virtual void vRun(void* threadData) override
{
MGPExecutor::ThreadData* data = (MGPExecutor::ThreadData*)threadData;
auto function = data->get();
GPContents* oldContent = mCollector->lock();
GPContents* input = NULL;
GPContents* mergeInput = NULL;
{
MGPAutoMutex __m(mInputMutex);
auto tempMergeInput = new GPContents;
input = _loadContent(mInputNum, mInput, mInputKeys);
for (int i=0; i<mVariableKey.size(); ++i)
{
auto v = mVariableKey[i];
MGPASSERT(v.first<=1);
if (v.first == 1)
{
tempMergeInput->pushContent(oldContent->getContent(v.second));
}
else
{
tempMergeInput->pushContent(input->getContent(v.second));
}
}
mergeInput = tempMergeInput->copyAsNoOwner();
tempMergeInput->decRef();
}
GPContents* tempOutput = function->vRun(mergeInput);
{
MGPAutoMutex __m(mInputMutex);
mergeInput->decRef();
input->decRef();
}
{
oldContent->setContent(0, tempOutput->getContent(0));
tempOutput->decRef();
mCollector->unlock(oldContent);
}
}
virtual void vRun(void* threadData) override
{
//GPCLOCK;
MGPExecutor::ThreadData* data = (MGPExecutor::ThreadData*)threadData;
auto function = data->get();
GPContents* orderedInput = NULL;
GPContents* orderedOriginInput = NULL;
{
//GPCLOCK;
MGPAutoMutex __m(mInputMutex);
GPContents* totalInput = _loadContent(mInputNum, mInput, mInputKeys);
orderedOriginInput = new GPContents;
for (int i=0; i<mVariableKey.size(); ++i)
{
orderedOriginInput->pushContent(totalInput->getContent(mVariableKey[i].second));
}
totalInput->decRef();
orderedInput = orderedOriginInput->copyAsNoOwner();
}
GPContents* output = NULL;
{
//GPCLOCK;
output = function->vRun(orderedInput);
}
{
//GPCLOCK;
MGPAutoMutex __m(mInputMutex);
orderedInput->decRef();
orderedOriginInput->decRef();
}
{
//GPCLOCK;
MGPAutoMutex __m(mOutputMutex);
mOutput->vSave(mOutputKeys, mOutput->nKeyNumber, output);
output->decRef();
}
}
void
SolverUnconstrained<Data,Problem>::makeStep( vector_type & _x, vector_type & _s, value_type _Delta,
f_type& __fx,
vector_type & _Tgrad_fx,
banded_matrix_type & _Hg,
symmetric_matrix_type & _Thess_fxT, symmetric_matrix_type & _Htil )
{
M_theta.update( _Delta, _x, _s );
_Tgrad_fx = prod( M_theta(), __fx.gradient( 0 ) );
banded_matrix_type __diag_grad_fx( _E_n, _E_n, 0, 0 );
matrix<value_type> __m( outer_prod( _Tgrad_fx,
scalar_vector<value_type>( _Tgrad_fx.size(), 1 ) ) );
__diag_grad_fx = banded_adaptor<matrix<value_type> >( __m, 0, 0 );
_Hg = prod( M_theta.jacobian(), __diag_grad_fx );
_Thess_fxT = prod( M_theta(), prod( __fx.hessian( 0 ), M_theta() ) );
_Htil = _Thess_fxT + _Hg;
}