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/*
* phylokernelsitemodel.h
* optimize SIMD likelihood function for site-specific model
* Created on: Jan 12, 2016
* Author: minh
*/
#ifndef PHYLOKERNELSITEMODEL_H_
#define PHYLOKERNELSITEMODEL_H_
#include "phylotree.h"
#include "model/modelset.h"
inline double horizontal_add(double x) {
return x;
}
template <class VectorClass, const int VCSIZE, const int nstates>
void PhyloTree::computeSitemodelPartialLikelihoodEigenSIMD(PhyloNeighbor *dad_branch, PhyloNode *dad) {
if (dad_branch->node->degree() > 3) {
// TODO: SIMD version for multifurcating node
computeSitemodelPartialLikelihoodEigen(dad_branch, dad);
return;
}
// don't recompute the likelihood
ASSERT(dad);
if (dad_branch->partial_lh_computed & 1)
return;
dad_branch->partial_lh_computed |= 1;
PhyloNode *node = (PhyloNode*)(dad_branch->node);
size_t nptn = aln->size(), tip_block_size = get_safe_upper_limit(nptn)*nstates;
size_t ptn, c;
size_t ncat = site_rate->getNRate();
size_t i, x, j;
size_t block = nstates * ncat;
ModelSet *models = (ModelSet*) model;
ASSERT(models->size() == nptn);
if (node->isLeaf()) {
dad_branch->lh_scale_factor = 0.0;
// scale number must be ZERO
// memset(dad_branch->scale_num, 0, nptn * sizeof(UBYTE));
if (!tip_partial_lh_computed)
computeTipPartialLikelihood();
return;
}
dad_branch->lh_scale_factor = 0.0;
// internal node
PhyloNeighbor *left = NULL, *right = NULL; // left & right are two neighbors leading to 2 subtrees
FOR_NEIGHBOR_IT(node, dad, it) {
PhyloNeighbor *nei = (PhyloNeighbor*)*it;
if (!left) left = (PhyloNeighbor*)(*it); else right = (PhyloNeighbor*)(*it);
if ((nei->partial_lh_computed & 1) == 0)
computeSitemodelPartialLikelihoodEigenSIMD<VectorClass, VCSIZE, nstates>(nei, node);
dad_branch->lh_scale_factor += nei->lh_scale_factor;
}
if (params->lh_mem_save == LM_PER_NODE && !dad_branch->partial_lh) {
// re-orient partial_lh
bool done = false;
FOR_NEIGHBOR_IT(node, dad, it2) {
PhyloNeighbor *backnei = ((PhyloNeighbor*)(*it2)->node->findNeighbor(node));
if (backnei->partial_lh) {
dad_branch->partial_lh = backnei->partial_lh;
dad_branch->scale_num = backnei->scale_num;
backnei->partial_lh = NULL;
backnei->scale_num = NULL;
backnei->partial_lh_computed &= ~1; // clear bit
done = true;
break;
}
}
ASSERT(done && "partial_lh is not re-oriented");
}
double sum_scale = 0.0;
if (!left->node->isLeaf() && right->node->isLeaf()) {
PhyloNeighbor *tmp = left;
left = right;
right = tmp;
}
ASSERT(node->degree() == 3); // does not work with multifurcating tree yet
if (left->node->isLeaf() && right->node->isLeaf()) {
/*--------------------- TIP-TIP (cherry) case ------------------*/
double *tip_partial_lh_left = tip_partial_lh + (left->node->id * tip_block_size);
double *tip_partial_lh_right = tip_partial_lh + (right->node->id * tip_block_size);
// scale number must be ZERO
memset(dad_branch->scale_num, 0, nptn * sizeof(UBYTE));
#ifdef _OPENMP
#pragma omp parallel for private(ptn, c, x, i, j) schedule(static)
#endif
for (ptn = 0; ptn < nptn; ptn++) {
