// The following two files provide classes and information for multithreaded
// programs. In the first one, the classes and functions are declared which we
-// need to start new threads and to wait for threads to return (i.e. the
-// <code>Thread</code> class and the <code>new_thread</code> functions). The
+// need to do assembly in parallel (i.e. the
+// <code>WorkStream</code> namespace). The
// second file has a class <code>MultithreadInfo</code> (and a global object
// <code>multithread_info</code> of that type) which can be used to query the
// number of processors in your system, which is often useful when deciding
// <code>AssemblyScratchData</code> and
// <code>AssemblyCopyData</code> structures is). Rather, we will
// only discuss the specific implementation.
+ //
+ // If you read the page mentioned above, you will find that in
+ // order to parallelize assembly, we need two data structures --
+ // one that corresponds to data that we need during local
+ // integration ("scratch data", i.e., things we only need as
+ // temporary storage), and one that carries information from the
+ // local integration to the function that then adds the local
+ // contributions to the corresponding elements of the global
+ // matrix. The former of these typically contains the FEValues and
+ // FEFaceValues objects, whereas the latter has the local matrix,
+ // local right hand side, and information about which degrees of
+ // freedom live on the cell for which we are assembling a local
+ // contribution. With this information, the following should be
+ // relatively self-explanatory:
struct AssemblyScratchData
- {};
+ {
+ AssemblyScratchData (const FiniteElement<dim> &fe);
+ AssemblyScratchData (const AssemblyScratchData &scratch_data);
+
+ FEValues<dim> fe_values;
+ FEFaceValues<dim> fe_face_values;
+ };
struct AssemblyCopyData
{
- FullMatrix<double> cell_matrix;
- Vector<double> cell_rhs;
+ FullMatrix<double> cell_matrix;
+ Vector<double> cell_rhs;
std::vector<types::global_dof_index> local_dof_indices;
};
// In the following function, the matrix and right hand side are
// assembled. As stated in the documentation of the main class above, it
// does not do this itself, but rather delegates to the function following
- // next, by splitting up the range of cells into chunks of approximately the
- // same size and assembling on each of these chunks in parallel.
+ // next, utilizing the WorkStream concept discussed in @ref threads .
+ //
+ // If you have looked through the @ref threads module, you will have
+ // seen that assembling in parallel does not take an incredible
+ // amount of extra code as long as you diligently describe what the
+ // scratch and copy data objects are, and if you define suitable
+ // functios for the local assembly and the copy operation from local
+ // contributions to global objects. This done, the following will do
+ // all the heavy lifting to get these operations done on multiple
+ // threads on as many cores as you have in your system:
template <int dim>
void AdvectionProblem<dim>::assemble_system ()
{
- // First, we want to find out how many threads shall assemble the matrix
- // in parallel. A reasonable choice would be that each processor in your
- // system processes one chunk of cells; if we were to use this
- // information, we could use the value of the global variable
- // <code>multithread_info.n_cpus</code>, which is determined at start-up
- // time of your program automatically. (Note that if the library was not
- // configured for multithreading, then the number of CPUs is set to one.)
- // However, sometimes there might be reasons to use another value. For
- // example, you might want to use less processors than there are in your
- // system in order not to use too many computational resources. For
- // this reason, the number of threads can be set by
- // <code>MultithreadInfo::set_thread_limit</code> and the current value
- // is returned by n_threads(). This
- // is also queried by functions inside the library to determine
- // how many threads they shall create.
-
- // It is worth noting, however, that this setup determines the load
- // distribution onto processor in a static way: it does not take into
- // account that some other part of our program may also be running
- // something in parallel at the same time as we get here (this is not the
- // case in the current program, but may easily be the case in more complex
- // applications). A discussion of how to deal with this case can be found
- // in the @ref threads module.
- //
- // Next, we need an object which is capable of keeping track of the
- // threads we created, and allows us to wait until they all have finished
- // (to <code>join</code> them in the language of threads). The
- // Threads::ThreadGroup class does this, which is basically just a
- // container for objects of type Threads::Thread that represent a single
- // thread; Threads::Thread is what the Threads::new_thread function below
- // will return when we start a new thread.
- //
- // Note that both Threads::ThreadGroup and Threads::Thread have a template
- // argument that represents the return type of the function being called
- // on a separate thread. Since most of the functions that we will call on
- // different threads have return type <code>void</code>, the template
- // argument has a default value <code>void</code>, so that in that case it
- // can be omitted. (However, you still need to write the angle brackets,
- // even if they are empty.)
