Reference documentation for deal.II version Git f15f581df6 20200710 15:30:09 0400

#include <deal.II/base/partitioner.h>
Public Member Functions  
Partitioner ()  
Partitioner (const unsigned int size)  
Partitioner (const IndexSet &locally_owned_indices, const IndexSet &ghost_indices_in, const MPI_Comm communicator_in)  
Partitioner (const IndexSet &locally_owned_indices, const MPI_Comm communicator_in)  
virtual void  reinit (const IndexSet &vector_space_vector_index_set, const IndexSet &read_write_vector_index_set, const MPI_Comm &communicator) override 
void  set_owned_indices (const IndexSet &locally_owned_indices) 
void  set_ghost_indices (const IndexSet &ghost_indices, const IndexSet &larger_ghost_index_set=IndexSet()) 
types::global_dof_index  size () const 
unsigned int  local_size () const 
const IndexSet &  locally_owned_range () const 
std::pair< types::global_dof_index, types::global_dof_index >  local_range () const 
bool  in_local_range (const types::global_dof_index global_index) const 
unsigned int  global_to_local (const types::global_dof_index global_index) const 
types::global_dof_index  local_to_global (const unsigned int local_index) const 
bool  is_ghost_entry (const types::global_dof_index global_index) const 
const IndexSet &  ghost_indices () const 
unsigned int  n_ghost_indices () const 
const std::vector< std::pair< unsigned int, unsigned int > > &  ghost_indices_within_larger_ghost_set () const 
const std::vector< std::pair< unsigned int, unsigned int > > &  ghost_targets () const 
const std::vector< std::pair< unsigned int, unsigned int > > &  import_indices () const 
unsigned int  n_import_indices () const 
const std::vector< std::pair< unsigned int, unsigned int > > &  import_targets () const 
bool  is_compatible (const Partitioner &part) const 
bool  is_globally_compatible (const Partitioner &part) const 
unsigned int  this_mpi_process () const 
unsigned int  n_mpi_processes () const 
virtual const MPI_Comm &  get_mpi_communicator () const override 
bool  ghost_indices_initialized () const 
template<typename Number , typename MemorySpaceType = MemorySpace::Host>  
void  export_to_ghosted_array_start (const unsigned int communication_channel, const ArrayView< const Number, MemorySpaceType > &locally_owned_array, const ArrayView< Number, MemorySpaceType > &temporary_storage, const ArrayView< Number, MemorySpaceType > &ghost_array, std::vector< MPI_Request > &requests) const 
template<typename Number , typename MemorySpaceType = MemorySpace::Host>  
void  export_to_ghosted_array_finish (const ArrayView< Number, MemorySpaceType > &ghost_array, std::vector< MPI_Request > &requests) const 
template<typename Number , typename MemorySpaceType = MemorySpace::Host>  
void  import_from_ghosted_array_start (const VectorOperation::values vector_operation, const unsigned int communication_channel, const ArrayView< Number, MemorySpaceType > &ghost_array, const ArrayView< Number, MemorySpaceType > &temporary_storage, std::vector< MPI_Request > &requests) const 
template<typename Number , typename MemorySpaceType = MemorySpace::Host>  
void  import_from_ghosted_array_finish (const VectorOperation::values vector_operation, const ArrayView< const Number, MemorySpaceType > &temporary_storage, const ArrayView< Number, MemorySpaceType > &locally_owned_storage, const ArrayView< Number, MemorySpaceType > &ghost_array, std::vector< MPI_Request > &requests) const 
std::size_t  memory_consumption () const 
Public Member Functions inherited from LinearAlgebra::CommunicationPatternBase  
virtual  ~CommunicationPatternBase ()=default 
Static Public Member Functions  
static ::ExceptionBase &  ExcIndexNotPresent (types::global_dof_index arg1, unsigned int arg2) 
static ::ExceptionBase &  ExcGhostIndexArrayHasWrongSize (unsigned int arg1, unsigned int arg2, unsigned int arg3) 
Private Member Functions  
void  initialize_import_indices_plain_dev () const 
Private Attributes  
types::global_dof_index  global_size 
IndexSet  locally_owned_range_data 
std::pair< types::global_dof_index, types::global_dof_index >  local_range_data 
IndexSet  ghost_indices_data 
unsigned int  n_ghost_indices_data 
std::vector< std::pair< unsigned int, unsigned int > >  ghost_targets_data 