VectorClass partial_lh_tmp[nstates/VCSIZE];
VectorClass *partial_lh = (VectorClass*)(dad_branch->partial_lh + ptn*block);
VectorClass *partial_lh_left = (VectorClass*)(tip_partial_lh_left + ptn*nstates);
VectorClass *partial_lh_right = (VectorClass*)(tip_partial_lh_right + ptn*nstates);
VectorClass expleft[nstates/VCSIZE];
VectorClass expright[nstates/VCSIZE];
VectorClass *eval = (VectorClass*)(models->at(ptn)->getEigenvalues());
VectorClass *evec = (VectorClass*)(models->at(ptn)->getEigenvectors());
VectorClass *inv_evec = (VectorClass*)(models->at(ptn)->getInverseEigenvectors());
VectorClass vleft[VCSIZE];
VectorClass vright[VCSIZE];
VectorClass res[VCSIZE];
VectorClass len_left, len_right;
VectorClass *this_evec;
for (c = 0; c < ncat; c++) {
len_left = site_rate->getRate(c) * left->length;
len_right = site_rate->getRate(c) * right->length;
for (i = 0; i < nstates/VCSIZE; i++) {
expleft[i] = exp(eval[i]*len_left) * partial_lh_left[i];
expright[i] = exp(eval[i]*len_right) * partial_lh_right[i];
}
// compute real partial likelihood vector
this_evec = evec;
for (x = 0; x < nstates/VCSIZE; x++) {
for (j = 0; j < VCSIZE; j++) {
vleft[j] = 0.0;
vright[j] = 0.0;
for (i = 0; i < nstates/VCSIZE; i++) {
vleft[j] = mul_add(this_evec[i], expleft[i], vleft[j]);
vright[j] = mul_add(this_evec[i], expright[i], vright[j]);
}
this_evec += nstates/VCSIZE;
}
partial_lh_tmp[x] = horizontal_add(vleft)*horizontal_add(vright);
}
// compute dot-product with inv_eigenvector
this_evec = inv_evec;
for (i = 0; i < nstates/VCSIZE; i++) {
for (j = 0; j < VCSIZE; j++) {
res[j] = 0.0;
for (x = 0; x < nstates/VCSIZE; x++) {
res[j] = mul_add(partial_lh_tmp[x], this_evec[x], res[j]);
}
this_evec += nstates/VCSIZE;
}
partial_lh[i] = horizontal_add(res);
}
// partial_lh_left += nstates/VCSIZE;
// partial_lh_right += nstates/VCSIZE;
partial_lh += nstates/VCSIZE;
}
}
} else if (left->node->isLeaf() && !right->node->isLeaf()) {
/*--------------------- TIP-INTERNAL NODE case ------------------*/
double *tip_partial_lh_left = tip_partial_lh + (left->node->id * tip_block_size);
// only take scale_num from the right subtree
memcpy(dad_branch->scale_num, right->scale_num, nptn * sizeof(UBYTE));
#ifdef _OPENMP
#pragma omp parallel for reduction(+: sum_scale) private(ptn, c, x, i, j) schedule(static)
#endif
for (ptn = 0; ptn < nptn; ptn++) {
VectorClass partial_lh_tmp[nstates/VCSIZE];
VectorClass *partial_lh = (VectorClass*)(dad_branch->partial_lh + ptn*block);
VectorClass *partial_lh_left = (VectorClass*)(tip_partial_lh_left + ptn*nstates);
VectorClass *partial_lh_right = (VectorClass*)(right->partial_lh + ptn*block);
VectorClass lh_max = 0.0;
VectorClass expleft[nstates/VCSIZE];
VectorClass expright[nstates/VCSIZE];
VectorClass *eval = (VectorClass*)(models->at(ptn)->getEigenvalues());
VectorClass *evec = (VectorClass*)(models->at(ptn)->getEigenvectors());
VectorClass *inv_evec = (VectorClass*)(models->at(ptn)->getInverseEigenvectors());
VectorClass vleft[VCSIZE];
VectorClass vright[VCSIZE];