- //
- // If you did not configure for multithreading, then the
- // <code>new_thread</code> function that is supposed to start a new thread
- // in parallel only executes the function which should be run in parallel,
- // waits for it to return (i.e. the function is executed sequentially),
- // and puts the return value into the <code>Thread</code>
- // object. Likewise, the function <code>join</code> that is supposed to
- // wait for all spawned threads to return, returns immediately, as there
- // can't be any threads running.
-
- // Now we have to split the range of cells into chunks of approximately
- // the same size. Each thread will then assemble the local contributions
- // of the cells within its chunk and transfer these contributions to the
- // global matrix. As splitting a range of cells is a rather common task
- // when using multithreading, there is a function in the
- // <code>Threads</code> namespace that does exactly this. In fact, it does
- // this not only for a range of cell iterators, but for iterators in
- // general, so you could use it for <code>std::vector::iterator</code> or
- // usual pointers as well.
- //
- // The function returns a vector of pairs of iterators, where the first
- // denotes the first cell of each chunk, while the second denotes the one
- // past the last (this half-open interval is the usual convention in the
- // C++ standard library, so we keep to it). Note that we have to specify
- // the actual data type of the iterators in angle brackets to the
- // function. This is necessary, since it is a template function which
- // takes the data type of the iterators as template argument; in the
- // present case, however, the data types of the two first parameters
- // differ (<code>begin_active</code> returns an
- // <code>active_iterator</code>, while <code>end</code> returns a
- // <code>raw_iterator</code>), and in this case the C++ language requires
- // us to specify the template type explicitly. For brevity, we first
- // typedef this data type to an alias.
-
- typedef typename DoFHandler<dim>::active_cell_iterator active_cell_iterator;
-
- // Finally, for each of the chunks of iterators we have computed, start
- // one thread (or if not in multithread mode: execute assembly on these
- // chunks sequentially). This is done using the following sequence of
- // function calls:
WorkStream::run(dof_handler.begin_active(),
dof_handler.end(),
*this,
&AdvectionProblem::local_assemble_system,
&AdvectionProblem::copy_local_to_global,
- AssemblyScratchData(),
+ AssemblyScratchData(fe),
AssemblyCopyData());
- // The reasons and internal workings of these functions can be found in
- // the report on the subject of multithreading, which is available online
- // as well. Suffice it to say that we create a new thread that calls the
- // <code>assemble_system_interval</code> function on the present object
- // (the <code>this</code> pointer), with the arguments following in the
- // second set of parentheses passed as parameters. The Threads::new_thread
- // function returns an object of type Threads::Thread, which we put into
- // the <code>threads</code> container. If a thread exits, the return value
- // of the function being called is put into a place such that the thread
- // objects can access it using their <code>return_value</code> function;
- // since the function we call doesn't have a return value, this does not
- // apply here. Note that you can copy around thread objects freely, and
- // that of course they will still represent the same thread.
-
- // When all the threads are running, the only thing we have to do is wait
- // for them to finish. This is necessary of course, as we can't proceed
- // with our tasks before the matrix and right hand side are
- // assembled. Waiting for all the threads to finish can be done using the
- // <code>join_all</code> function in the <code>ThreadGroup</code>
- // container, which just calls <code>join</code> on each of the thread
- // objects it stores.
- //
- // Again, if the library was not configured to use multithreading, then
- // no threads can run in parallel and the function returns immediately.
-
-
// After the matrix has been assembled in parallel, we still have to
// eliminate hanging node constraints. This is something that can't be
// done on each of the threads separately, so we have to do it now.
- hanging_node_constraints.condense (system_matrix);
- hanging_node_constraints.condense (system_rhs);
// Note also, that unlike in previous examples, there are no boundary
// conditions to be applied to the system of equations. This, of course,
// is due to the fact that we have included them into the weak formulation
// of the problem.