std::vector< std::pair< unsigned int, unsigned int > >  import_indices_data 
std::vector< std::pair< std::unique_ptr< unsigned int[], void(*)(unsigned int *)>, unsigned int > >  import_indices_plain_dev 
unsigned int  n_import_indices_data 
std::vector< std::pair< unsigned int, unsigned int > >  import_targets_data 
std::vector< unsigned int >  import_indices_chunks_by_rank_data 
unsigned int  n_ghost_indices_in_larger_set 
std::vector< unsigned int >  ghost_indices_subset_chunks_by_rank_data 
std::vector< std::pair< unsigned int, unsigned int > >  ghost_indices_subset_data 
unsigned int  my_pid 
unsigned int  n_procs 
MPI_Comm  communicator 
bool  have_ghost_indices 
This class defines a model for the partitioning of a vector (or, in fact, any linear data structure) among processors using MPI.
The partitioner stores the global vector size and the locally owned range as a halfopen interval [lower
, upper
) on each process. Furthermore, it includes a structure for the pointtopoint communication patterns. It allows the inclusion of ghost indices (i.e. indices that a current processor needs to have access to, but are owned by another process) through an IndexSet. In addition, it also stores the other processors' ghost indices belonging to the current processor (see import_targets()), which are the indices where other processors might require information from. In a sense, these import indices form the dual of the ghost indices. This information is gathered once when constructing the partitioner, which obviates subsequent global communication steps when exchanging data.
The figure below gives an example of index space \([0,74)\) being split into four parts that are each owned by one MPI process:
The first row (above the thick black line) shows which process owns which elements. Below it, the next four lines indicate which elements of the overall array each processor wants to know about – this is generally a superset of the locally owned elements, with the difference being what are called "ghost elements".
To understand the remaining pieces of the figure (and this class), remember that in MPI, you can't just ask another process for data. (That's not quite true: There are mechanisms in newer MPI standards for this, but as a general rule it's true.) Rather, if you need information, you need to send another process a message, the other process needs to expect the message and respond as appropriate with a message of its own. In practice, it is therefore easier and faster if each process will already know what it will be asked and, at the appropriate time, just send that data. The remaining lines of information set up this kind of scheme.
To this end, note that process 0 will want to know about five elements it does not own itself: those with indices 20, 21 (owned by process 1); and 40, 41, 43 (owned by process 2). Similar information can be obtained from the following lines. To satisfy this need for knowledge, it would therefore be quite useful if process 1 stored that, at the appropriate time, it will have to send elements 20, 21 to process 0. Similarly, if you go through lines 24 below the thick black line, you will see that process 0 should know that it will need to send elements 1, 2, 13, 18, 19 to process 1; 18, 19 to process 2; and elements 1, 2, 13 to process 3. These are called "import indices" because other processes will want to import them. Instead of storing these indices as a set, it is often useful to use halfopen index sets instead, and so the import indices listed above form the following collection of sets: [1,3)
, [13,14)
, [18,20)
, [18,20)
, [1,3)
, [13,14)
. This is how the import indices are shown in the figure above. We now only have to know which of these halfopen sets are to be sent to whom. This is done in the line above it where we list the "import targets" (i.e., the target processes for an import operations): Process 1 will receive 5 elements (which are comprised of the first three halfopen target index sets), process 2 will receive 2 indices (the fourth halfopen interval), and process 3 will receive 3 indices (the remaining two halfopen intervals). This information is encoded as the pairs {1,5}
, {2,2}
, {3,3}
as the import targets. Similar considerations can be made for what processes 1, 2, and 3 will have to send out.