VectorClass res[VCSIZE];
VectorClass len_left, len_right;
VectorClass *this_evec;
for (c = 0; c < ncat; c++) {
len_left = site_rate->getRate(c) * left->length;
len_right = site_rate->getRate(c) * right->length;
for (i = 0; i < nstates/VCSIZE; i++) {
expleft[i] = exp(eval[i]*len_left) * partial_lh_left[i];
expright[i] = exp(eval[i]*len_right) * partial_lh_right[i];
}
// compute real partial likelihood vector
this_evec = evec;
for (x = 0; x < nstates/VCSIZE; x++) {
for (j = 0; j < VCSIZE; j++) {
vleft[j] = 0.0;
vright[j] = 0.0;
for (i = 0; i < nstates/VCSIZE; i++) {
vleft[j] = mul_add(this_evec[i], expleft[i], vleft[j]);
vright[j] = mul_add(this_evec[i], expright[i], vright[j]);
}
this_evec += nstates/VCSIZE;
}
partial_lh_tmp[x] = horizontal_add(vleft)*horizontal_add(vright);
}
// compute dot-product with inv_eigenvector
this_evec = inv_evec;
for (i = 0; i < nstates/VCSIZE; i++) {
for (j = 0; j < VCSIZE; j++) {
res[j] = 0.0;
for (x = 0; x < nstates/VCSIZE; x++) {
res[j] = mul_add(partial_lh_tmp[x], this_evec[x], res[j]);
}
this_evec += nstates/VCSIZE;
}
lh_max = max(lh_max, abs(partial_lh[i] = horizontal_add(res)));
}
// partial_lh_left += nstates/VCSIZE;
partial_lh_right += nstates/VCSIZE;
partial_lh += nstates/VCSIZE;
}
// check if one should scale partial likelihoods
double dmax = horizontal_max(lh_max);
if (dmax < SCALING_THRESHOLD) {
partial_lh = (VectorClass*)(dad_branch->partial_lh + ptn*block);
if (dmax == 0.0) {
// for very shitty data
for (c = 0; c < ncat; c++)
memcpy(&partial_lh[c*nstates/VCSIZE], &tip_partial_lh[ptn*nstates], nstates*sizeof(double));
sum_scale += LOG_SCALING_THRESHOLD* 4 * ptn_freq[ptn];
//sum_scale += log(lh_max) * ptn_freq[ptn];
dad_branch->scale_num[ptn] += 4;
int nsite = aln->getNSite();
for (i = 0, x = 0; i < nsite && x < ptn_freq[ptn]; i++)
if (aln->getPatternID(i) == ptn) {
outWarning((string)"Numerical underflow for site " + convertIntToString(i+1));
x++;
}
} else {
// now do the likelihood scaling
for (i = 0; i < block/VCSIZE; i++) {
partial_lh[i] *= SCALING_THRESHOLD_INVER;
//partial_lh[i] /= lh_max;
}
// unobserved const pattern will never have underflow
sum_scale += LOG_SCALING_THRESHOLD * ptn_freq[ptn];
//sum_scale += log(lh_max) * ptn_freq[ptn];
dad_branch->scale_num[ptn] += 1;
}
}
}
dad_branch->lh_scale_factor += sum_scale;
} else {
/*--------------------- INTERNAL-INTERNAL NODE case ------------------*/
#ifdef _OPENMP
#pragma omp parallel for reduction(+: sum_scale) private(ptn, c, x, i, j) schedule(static)
#endif
for (ptn = 0; ptn < nptn; ptn++) {
VectorClass partial_lh_tmp[nstates/VCSIZE];
VectorClass *partial_lh = (VectorClass*)(dad_branch->partial_lh + ptn*block);
VectorClass *partial_lh_left = (VectorClass*)(left->partial_lh + ptn*block);
VectorClass *partial_lh_right = (VectorClass*)(right->partial_lh + ptn*block);
VectorClass lh_max = 0.0;
VectorClass expleft[nstates/VCSIZE];
VectorClass expright[nstates/VCSIZE];
VectorClass *eval = (VectorClass*)(models->at(ptn)->getEigenvalues());
VectorClass *evec = (VectorClass*)(models->at(ptn)->getEigenvectors());