+ hanging_node_constraints.condense (system_matrix);
+ hanging_node_constraints.condense (system_rhs);
}
+ // As already mentioned above, we need to have scratch objects for
+ // the parallel computation of local contributions. These objects
+ // contain FEValues and FEFaceValues objects, and so we will need to
+ // have constructors and copy constructors that allow us to create
+ // them. In initializing them, note first that we use bilinear
+ // elements, soGauss formulae with two points in each space
+ // direction are sufficient. For the cell terms we need the values
+ // and gradients of the shape functions, the quadrature points in
+ // order to determine the source density and the advection field at
+ // a given point, and the weights of the quadrature points times the
+ // determinant of the Jacobian at these points. In contrast, for the
+ // boundary integrals, we don't need the gradients, but rather the
+ // normal vectors to the cells. This determines which update flags
+ // we will have to pass to the constructors of the members of the
+ // class:
+ template <int dim>
+ AdvectionProblem<dim>::AssemblyScratchData::
+ AssemblyScratchData (const FiniteElement<dim> &fe)
+ :
+ fe_values (fe,
+ QGauss<dim>(2),
+ update_values | update_gradients |
+ update_quadrature_points | update_JxW_values),
+ fe_face_values (fe,
+ QGauss<dim-1>(2),
+ update_values | update_quadrature_points |
+ update_JxW_values | update_normal_vectors)
+ {}
+
+
+
+ template <int dim>
+ AdvectionProblem<dim>::AssemblyScratchData::
+ AssemblyScratchData (const AssemblyScratchData &scratch_data)
+ :
+ fe_values (scratch_data.fe_values.get_fe(),
+ scratch_data.fe_values.get_quadrature(),
+ update_values | update_gradients |
+ update_quadrature_points | update_JxW_values),
+ fe_face_values (scratch_data.fe_face_values.get_fe(),
+ scratch_data.fe_face_values.get_quadrature(),
+ update_values | update_quadrature_points |
+ update_JxW_values | update_normal_vectors)
+ {}
+
+
+
+
// Now, this is the function that does the actual work. It is not very
// different from the <code>assemble_system</code> functions of previous
// example programs, so we will again only comment on the differences. The
// mathematical stuff follows closely what we have said in the introduction.
+ //
+ // There are a number of points worth mentioning here, though. The
+ // first one is that we have moved the FEValues and FEFaceValues
+ // objects into the ScratchData object. We have done so because the
+ // alternative would have been to simply create one every time we
+ // get into this function -- i.e., on every cell. It now turns out
+ // that the FEValues classes were written with the explicit goal of
+ // moving everything that remains the same from cell to cell into
+ // the construction of the object, and only do as little work as
+ // possible in FEValues::reinit() whenever we move to a new
+ // cell. What this means is that it would be very expensive to
+ // create a new object of this kind in this function as we would
+ // have to do it for every cell -- exactly the thing we wanted to
+ // avoid with the FEValues class. Instead, what we do is create it
+ // only once (or a small number of times) in the scratch objects and
+ // then re-use it as often as we can.
+ //
+ // This begs the question of whether there are other objects we
+ // create in this function whose creation is expensive compared to
+ // its use. Indeed, at the top of the function, we declare all sorts
+ // of objects. The <code>AdvectionField</code>,
+ // <code>RightHandSide</code> and <code>BoundaryValues</code> do not
+ // cost much to create, so there is no harm here. However,
+ // allocating memory in creating the <code>rhs_values</code> and
+ // similar variables below typically costs a significant amount of
+ // time, compared to just accessing the (temporary) values we store
+ // in them. Consequently, these would be candidates for moving into
+ // the <code>AssemblyScratchData</code> class. We will leave this as
+ // an exercise.
template <int dim>
void
AdvectionProblem<dim>::
local_assemble_system (const typename DoFHandler<dim>::active_cell_iterator &cell,
- AssemblyScratchData &scratch,
+ AssemblyScratchData &scratch_data,
AssemblyCopyData ©_data)
{
// First of all, we will need some objects that describe boundary values,
const RightHandSide<dim> right_hand_side;
const BoundaryValues<dim> boundary_values;
- // Next we need quadrature formula for the cell terms, but also for the
- // integral over the inflow boundary, which will be a face integral. As we
- // use bilinear elements, Gauss formulae with two points in each space
- // direction are sufficient.
- QGauss<dim> quadrature_formula(2);
- QGauss<dim-1> face_quadrature_formula(2);
-
- // Finally, we need objects of type <code>FEValues</code> and
- // <code>FEFaceValues</code>. For the cell terms we need the values and
- // gradients of the shape functions, the quadrature points in order to
- // determine the source density and the advection field at a given point,
- // and the weights of the quadrature points times the determinant of the
- // Jacobian at these points. In contrast, for the boundary integrals, we
- // don't need the gradients, but rather the normal vectors to the cells.