Finally, when receiving information, it is useful to know how many indices each process will receive from whom since then one can already preallocate buffers of the right size. This is listed in the last line under "ghost targets": Process 0 will receive two elements from process 1 (namely those with indices 20, 21), and three from process 2 (namely those with indices 40, 41, 43). This is encoded as pairs {1,2}
and {2,3}
. Again, similar considerations can be made for what processes 1, 2, and 3 should expect, and what is then shown in their respective columns.
The main purpose of this class is to set up these data structures knowing only which process owns which elements, and for which additional ghost elements each process needs knowledge.
The partitioner includes a mechanism for converting global to local and local to global indices. Internally, this class stores vector elements using the convention as follows: The local range is associated with local indices [0,local_size
), and ghost indices are stored consecutively in [local_size
, local_size
+ n_ghost_indices
). The ghost indices are sorted according to their global index.
This class also handles the ghost data exchange for partitioned arrays of objects – i.e., if a separate class stores the locally owned elements on each process, then this class facilitates the importation of those elements that are locally needed as ghosts but stored elsewhere. An example of where this class is used is the LinearAlgebra::distributed::Vector class.
The data exchange happens through four functions:
The MPI communication routines are pointtopoint communication patterns.
This partitioner class operates on a fixed set of ghost indices and must always be compatible with the ghost indices inside the array whose partitioning it represents. In some cases, one only wants to send around some of the ghost indices present in a vector, but without creating a copy of the vector with a suitable index set  think e.g. of local time stepping where different regions of a vector might be exchanged at different stages of a time step slice. This class supports that case by the following model: A vector is first created with the full ghosted index set. Then, a second Partitioner instance is created that sets ghost indices with a tighter index set as ghosts, but specifying the larger index set as the second argument to the set_ghost_indices() call. When data is exchanged, the export_to_ghosted_array_start() and import_from_ghosted_array_start() detect this case and only send the selected indices, taken from the full array of ghost entries.
Definition at line 193 of file partitioner.h.
Utilities::MPI::Partitioner::Partitioner  (  ) 
Default constructor.
Definition at line 26 of file partitioner.cc.
Utilities::MPI::Partitioner::Partitioner  (  const unsigned int  size  ) 
Constructor with size argument. Creates an MPI_COMM_SELF structure where there is no real parallel layout.
Definition at line 41 of file partitioner.cc.
Utilities::MPI::Partitioner::Partitioner  (  const IndexSet &  locally_owned_indices, 
const IndexSet &  ghost_indices_in,  
const MPI_Comm  communicator_in  
) 
Constructor with index set arguments. This constructor creates a distributed layout based on a given communicators, an IndexSet describing the locally owned range and another one for describing ghost indices that are owned by other processors, but that we need to have read or write access to.
Definition at line 61 of file partitioner.cc.
Utilities::MPI::Partitioner::Partitioner  (  const IndexSet &  locally_owned_indices, 
const MPI_Comm  communicator_in  
) 
Constructor with one index set argument. This constructor creates a distributed layout based on a given communicator, and an IndexSet describing the locally owned range. It allows to set the ghost indices at a later time. Apart from this, it is similar to the other constructor with two index sets.
Definition at line 80 of file partitioner.cc.

overridevirtual 
Reinitialize the communication pattern. The first argument vector_space_vector_index_set
is the index set associated to a VectorSpaceVector object. The second argument read_write_vector_index_set
is the index set associated to a ReadWriteVector object.
Implements LinearAlgebra::CommunicationPatternBase.
Definition at line 98 of file partitioner.cc.
void Utilities::MPI::Partitioner::set_owned_indices  (  const IndexSet &  locally_owned_indices  ) 
Set the locally owned indices. Used in the constructor.
Definition at line 111 of file partitioner.cc.
void Utilities::MPI::Partitioner::set_ghost_indices  (  const IndexSet &  ghost_indices, 
const IndexSet &  larger_ghost_index_set = IndexSet() 

) 
Set the ghost indices after the constructor has been called.