VectorClass *inv_evec = (VectorClass*)(models->at(ptn)->getInverseEigenvectors());
VectorClass vleft[VCSIZE];
VectorClass vright[VCSIZE];
VectorClass res[VCSIZE];
VectorClass len_left, len_right;
VectorClass *this_evec;
dad_branch->scale_num[ptn] = left->scale_num[ptn] + right->scale_num[ptn];
for (c = 0; c < ncat; c++) {
len_left = site_rate->getRate(c) * left->length;
len_right = site_rate->getRate(c) * right->length;
for (i = 0; i < nstates/VCSIZE; i++) {
expleft[i] = exp(eval[i]*len_left) * partial_lh_left[i];
expright[i] = exp(eval[i]*len_right) * partial_lh_right[i];
}
// compute real partial likelihood vector
this_evec = evec;
for (x = 0; x < nstates/VCSIZE; x++) {
for (j = 0; j < VCSIZE; j++) {
vleft[j] = 0.0;
vright[j] = 0.0;
// this_evec = evec + (x*VCSIZE+j)*nstates/VCSIZE;
for (i = 0; i < nstates/VCSIZE; i++) {
vleft[j] = mul_add(this_evec[i], expleft[i], vleft[j]);
vright[j] = mul_add(this_evec[i], expright[i], vright[j]);
}
this_evec += nstates/VCSIZE;
}
partial_lh_tmp[x] = horizontal_add(vleft)*horizontal_add(vright);
}
// compute dot-product with inv_eigenvector
this_evec = inv_evec;
for (i = 0; i < nstates/VCSIZE; i++) {
for (j = 0; j < VCSIZE; j++) {
res[j] = 0.0;
for (x = 0; x < nstates/VCSIZE; x++) {
res[j] = mul_add(partial_lh_tmp[x], this_evec[x], res[j]);
}
this_evec += nstates/VCSIZE;
}
lh_max = max(lh_max, abs(partial_lh[i] = horizontal_add(res)));
}
partial_lh_left += nstates/VCSIZE;
partial_lh_right += nstates/VCSIZE;
partial_lh += nstates/VCSIZE;
}
// check if one should scale partial likelihoods
double dmax = horizontal_max(lh_max);
if (dmax < SCALING_THRESHOLD) {
partial_lh = (VectorClass*)(dad_branch->partial_lh + ptn*block);
if (dmax == 0.0) {
// for very shitty data
for (c = 0; c < ncat; c++)
memcpy(&partial_lh[c*nstates/VCSIZE], &tip_partial_lh[ptn*nstates], nstates*sizeof(double));
sum_scale += LOG_SCALING_THRESHOLD* 4 * ptn_freq[ptn];
//sum_scale += log(lh_max) * ptn_freq[ptn];
dad_branch->scale_num[ptn] += 4;
int nsite = aln->getNSite();
for (i = 0, x = 0; i < nsite && x < ptn_freq[ptn]; i++)
if (aln->getPatternID(i) == ptn) {
outWarning((string)"Numerical underflow for site " + convertIntToString(i+1));
x++;
}
} else {
// now do the likelihood scaling
for (i = 0; i < block/VCSIZE; i++) {
partial_lh[i] *= SCALING_THRESHOLD_INVER;
//partial_lh[i] /= lh_max;
}
// unobserved const pattern will never have underflow
sum_scale += LOG_SCALING_THRESHOLD * ptn_freq[ptn];
//sum_scale += log(lh_max) * ptn_freq[ptn];
dad_branch->scale_num[ptn] += 1;
}
}
}
dad_branch->lh_scale_factor += sum_scale;
}
}
template <class VectorClass, const int VCSIZE, const int nstates>
void PhyloTree::computeSitemodelLikelihoodDervEigenSIMD(PhyloNeighbor *dad_branch, PhyloNode *dad, double &df, double &ddf) {
PhyloNode *node = (PhyloNode*) dad_branch->node;
PhyloNeighbor *node_branch = (PhyloNeighbor*) node->findNeighbor(dad);
if (!central_partial_lh)
initializeAllPartialLh();
if (node->isLeaf()) {
PhyloNode *tmp_node = dad;
dad = node;
