_dynamic(const bool newton_raphson); inline const unsigned N() const { if (m_N == 0) return m_dim; else return m_N; } void build_LE_A(); void build_LE_RHS(); void LE_invert(); template void LE_compute_x(T * RESTRICT x); template T delta(const T * RESTRICT V); template void store(const T * RESTRICT V); virtual netlist_time compute_next_timestep() override; template inline nl_ext_double &A(const T1 &r, const T2 &c) { return m_A[r][c]; } template inline nl_ext_double &W(const T1 &r, const T2 &c) { return m_W[r][c]; } template inline nl_ext_double &Ainv(const T1 &r, const T2 &c) { return m_Ainv[r][c]; } template inline nl_ext_double &RHS(const T1 &r) { return m_RHS[r]; } template inline nl_ext_double &lA(const T1 &r, const T2 &c) { return m_lA[r][c]; } template inline nl_ext_double &lAinv(const T1 &r, const T2 &c) { return m_lAinv[r][c]; } ATTR_ALIGN nl_double m_last_RHS[_storage_N]; // right hand side - contains currents ATTR_ALIGN nl_double m_last_V[_storage_N]; terms_t * m_terms[_storage_N]; terms_t *m_rails_temp; private: static const std::size_t m_pitch = ((( _storage_N) + 7) / 8) * 8; ATTR_ALIGN nl_ext_double m_A[_storage_N][m_pitch]; ATTR_ALIGN nl_ext_double m_Ainv[_storage_N][m_pitch]; ATTR_ALIGN nl_ext_double m_W[_storage_N][m_pitch]; ATTR_ALIGN nl_ext_double m_RHS[_storage_N]; // right hand side - contains currents ATTR_ALIGN nl_ext_double m_lA[_storage_N][m_pitch]; ATTR_ALIGN nl_ext_double m_lAinv[_storage_N][m_pitch]; //ATTR_ALIGN nl_ext_double m_RHSx[_storage_N]; const unsigned m_dim; }; // ---------------------------------------------------------------------------------------- // matrix_solver_direct // ---------------------------------------------------------------------------------------- template matrix_solver_sm_t::~matrix_solver_sm_t() { for (unsigned k = 0; k < N(); k++) { pfree(m_terms[k]); } pfree_array(m_rails_temp); #if (NL_USE_DYNAMIC_ALLOCATION) pfree_array(m_A); #endif } template netlist_time matrix_solver_sm_t::compute_next_timestep() { nl_double new_solver_timestep = m_params.m_max_timestep; if (m_params.m_dynamic) { /* * FIXME: We should extend the logic to use either all nets or * only output nets. */ for (unsigned k = 0, iN=N(); k < iN; k++) { analog_net_t *n = m_nets[k]; const nl_double DD_n = (n->Q_Analog() - m_last_V[k]); const nl_double hn = current_timestep(); nl_double DD2 = (DD_n / hn - n->m_DD_n_m_1 / n->m_h_n_m_1) / (hn + n->m_h_n_m_1); nl_double new_net_timestep; n->m_h_n_m_1 = hn; n->m_DD_n_m_1 = DD_n; if (nl_math::abs(DD2) > NL_FCONST(1e-30)) // avoid div-by-zero new_net_timestep = nl_math::sqrt(m_params.m_lte / nl_math::abs(NL_FCONST(0.5)*DD2)); else new_net_timestep = m_params.m_max_timestep; if (new_net_timestep < new_solver_timestep) new_solver_timestep = new_net_timestep; m_last_V[k] = n->Q_Analog(); } if (new_solver_timestep < m_params.m_min_timestep) new_solver_timestep = m_params.m_min_timestep; } //if (new_solver_timestep > 10.0 * hn) // new_solver_timestep = 10.0 * hn; return netlist_time::from_double(new_solver_timestep); } template ATTR_COLD void matrix_solver_sm_t::add_term(int k, terminal_t *term) { if (term->m_otherterm->net().isRailNet()) { m_rails_temp[k].add(term, -1, false); } else { int ot = get_net_idx(&term->m_otherterm->net()); if (ot>=0) { m_terms[k]->add(term, ot, true); } /* Should this be allowed ? */ else // if (ot<0) { m_rails_temp[k].add(term, ot, true); log().fatal("found term with missing othernet {1}\n", term->name()); } } } template ATTR_COLD void matrix_solver_sm_t::vsetup(analog_net_t::list_t &nets) { if (m_dim < nets.size()) log().fatal("Dimension {1} less than {2}", m_dim, nets.size()); for (unsigned k = 0; k < N(); k++) { m_terms[k]->clear(); m_rails_temp[k].clear(); } matrix_solver_t::setup_base(nets); for (unsigned k = 0; k < N(); k++) { m_terms[k]->m_railstart = m_terms[k]->count(); for (unsigned i = 0; i < m_rails_temp[k].count(); i++) this->m_terms[k]->add(m_rails_temp[k].terms()[i], m_rails_temp[k].net_other()[i], false); m_rails_temp[k].clear(); // no longer needed m_terms[k]->set_pointers(); } #if 1 /* Sort in descending order by number of connected matrix voltages. * The idea is, that for Gauss-Seidel algo the first voltage computed * depends on the greatest number of previous voltages thus taking into * account the maximum amout of information. * * This actually improves performance on popeye slightly. Average * GS computations reduce from 2.509 to 2.370 * * Smallest to largest : 2.613 * Unsorted : 2.509 * Largest to smallest : 2.370 * * Sorting as a general matrix pre-conditioning is mentioned in * literature but I have found no articles about Gauss Seidel. * * For Gaussian Elimination however increasing order is better suited. * FIXME: Even better would be to sort on elements right of the matrix diagonal. * */ int sort_order = (type() == GAUSS_SEIDEL ? 1 : -1); for (unsigned k = 0; k < N() / 2; k++) for (unsigned i = 0; i < N() - 1; i++) { if ((m_terms[i]->m_railstart - m_terms[i+1]->m_railstart) * sort_order < 0) { std::swap(m_terms[i], m_terms[i+1]); std::swap(m_nets[i], m_nets[i+1]); } } for (unsigned k = 0; k < N(); k++) { int *other = m_terms[k]->net_other(); for (unsigned i = 0; i < m_terms[k]->count(); i++) if (other[i] != -1) other[i] = get_net_idx(&m_terms[k]->terms()[i]->m_otherterm->net()); } #endif /* create a list of non zero elements right of the diagonal * These list anticipate the population of array elements by * Gaussian elimination. */ for (unsigned k = 0; k < N(); k++) { terms_t * t = m_terms[k]; /* pretty brutal */ int *other = t->net_other(); t->m_nz.clear(); if (k==0) t->m_nzrd.clear(); else { t->m_nzrd = m_terms[k-1]->m_nzrd; unsigned j=0; while(j < t->m_nzrd.size()) { if (t->m_nzrd[j] < k + 1) t->m_nzrd.remove_at(j); else j++; } } for (unsigned i = 0; i < t->m_railstart; i++) { if (!t->m_nzrd.contains(other[i]) && other[i] >= (int) (k + 1)) t->m_nzrd.push_back(other[i]); if (!t->m_nz.contains(other[i])) t->m_nz.push_back(other[i]); } /* and sort */ psort_list(t->m_nzrd); t->m_nz.push_back(k); // add diagonal psort_list(t->m_nz); } /* create a list of non zero elements below diagonal k * This should reduce cache misses ... */ bool touched[_storage_N][_storage_N] = { { false } }; for (unsigned k = 0; k < N(); k++) { m_terms[k]->m_nzbd.clear(); for (unsigned j = 0; j < m_terms[k]->m_nz.size(); j++) touched[k][m_terms[k]->m_nz[j]] = true; } for (unsigned k = 0; k < N(); k++) { for (unsigned row = k + 1; row < N(); row++) { if (touched[row][k]) { if (!m_terms[k]->m_nzbd.contains(row)) m_terms[k]->m_nzbd.push_back(row); for (unsigned col = k; col < N(); col++) if (touched[k][col]) touched[row][col] = true; } } } if (0) for (unsigned k = 0; k < N(); k++) { pstring line = pfmt("{1}")(k, "3"); for (unsigned j = 0; j < m_terms[k]->m_nzrd.size(); j++) line += pfmt(" {1}")(m_terms[k]->m_nzrd[j], "3"); log().verbose("{1}", line); } /* * save states */ save(NLNAME(m_last_RHS)); save(NLNAME(m_last_V)); for (unsigned k = 0; k < N(); k++) { pstring num = pfmt("{1}")(k); save(RHS(k), "RHS" + num); save(m_terms[k]->go(),"GO" + num, m_terms[k]->count()); save(m_terms[k]->gt(),"GT" + num, m_terms[k]->count()); save(m_terms[k]->Idr(),"IDR" + num , m_terms[k]->count()); } } template void matrix_solver_sm_t::build_LE_A() { const unsigned iN = N(); for (unsigned k = 0; k < iN; k++) { for (unsigned i=0; i < iN; i++) A(k,i) = 0.0; const unsigned terms_count = m_terms[k]->count(); const unsigned railstart = m_terms[k]->m_railstart; const nl_double * RESTRICT gt = m_terms[k]->gt(); { nl_double akk = 0.0; for (unsigned i = 0; i < terms_count; i++) akk += gt[i]; A(k,k) = akk; } const nl_double * RESTRICT go = m_terms[k]->go(); const int * RESTRICT net_other = m_terms[k]->net_other(); for (unsigned i = 0; i < railstart; i++) A(k,net_other[i]) -= go[i]; } } template void matrix_solver_sm_t::build_LE_RHS() { const unsigned iN = N(); for (unsigned k = 0; k < iN; k++) { nl_double rhsk_a = 0.0; nl_double rhsk_b = 0.0; const int terms_count = m_terms[k]->count(); const nl_double * RESTRICT go = m_terms[k]->go(); const nl_double * RESTRICT Idr = m_terms[k]->Idr(); const nl_double * const * RESTRICT other_cur_analog = m_terms[k]->other_curanalog(); for (int i = 0; i < terms_count; i++) rhsk_a = rhsk_a + Idr[i]; for (int i = m_terms[k]->m_railstart; i < terms_count; i++) //rhsk = rhsk + go[i] * terms[i]->m_otherterm->net().as_analog().Q_Analog(); rhsk_b = rhsk_b + go[i] * *other_cur_analog[i]; RHS(k) = rhsk_a + rhsk_b; } } template void matrix_solver_sm_t::LE_invert() { const unsigned kN = N(); for (unsigned i = 0; i < kN; i++) { for (unsigned j = 0; j < kN; j++) { W(i,j) = lA(i,j) = A(i,j); Ainv(i,j) = 0.0; } Ainv(i,i) = 1.0; } /* down */ for (unsigned i = 0; i < kN; i++) { /* FIXME: Singular matrix? */ const nl_double f = 1.0 / W(i,i); const unsigned * RESTRICT const p = m_terms[i]->m_nzrd.data(); const unsigned e = m_terms[i]->m_nzrd.size(); /* Eliminate column i from row j */ const unsigned * RESTRICT const pb = m_terms[i]->m_nzbd.data(); const unsigned eb = m_terms[i]->m_nzbd.size(); for (unsigned jb = 0; jb < eb; jb++) { const unsigned j = pb[jb]; const nl_double f1 = - W(j,i) * f; if (f1 != 0.0) { for (unsigned k = 0; k < e; k++) W(j,p[k]) += W(i,p[k]) * f1; for (unsigned k = 0; k <= i; k ++) Ainv(j,k) += Ainv(i,k) * f1; } } } /* up */ for (int i = kN - 1; i >= 0; i--) { /* FIXME: Singular matrix? */ const nl_double f = 1.0 / W(i,i); for (int j = i - 1; j>=0; j--) { const nl_double f1 = - W(j,i) * f; if (f1 != 0.0) { for (unsigned k = i; k < kN; k++) W(j,k) += W(i,k) * f1; for (unsigned k = 0; k < kN; k++) Ainv(j,k) += Ainv(i,k) * f1; } } for (unsigned k = 0; k < kN; k++) { Ainv(i,k) *= f; lAinv(i,k) = Ainv(i,k); } } } template template void matrix_solver_sm_t::LE_compute_x( T * RESTRICT x) { const unsigned kN = N(); for (int i=0; i template T matrix_solver_sm_t::delta( const T * RESTRICT V) { /* FIXME: Ideally we should also include currents (RHS) here. This would * need a revaluation of the right hand side after voltages have been updated * and thus belong into a different calculation. This applies to all solvers. */ const unsigned iN = this->N(); T cerr = 0; for (unsigned i = 0; i < iN; i++) cerr = std::fmax(cerr, nl_math::abs(V[i] - (T) this->m_nets[i]->m_cur_Analog)); return cerr; } template template void matrix_solver_sm_t::store( const T * RESTRICT V) { for (unsigned i = 0, iN=N(); i < iN; i++) { this->m_nets[i]->m_cur_Analog = V[i]; } } template int matrix_solver_sm_t::solve_non_dynamic(ATTR_UNUSED const bool newton_raphson) { static const bool incremental = true; static uint cnt = 0; nl_double new_V[_storage_N]; // = { 0.0 }; if (0 || ((cnt % 200) == 0)) { /* complete calculation */ this->LE_invert(); } else { const auto iN = N(); if (not incremental) { for (int row = 0; row < iN; row ++) for (int k = 0; k < iN; k++) Ainv(row,k) = lAinv(row, k); } for (int row = 0; row < iN; row ++) { nl_double v[m_pitch] = {0}; unsigned cols[m_pitch]; unsigned colcount = 0; auto &nz = m_terms[row]->m_nz; for (auto & col : nz) { v[col] = A(row,col) - lA(row,col); if (incremental) lA(row,col) = A(row,col); if (v[col] != 0.0) cols[colcount++] = col; } if (colcount > 0) { nl_double lamba = 0.0; nl_double w[m_pitch] = {0}; nl_double z[m_pitch]; /* compute w and lamba */ for (unsigned i = 0; i < iN; i++) z[i] = Ainv(i, row); /* u is row'th column */ for (unsigned j = 0; j < colcount; j++) lamba += v[cols[j]] * z[cols[j]]; for (unsigned j=0; jLE_compute_x(new_V); if (newton_raphson) { nl_double err = delta(new_V); store(new_V); return (err > this->m_params.m_accuracy) ? 2 : 1; } else { store(new_V); return 1; } } template inline int matrix_solver_sm_t::vsolve_non_dynamic(const bool newton_raphson) { this->build_LE_A(); this->build_LE_RHS(); for (unsigned i=0, iN=N(); i < iN; i++) m_last_RHS[i] = RHS(i); this->m_stat_calculations++; return this->solve_non_dynamic(newton_raphson); } template matrix_solver_sm_t::matrix_solver_sm_t(const solver_parameters_t *params, const int size) : matrix_solver_t(GAUSSIAN_ELIMINATION, params) , m_dim(size) { m_rails_temp = palloc_array(terms_t, N()); #if (NL_USE_DYNAMIC_ALLOCATION) m_A = palloc_array(nl_ext_double, N() * m_pitch); #endif for (unsigned k = 0; k < N(); k++) { m_terms[k] = palloc(terms_t); m_last_RHS[k] = 0.0; m_last_V[k] = 0.0; } } template matrix_solver_sm_t::matrix_solver_sm_t(const eSolverType type, const solver_parameters_t *params, const int size) : matrix_solver_t(type, params) , m_dim(size) { m_rails_temp = palloc_array(terms_t, N()); #if (NL_USE_DYNAMIC_ALLOCATION) m_A = palloc_array(nl_ext_double, N() * m_pitch); #endif for (unsigned k = 0; k < N(); k++) { m_terms[k] = palloc(terms_t); m_last_RHS[k] = 0.0; m_last_V[k] = 0.0; } } NETLIB_NAMESPACE_DEVICES_END() #endif /* NLD_MS_DIRECT_H_ */