The optional parameter larger_ghost_index_set
allows defining an indirect addressing into a larger set of ghost indices. This setup is useful if a distributed vector is based on that larger ghost index set but only a tighter subset should be communicated according to ghost_indices
.
Definition at line 152 of file partitioner.cc.
types::global_dof_index Utilities::MPI::Partitioner::size  (  )  const 
Return the global size.
unsigned int Utilities::MPI::Partitioner::local_size  (  )  const 
Return the local size, i.e. local_range().second minus local_range().first.
const IndexSet& Utilities::MPI::Partitioner::locally_owned_range  (  )  const 
Return an IndexSet representation of the local range. This class only supports contiguous local ranges, so the IndexSet actually only consists of one single range of data, and is equivalent to the result of local_range().
std::pair<types::global_dof_index, types::global_dof_index> Utilities::MPI::Partitioner::local_range  (  )  const 
Return the local range. The returned pair consists of the index of the first element and the index of the element one past the last locally owned one.
bool Utilities::MPI::Partitioner::in_local_range  (  const types::global_dof_index  global_index  )  const 
Return true if the given global index is in the local range of this processor.
unsigned int Utilities::MPI::Partitioner::global_to_local  (  const types::global_dof_index  global_index  )  const 
Return the local index corresponding to the given global index. If the given global index is neither locally owned nor a ghost, an exception is thrown.
Note that the returned local index for locally owned indices will be between 0 and local_size()1
, and the local index for ghosts is between local_size()
and local_size()+n_ghost_indices()1
.
types::global_dof_index Utilities::MPI::Partitioner::local_to_global  (  const unsigned int  local_index  )  const 
Return the global index corresponding to the given local index.
Note that the local index for locally owned indices must be between 0 and local_size()1
, and the local index for ghosts must be between local_size()
and local_size()+n_ghost_indices()1
.
bool Utilities::MPI::Partitioner::is_ghost_entry  (  const types::global_dof_index  global_index  )  const 
Return whether the given global index is a ghost index on the present processor. Returns false for indices that are owned locally and for indices not present at all.
const IndexSet& Utilities::MPI::Partitioner::ghost_indices  (  )  const 
Return an IndexSet representation of all ghost indices.
unsigned int Utilities::MPI::Partitioner::n_ghost_indices  (  )  const 
Return the number of ghost indices. Same as ghost_indices().n_elements(), but cached for simpler access.
const std::vector<std::pair<unsigned int, unsigned int> >& Utilities::MPI::Partitioner::ghost_indices_within_larger_ghost_set  (  )  const 
In case the partitioner was built to define ghost indices as a subset of indices in a larger set of ghosts, this function returns the numbering in terms of ranges within that set. Similar structure as in an IndexSet, but tailored to be iterated over.
In case the partitioner did not take a second set of ghost indices into account, this subset is simply defined as the halfopen interval [0, n_ghost_indices())
.
const std::vector<std::pair<unsigned int, unsigned int> >& Utilities::MPI::Partitioner::ghost_targets  (  )  const 
Return a list of processors (first entry) and the number of ghost degrees of freedom owned by that processor (second entry). The sum of the latter over all processors equals n_ghost_indices().
const std::vector<std::pair<unsigned int, unsigned int> >& Utilities::MPI::Partitioner::import_indices  (  )  const 
Return a vector of ranges of local indices that we are importing during compress(), i.e., others' ghosts that belong to the local range. Similar structure as in an IndexSet, but tailored to be iterated over, and some indices may be duplicated. The returned pairs consists of the index of the first element and the index of the element one past the last one in a range.
unsigned int Utilities::MPI::Partitioner::n_import_indices  (  )  const 
Number of import indices, i.e., indices that are ghosts on other processors and we will receive data from.
const std::vector<std::pair<unsigned int, unsigned int> >& Utilities::MPI::Partitioner::import_targets  (  )  const 
Return a list of processors (first entry) and the number of degrees of freedom imported from it during compress() operation (second entry) for all the processors that data is obtained from, i.e., locally owned indices that are ghosts on other processors.
bool Utilities::MPI::Partitioner::is_compatible  (  const Partitioner &  part  )  const 
Check whether the given partitioner is compatible with the current partitioner. Two partitioners are compatible if they have the same local sizes and the same ghost indices. They do not necessarily need to correspond to the same data that is stored based on these partioner objects. This is a local operation only, i.e., if only some processors decide that the partitioning is not compatible, only these processors will return false
, whereas the other processors will return true
.