- FEValues<dim> fe_values (fe, quadrature_formula,
- update_values | update_gradients |
- update_quadrature_points | update_JxW_values);
- FEFaceValues<dim> fe_face_values (fe, face_quadrature_formula,
- update_values | update_quadrature_points |
- update_JxW_values | update_normal_vectors);
-
// Then we define some abbreviations to avoid unnecessarily long lines:
const unsigned int dofs_per_cell = fe.dofs_per_cell;
- const unsigned int n_q_points = quadrature_formula.size();
- const unsigned int n_face_q_points = face_quadrature_formula.size();
+ const unsigned int n_q_points = scratch_data.fe_values.get_quadrature().size();
+ const unsigned int n_face_q_points = scratch_data.fe_face_values.get_quadrature().size();
// We declare cell matrix and cell right hand side...
- copy_data.cell_matrix = FullMatrix<double> (dofs_per_cell, dofs_per_cell);
- copy_data.cell_rhs = Vector<double> (dofs_per_cell);
+ copy_data.cell_matrix.reinit (dofs_per_cell, dofs_per_cell);
+ copy_data.cell_rhs.reinit (dofs_per_cell);
// ... an array to hold the global indices of the degrees of freedom of
// the cell on which we are presently working...
// ... then initialize the <code>FEValues</code> object...
- fe_values.reinit (cell);
+ scratch_data.fe_values.reinit (cell);
// ... obtain the values of right hand side and advection directions
// at the quadrature points...
- advection_field.value_list (fe_values.get_quadrature_points(),
+ advection_field.value_list (scratch_data.fe_values.get_quadrature_points(),
advection_directions);
- right_hand_side.value_list (fe_values.get_quadrature_points(),
+ right_hand_side.value_list (scratch_data.fe_values.get_quadrature_points(),
rhs_values);
// ... set the value of the streamline diffusion parameter as
{
for (unsigned int j=0; j<dofs_per_cell; ++j)
copy_data.cell_matrix(i,j) += ((advection_directions[q_point] *
- fe_values.shape_grad(j,q_point) *
- (fe_values.shape_value(i,q_point) +
+ scratch_data.fe_values.shape_grad(j,q_point) *
+ (scratch_data.fe_values.shape_value(i,q_point) +
delta *
(advection_directions[q_point] *
- fe_values.shape_grad(i,q_point)))) *
- fe_values.JxW(q_point));
+ scratch_data.fe_values.shape_grad(i,q_point)))) *
+ scratch_data.fe_values.JxW(q_point));
- copy_data.cell_rhs(i) += ((fe_values.shape_value(i,q_point) +
+ copy_data.cell_rhs(i) += ((scratch_data.fe_values.shape_value(i,q_point) +
delta *
(advection_directions[q_point] *
- fe_values.shape_grad(i,q_point)) ) *
+ scratch_data.fe_values.shape_grad(i,q_point)) ) *
rhs_values[q_point] *
- fe_values.JxW (q_point));
+ scratch_data.fe_values.JxW (q_point));
}
- // Besides the cell terms which we have build up now, the bilinear
+ // Besides the cell terms which we have built up now, the bilinear
// form of the present problem also contains terms on the boundary of
// the domain. Therefore, we have to check whether any of the faces of
// this cell are on the boundary of the domain, and if so assemble the
// domain. Just as for the usual FEValues object which we have
// used in previous examples and also above, we have to
// reinitialize the FEFaceValues object for the present face:
- fe_face_values.reinit (cell, face);
+ scratch_data.fe_face_values.reinit (cell, face);
// For the quadrature points at hand, we ask for the values of
// the inflow function and for the direction of flow:
- boundary_values.value_list (fe_face_values.get_quadrature_points(),
+ boundary_values.value_list (scratch_data.fe_face_values.get_quadrature_points(),
face_boundary_values);
- advection_field.value_list (fe_face_values.get_quadrature_points(),
+ advection_field.value_list (scratch_data.fe_face_values.get_quadrature_points(),
face_advection_directions);
// Now loop over all quadrature points and see whether it is on
// so if the advection direction points into the domain, its
// scalar product with the normal vector must be negative):
for (unsigned int q_point=0; q_point<n_face_q_points; ++q_point)
- if (fe_face_values.normal_vector(q_point) *
+ if (scratch_data.fe_face_values.normal_vector(q_point) *
face_advection_directions[q_point]
< 0)
// If the is part of the inflow boundary, then compute the
{
for (unsigned int j=0; j<dofs_per_cell; ++j)
copy_data.cell_matrix(i,j) -= (face_advection_directions[q_point] *
- fe_face_values.normal_vector(q_point) *
- fe_face_values.shape_value(i,q_point) *
- fe_face_values.shape_value(j,q_point) *
- fe_face_values.JxW(q_point));
+ scratch_data.fe_face_values.normal_vector(q_point) *
+ scratch_data.fe_face_values.shape_value(i,q_point) *
+ scratch_data.fe_face_values.shape_value(j,q_point) *