node = tmp_node;
PhyloNeighbor *tmp_nei = dad_branch;
dad_branch = node_branch;
node_branch = tmp_nei;
}
if ((dad_branch->partial_lh_computed & 1) == 0)
computeSitemodelPartialLikelihoodEigenSIMD<VectorClass,VCSIZE,nstates>(dad_branch, dad);
if ((node_branch->partial_lh_computed & 1) == 0)
computeSitemodelPartialLikelihoodEigenSIMD<VectorClass,VCSIZE,nstates>(node_branch, node);
// size_t nstates = aln->num_states;
size_t ncat = site_rate->getNRate();
size_t block = ncat * nstates;
size_t ptn; // for big data size > 4GB memory required
size_t c, i, j;
size_t nptn = aln->size();
size_t maxptn = ((nptn+VCSIZE-1)/VCSIZE)*VCSIZE;
ASSERT(theta_all);
if (!theta_computed) {
// precompute theta for fast branch length optimization
if (dad->isLeaf()) {
// special treatment for TIP-INTERNAL NODE case
double *tip_partial_lh_node = tip_partial_lh + (dad->id * get_safe_upper_limit(nptn)*nstates);
#ifdef _OPENMP
#pragma omp parallel for private(ptn, i, c) schedule(static)
#endif
for (ptn = 0; ptn < nptn; ptn++) {
VectorClass *partial_lh_dad = (VectorClass*)(dad_branch->partial_lh + ptn*block);
VectorClass *theta = (VectorClass*)(theta_all + ptn*block);
VectorClass *lh_tip = (VectorClass*)(tip_partial_lh_node + ptn*nstates);
for (c = 0; c < ncat; c++) {
for (i = 0; i < nstates/VCSIZE; i++) {
theta[i] = lh_tip[i] * partial_lh_dad[i];
}
partial_lh_dad += nstates/VCSIZE;
theta += nstates/VCSIZE;
}
}
} else
{
// both dad and node are internal nodes
size_t block_VCSIZE = block/VCSIZE;
// size_t all_entries = nptn*block;
#ifdef _OPENMP
#pragma omp parallel for private(ptn, i) schedule(static)
#endif
for (ptn = 0; ptn < nptn; ptn++) {
VectorClass *partial_lh_dad = (VectorClass*)(dad_branch->partial_lh + ptn*block);
VectorClass *theta = (VectorClass*)(theta_all + ptn*block);
VectorClass *partial_lh_node = (VectorClass*)(node_branch->partial_lh + ptn*block);
for (i = 0; i < block_VCSIZE; i++) {
theta[i] = partial_lh_node[i] * partial_lh_dad[i];
}
}
}
if (nptn < maxptn) {
// copy dummy values
for (ptn = nptn; ptn < maxptn; ptn++)
memcpy(&theta_all[ptn*block], theta_all, block*sizeof(double));
}
theta_computed = true;
}
ModelSet *models = (ModelSet*)model;
VectorClass my_df = 0.0, my_ddf = 0.0;
VectorClass dad_length = dad_branch->length;
VectorClass unit = 1.0;
#ifdef _OPENMP
#pragma omp parallel private(ptn, i, c, j)
{
VectorClass my_df_thread = 0.0;
VectorClass my_ddf_thread = 0.0;
#pragma omp for nowait schedule(static)
#endif
for (ptn = 0; ptn < nptn; ptn+=VCSIZE) {
VectorClass lh_ptn[VCSIZE];
VectorClass df_ptn[VCSIZE];
VectorClass ddf_ptn[VCSIZE];
VectorClass *theta = (VectorClass*)(theta_all + ptn*block);
VectorClass* eval;
for (j = 0; j < VCSIZE; j++) {
lh_ptn[j] = 0.0;
df_ptn[j] = 0.0;
ddf_ptn[j] = 0.0;
if (ptn+j < nptn) {
eval = (VectorClass*)models->at(ptn+j)->getEigenvalues();
} else {
eval = (VectorClass*)models->at(nptn-1)->getEigenvalues();
}
for (c = 0; c < ncat; c++) {
VectorClass cat_rate = site_rate->getRate(c);
VectorClass lh_cat = 0.0, df_cat = 0.0, ddf_cat = 0.0;