Definition at line 435 of file partitioner.cc.
bool Utilities::MPI::Partitioner::is_globally_compatible  (  const Partitioner &  part  )  const 
Check whether the given partitioner is compatible with the current partitioner. Two partitioners are compatible if they have the same local size and the same ghost indices. They do not necessarily need to correspond to the same data that is stored based on these partioner objects. As opposed to is_compatible(), this method checks for compatibility among all processors and the method only returns true
if the partitioner is the same on all processors. In other words, it does a global "and" operation over the results returned by is_compatible() on all involved processes.
This method performs global communication, so make sure to use it only in a context where all processors call it the same number of times.
Definition at line 462 of file partitioner.cc.
unsigned int Utilities::MPI::Partitioner::this_mpi_process  (  )  const 
Return the MPI ID of the calling processor. Cached to have simple access.
unsigned int Utilities::MPI::Partitioner::n_mpi_processes  (  )  const 
Return the total number of MPI processor participating in the given partitioner. Cached to have simple access.

overridevirtual 
Return the MPI communicator underlying the partitioner object.
Implements LinearAlgebra::CommunicationPatternBase.
bool Utilities::MPI::Partitioner::ghost_indices_initialized  (  )  const 
Return whether ghost indices have been explicitly added as a ghost_indices
argument. Only true if a reinit() call or constructor provided that argument.
void Utilities::MPI::Partitioner::export_to_ghosted_array_start  (  const unsigned int  communication_channel, 
const ArrayView< const Number, MemorySpaceType > &  locally_owned_array,  
const ArrayView< Number, MemorySpaceType > &  temporary_storage,  
const ArrayView< Number, MemorySpaceType > &  ghost_array,  
std::vector< MPI_Request > &  requests  
)  const 
Start the exportation of the data in a locally owned array to the range described by the ghost indices of this class.
communication_channel  Sets an offset to the MPI_Isend and MPI_Irecv calls that avoids interference with other ongoing export_to_ghosted_array_start() calls on different entries. Typically handled within the blocks of a block vector. Any value less than 200 is a valid value. 
locally_owned_array  The array of data from which the data is extracted and sent to the ghost entries on a remote processor. 
temporary_storage  A temporary storage array of length n_import_indices() that is used to hold the packed data from the locally_owned_array to be sent. Note that this array must not be touched until the respective export_to_ghosted_array_finish() call has been made because the model uses nonblocking communication. 
ghost_array  The array that will receive the exported data, i.e., the entries that a remote processor sent to the calling process. Its size must either be n_ghost_indices() or equal the number of ghost indices in the larger index set that was given as second argument to set_ghost_indices(). In case only selected indices are sent, no guarantee is made regarding the entries that do not get set. Some of them might be used to organize the transfer and later reset to zero, so make sure you do not use them in computations. 
requests  The list of MPI requests for the ongoing nonblocking communication that will be finalized in the export_to_ghosted_array_finish() call. 
This functionality is used in LinearAlgebra::distributed::Vector::update_ghost_values().
void Utilities::MPI::Partitioner::export_to_ghosted_array_finish  (  const ArrayView< Number, MemorySpaceType > &  ghost_array, 
std::vector< MPI_Request > &  requests  
)  const 
Finish the exportation of the data in a locally owned array to the range described by the ghost indices of this class.
ghost_array  The array that will receive the exported data started in the export_to_ghosted_array_start() . This must be the same array as passed to that function, otherwise the behavior is undefined. 
requests  The list of MPI requests for the ongoing nonblocking communication that were started in the export_to_ghosted_array_start() call. This must be the same array as passed to that function, otherwise MPI will likely throw an error. 