+ scratch_data.fe_face_values.JxW(q_point));
copy_data.cell_rhs(i) -= (face_advection_directions[q_point] *
- fe_face_values.normal_vector(q_point) *
+ scratch_data.fe_face_values.normal_vector(q_point) *
face_boundary_values[q_point] *
- fe_face_values.shape_value(i,q_point) *
- fe_face_values.JxW(q_point));
- };
- };
+ scratch_data.fe_face_values.shape_value(i,q_point) *
+ scratch_data.fe_face_values.JxW(q_point));
+ }
+ }
// Now go on by transferring the local contributions to the system of
+ // The second function we needed to write was the one that copies
+ // the local contributions the previous function has computed and
+ // put into the copy data object, into the global matrix and right
+ // hand side vector objects. This is essentially what we always had
+ // as the last block of code when assembling something on every
+ // cell. The following should therefore be pretty obvious:
template <int dim>
void
AdvectionProblem<dim>::copy_local_to_global (const AssemblyCopyData ©_data)
{
- // Up until now we have not taken care of the fact that this function
- // might run more than once in parallel, as the operations above only
- // work on variables that are local to this function, or if they are
- // global (such as the information on the grid, the DoF handler, or
- // the DoF numbers) they are only read. Thus, the different threads do
- // not disturb each other.
- //
- // On the other hand, we would now like to write the local
- // contributions to the global system of equations into the global
- // objects. This needs some kind of synchronization, as if we would
- // not take care of the fact that multiple threads write into the
- // matrix at the same time, we might be surprised that one threads
- // reads data from the matrix that another thread is presently
- // overwriting, or similar things. Thus, to make sure that only one
- // thread operates on these objects at a time, we have to lock
- // it. This is done using a <code>Mutex</code>, which is short for
- // <code>mutually exclusive</code>: a thread that wants to write to
- // the global objects acquires this lock, but has to wait if it is
- // presently owned by another thread. If it has acquired the lock, it
- // can be sure that no other thread is presently writing to the
- // matrix, and can do so freely. When finished, we release the lock
- // again so as to allow other threads to acquire it and write to the
- // matrix.
for (unsigned int i=0; i<copy_data.local_dof_indices.size(); ++i)
{
for (unsigned int j=0; j<copy_data.local_dof_indices.size(); ++j)
system_rhs(copy_data.local_dof_indices[i]) += copy_data.cell_rhs(i);
}
- // At this point, the locked operations on the global matrix are done,
- // i.e. other threads can now enter into the protected section by
- // acquiring the lock. Two final notes are in place here, however:
- //
- // 1. If the library was not configured for multithreading, then
- // there can't be parallel threads and there is no need to
- // synchronize. Thus, the <code>lock</code> and <code>release</code>
- // functions are no-ops, i.e. they return without doing anything.
- //
- // 2. In order to work properly, it is essential that all threads try
- // to acquire the same lock. This, of course, can not be achieved if
- // the lock is a local variable, as then each thread would acquire its
- // own lock. Therefore, the lock variable is a member variable of the
- // class; since all threads execute member functions of the same
- // object, they have the same <code>this</code> pointer and therefore
- // also operate on the same <code>lock</code>.
}
else
{
refine_grid ();
- };
+ }
std::cout << " Number of active cells: "
assemble_system ();
solve ();
output_results (cycle);
- };
+ }
DataOut<dim> data_out;
data_out.attach_dof_handler (dof_handler);
for (unsigned int subface_no=0; subface_no<face->n_children(); ++subface_no)
active_neighbors.push_back (
cell->neighbor_child_on_subface(face_no, subface_no));
- };
- };
+ }
+ }
// OK, now that we have all the neighbors, lets start the computation
// on each of them. First we do some preliminaries: find out about the
this_midpoint_value[0]) /
distance *
y;
- };
+ }
// If now, after collecting all the information from the neighbors, we
// can determine an approximation of the gradient for the present
*error_on_this_cell = (std::pow(cell->diameter(),
1+1.0*dim/2) *
std::sqrt(gradient.square()));
- };
+ }
}
}
<< "----------------------------------------------------"
<< std::endl;
return 1;
- };
+ }
return 0;
}
projects / dealii-svn.git / commitdiff