for (i = 0; i < nstates/VCSIZE; i++) {
VectorClass cof = eval[i]*cat_rate;
VectorClass val = exp(cof*dad_length) * theta[i];
VectorClass val1 = cof*val;
lh_cat += val;
df_cat += val1;
ddf_cat = mul_add(cof, val1, ddf_cat);
}
VectorClass prop = site_rate->getProp(c);
lh_ptn[j] = mul_add(prop, lh_cat, lh_ptn[j]);
df_ptn[j] = mul_add(prop, df_cat, df_ptn[j]);
ddf_ptn[j] = mul_add(prop, ddf_cat, ddf_ptn[j]);
theta += nstates/VCSIZE;
}
}
VectorClass inv_lh_ptn = horizontal_add(lh_ptn) + VectorClass().load_a(&ptn_invar[ptn]);
inv_lh_ptn = unit / abs(inv_lh_ptn);
VectorClass freq;
freq.load_a(&ptn_freq[ptn]);
VectorClass df_ptn_sum = horizontal_add(df_ptn) * inv_lh_ptn;
VectorClass ddf_ptn_sum = horizontal_add(ddf_ptn) * inv_lh_ptn;
ddf_ptn_sum = nmul_add(df_ptn_sum, df_ptn_sum, ddf_ptn_sum);
#ifdef _OPENMP
my_df_thread = mul_add(df_ptn_sum, freq, my_df_thread);
my_ddf_thread = mul_add(ddf_ptn_sum, freq, my_ddf_thread);
#else
my_df = mul_add(df_ptn_sum, freq, my_df);
my_ddf = mul_add(ddf_ptn_sum, freq, my_ddf);
#endif
} // for loop
#ifdef _OPENMP
#pragma omp critical
{
my_df += my_df_thread;
my_ddf += my_ddf_thread;
}
}
#endif
df = horizontal_add(my_df);
ddf = horizontal_add(my_ddf);
if (isnan(df) || isinf(df)) {
df = 0.0;
ddf = 0.0;
outWarning("Numerical instability (some site-likelihood = 0)");
}
}
template <class VectorClass, const int VCSIZE, const int nstates>
double PhyloTree::computeSitemodelLikelihoodBranchEigenSIMD(PhyloNeighbor *dad_branch, PhyloNode *dad) {
PhyloNode *node = (PhyloNode*) dad_branch->node;
PhyloNeighbor *node_branch = (PhyloNeighbor*) node->findNeighbor(dad);
if (!central_partial_lh)
initializeAllPartialLh();
if (node->isLeaf()) {
PhyloNode *tmp_node = dad;
dad = node;
node = tmp_node;
PhyloNeighbor *tmp_nei = dad_branch;
dad_branch = node_branch;
node_branch = tmp_nei;
}
if ((dad_branch->partial_lh_computed & 1) == 0)
computeSitemodelPartialLikelihoodEigenSIMD<VectorClass, VCSIZE, nstates>(dad_branch, dad);
if ((node_branch->partial_lh_computed & 1) == 0)
computeSitemodelPartialLikelihoodEigenSIMD<VectorClass, VCSIZE, nstates>(node_branch, node);
size_t ncat = site_rate->getNRate();
size_t block = ncat * nstates;
size_t ptn; // for big data size > 4GB memory required
size_t c, i, j;
size_t nptn = aln->size();
size_t maxptn = get_safe_upper_limit(nptn);
ModelSet *models = (ModelSet*)model;
VectorClass tree_lh = 0.0;
VectorClass *cat_length = aligned_alloc<VectorClass>(ncat);
VectorClass *cat_prop = aligned_alloc<VectorClass>(ncat);
for (c = 0; c < ncat; c++) {
cat_length[c] = site_rate->getRate(c) * dad_branch->length;
cat_prop[c] = site_rate->getProp(c);
}
if (dad->isLeaf()) {
// copy dummy values because VectorClass will access beyond nptn
for (ptn = nptn; ptn < maxptn; ptn++)
memcpy(&dad_branch->partial_lh[ptn*block], &dad_branch->partial_lh[(ptn-1)*block], block*sizeof(double));
// special treatment for TIP-INTERNAL NODE case
double *tip_partial_lh_node = tip_partial_lh + (dad->id * get_safe_upper_limit(nptn)*nstates);
#ifdef _OPENMP
#pragma omp parallel private(ptn, i, c, j)
{
VectorClass tree_lh_thread = 0.0;
#pragma omp for nowait schedule(static)
#endif
for (ptn = 0; ptn < nptn; ptn+=VCSIZE) {
VectorClass lh_ptn[VCSIZE];
VectorClass* eval;
VectorClass *partial_lh_dad = (VectorClass*)(dad_branch->partial_lh + ptn*block);
VectorClass *partial_lh_node = (VectorClass*)(tip_partial_lh_node + ptn*nstates);
for (j = 0; j < VCSIZE; j++) {
lh_ptn[j] = 0.0;
if (ptn+j < nptn) {
eval = (VectorClass*)models->at(ptn+j)->getEigenvalues();
} else {
eval = (VectorClass*)models->at(nptn-1)->getEigenvalues();
}
for (c = 0; c < ncat; c++) {
VectorClass lh_cat = 0.0;
for (i = 0; i < nstates/VCSIZE; i++) {
lh_cat=mul_add(exp(eval[i]*cat_length[c]), partial_lh_dad[i] * partial_lh_node[i], lh_cat);
}
lh_ptn[j] = mul_add(cat_prop[c], lh_cat, lh_ptn[j]);
partial_lh_dad += nstates/VCSIZE;
// partial_lh_node += nstates/VCSIZE;
}
partial_lh_node += nstates/VCSIZE;
}
VectorClass freq;
freq.load_a(&ptn_freq[ptn]);
VectorClass lh_ptn_sum = horizontal_add(lh_ptn) + VectorClass().load_a(&ptn_invar[ptn]);
lh_ptn_sum = log(abs(lh_ptn_sum));
lh_ptn_sum.store_a(&_pattern_lh[ptn]);
#ifdef _OPENMP
tree_lh_thread = mul_add(lh_ptn_sum, freq, tree_lh_thread);
#else
tree_lh = mul_add(lh_ptn_sum, freq, tree_lh);
#endif
} // for loop
#ifdef _OPENMP
#pragma omp critical
{
tree_lh += tree_lh_thread;
}
}
#endif
} else
{
// both dad and node are internal nodes
// copy dummy values because VectorClass will access beyond nptn
for (ptn = nptn; ptn < maxptn; ptn++) {
memcpy(&dad_branch->partial_lh[ptn*block], &dad_branch->partial_lh[(ptn-1)*block], block*sizeof(double));
memcpy(&node_branch->partial_lh[ptn*block], &node_branch->partial_lh[(ptn-1)*block], block*sizeof(double));
}
#ifdef _OPENMP
#pragma omp parallel private(ptn, i, c, j)
{
VectorClass tree_lh_thread = 0.0;
#pragma omp for nowait schedule(static)
#endif
for (ptn = 0; ptn < nptn; ptn+=VCSIZE) {
VectorClass lh_ptn[VCSIZE];
VectorClass* eval;
VectorClass *partial_lh_dad = (VectorClass*)(dad_branch->partial_lh + ptn*block);
VectorClass *partial_lh_node = (VectorClass*)(node_branch->partial_lh + ptn*block);
for (j = 0; j < VCSIZE; j++) {
lh_ptn[j] = 0.0;
if (ptn+j < nptn) {
eval = (VectorClass*)models->at(ptn+j)->getEigenvalues();
} else {
eval = (VectorClass*)models->at(nptn-1)->getEigenvalues();
}
for (c = 0; c < ncat; c++) {
VectorClass lh_cat = 0.0;
for (i = 0; i < nstates/VCSIZE; i++) {
lh_cat = mul_add(exp(eval[i]*cat_length[c]), partial_lh_dad[i] * partial_lh_node[i], lh_cat);
}
lh_ptn[j] = mul_add(cat_prop[c], lh_cat, lh_ptn[j]);
partial_lh_dad += nstates/VCSIZE;
partial_lh_node += nstates/VCSIZE;
}
}
VectorClass freq;
freq.load_a(&ptn_freq[ptn]);
VectorClass lh_ptn_sum = horizontal_add(lh_ptn) + VectorClass().load_a(&ptn_invar[ptn]);
lh_ptn_sum = log(abs(lh_ptn_sum));
lh_ptn_sum.store_a(&_pattern_lh[ptn]);
#ifdef _OPENMP
tree_lh_thread = mul_add(lh_ptn_sum, freq, tree_lh_thread);
#else
tree_lh = mul_add(lh_ptn_sum, freq, tree_lh);
#endif
} // for loop
#ifdef _OPENMP
#pragma omp critical
{
tree_lh += tree_lh_thread;
}
}
#endif
}
double tree_lh_final = horizontal_add(tree_lh) + node_branch->lh_scale_factor + dad_branch->lh_scale_factor;
if (isnan(tree_lh_final) || isinf(tree_lh_final)) {
cout << "WARNING: Numerical underflow caused by alignment sites";
i = aln->getNSite();
int j;
for (j = 0, c = 0; j < i; j++) {
ptn = aln->getPatternID(j);
if (isnan(_pattern_lh[ptn]) || isinf(_pattern_lh[ptn])) {
cout << " " << j+1;
c++;
if (c >= 10) {
cout << " ...";
break;
}
}
}
cout << endl;
tree_lh_final = node_branch->lh_scale_factor + dad_branch->lh_scale_factor;
for (ptn = 0; ptn < nptn; ptn++) {
if (isnan(_pattern_lh[ptn]) || isinf(_pattern_lh[ptn])) {
_pattern_lh[ptn] = LOG_SCALING_THRESHOLD*4; // log(2^(-1024))
}
tree_lh_final += _pattern_lh[ptn] * ptn_freq[ptn];
}
}
ASSERT(!isnan(tree_lh_final) && !isinf(tree_lh_final));
aligned_free(cat_prop);
aligned_free(cat_length);
return tree_lh_final;
}
template <class VectorClass, const int VCSIZE, const int nstates>
double PhyloTree::computeSitemodelLikelihoodFromBufferEigenSIMD() {
ASSERT(theta_all && theta_computed);
// size_t nstates = aln->num_states;
size_t ncat = site_rate->getNRate();
size_t block = ncat * nstates;
size_t ptn; // for big data size > 4GB memory required
size_t c, i, j;
size_t nptn = aln->size();
ModelSet *models = (ModelSet*)model;
VectorClass tree_lh = 0.0;
VectorClass *cat_length = aligned_alloc<VectorClass>(ncat);
VectorClass *cat_prop = aligned_alloc<VectorClass>(ncat);
for (c = 0; c < ncat; c++) {
cat_length[c] = site_rate->getRate(c) * current_it->length;
cat_prop[c] = site_rate->getProp(c);
}
#ifdef _OPENMP
#pragma omp parallel private(ptn, i, c, j)
{
VectorClass tree_lh_thread = 0.0;
#pragma omp for nowait schedule(static)
#endif
for (ptn = 0; ptn < nptn; ptn+=VCSIZE) {
VectorClass lh_ptn[VCSIZE];
VectorClass* eval;
VectorClass *theta = (VectorClass*)(theta_all + ptn*block);
for (j = 0; j < VCSIZE; j++) {
lh_ptn[j] = 0.0;
if (ptn+j < nptn) {
eval = (VectorClass*)models->at(ptn+j)->getEigenvalues();
} else {
eval = (VectorClass*)models->at(nptn-1)->getEigenvalues();
}
for (c = 0; c < ncat; c++) {
VectorClass lh_cat = 0.0;
for (i = 0; i < nstates/VCSIZE; i++) {
lh_cat = mul_add(exp(eval[i]*cat_length[c]), theta[i], lh_cat);
}
lh_ptn[j] = mul_add(cat_prop[c], lh_cat, lh_ptn[j]);
theta += nstates/VCSIZE;
}
}
VectorClass freq;
freq.load_a(&ptn_freq[ptn]);
VectorClass lh_ptn_sum = horizontal_add(lh_ptn) + VectorClass().load_a(&ptn_invar[ptn]);
lh_ptn_sum = log(abs(lh_ptn_sum));
lh_ptn_sum.store_a(&_pattern_lh[ptn]);
#ifdef _OPENMP
tree_lh_thread = mul_add(lh_ptn_sum, freq, tree_lh_thread);
#else
tree_lh = mul_add(lh_ptn_sum, freq, tree_lh);
#endif
} // for loop
#ifdef _OPENMP
#pragma omp critical
{
tree_lh += tree_lh_thread;
}
}
#endif
double tree_lh_final = horizontal_add(tree_lh) + current_it->lh_scale_factor + current_it_back->lh_scale_factor;
aligned_free(cat_prop);
aligned_free(cat_length);
return tree_lh_final;
}
#endif /* PHYLOKERNELSITEMODEL_H_ */
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