This functionality is used in LinearAlgebra::distributed::Vector::update_ghost_values().
void Utilities::MPI::Partitioner::import_from_ghosted_array_start  (  const VectorOperation::values  vector_operation, 
const unsigned int  communication_channel,  
const ArrayView< Number, MemorySpaceType > &  ghost_array,  
const ArrayView< Number, MemorySpaceType > &  temporary_storage,  
std::vector< MPI_Request > &  requests  
)  const 
Start importing the data on an array indexed by the ghost indices of this class that is later accumulated into a locally owned array with import_from_ghosted_array_finish().
vector_operation  Defines how the data sent to the owner should be combined with the existing entries, e.g., added into. 
communication_channel  Sets an offset to the MPI_Isend and MPI_Irecv calls that avoids interference with other ongoing import_from_ghosted_array_start() calls on different entries. Typically handled within the blocks of a block vector. Any value less than 200 is a valid value. 
ghost_array  The array of ghost data that is sent to a remote owner of the respective index in a vector. Its size must either be n_ghost_indices() or equal the number of ghost indices in the larger index set that was given as second argument to set_ghost_indices(). This or the subsequent import_from_ghosted_array_finish() function, the order is implementationdependent, will set all data entries behind ghost_array to zero. 
temporary_storage  A temporary storage array of length n_import_indices() that is used to hold the packed data from MPI communication that will later be written into the locally owned array. Note that this array must not be touched until the respective import_from_ghosted_array_finish() call has been made because the model uses nonblocking communication. 
requests  The list of MPI requests for the ongoing nonblocking communication that will be finalized in the export_to_ghosted_array_finish() call. 
This functionality is used in LinearAlgebra::distributed::Vector::compress().
void Utilities::MPI::Partitioner::import_from_ghosted_array_finish  (  const VectorOperation::values  vector_operation, 
const ArrayView< const Number, MemorySpaceType > &  temporary_storage,  
const ArrayView< Number, MemorySpaceType > &  locally_owned_storage,  
const ArrayView< Number, MemorySpaceType > &  ghost_array,  
std::vector< MPI_Request > &  requests  
)  const 
Finish importing the data from an array indexed by the ghost indices of this class into a specified locally owned array, combining the results according to the given input vector_operation
.
vector_operation  Defines how the data sent to the owner should be combined with the existing entries, e.g., added into. 
temporary_storage  The same array given to the import_from_ghosted_array_start() call that contains the packed data from MPI communication. In thus function, it is combined at the corresponding entries described by the ghost relations according to vector_operation . 
ghost_array  The array of ghost data that is sent to a remote owner of the respective index in a vector. Its size must either be n_ghost_indices() or equal the number of ghost indices in the larger index set that was given as second argument to set_ghost_indices(). This function will set all data entries behind ghost_array to zero for the implementationdependent cases when it was not already done in the import_from_ghosted_array_start() call. 
locally_owned_storage  The array of data where the resulting data sent by remote processes to the calling process will be accumulated into. 
requests  The list of MPI requests for the ongoing nonblocking communication that have been initiated in the import_to_ghosted_array_finish() call. This must be the same array as passed to that function, otherwise MPI will likely throw an error. 
This functionality is used in LinearAlgebra::distributed::Vector::compress().
std::size_t Utilities::MPI::Partitioner::memory_consumption  (  )  const 
Compute the memory consumption of this structure.
Definition at line 471 of file partitioner.cc.

private 
Initialize import_indices_plain_dev from import_indices_data. This function is only used when using CUDAaware MPI.

private 
The global size of the vector over all processors
Definition at line 650 of file partitioner.h.

private 
The range of the vector that is stored locally.
Definition at line 655 of file partitioner.h.

private 
The range of the vector that is stored locally. Extracted from locally_owned_range for performance reasons.
Definition at line 662 of file partitioner.h.

private 
The set of indices to which we need to have read access but that are not locally owned
Definition at line 668 of file partitioner.h.

private 
A variable caching the number of ghost indices. It would be expensive to use ghost_indices.n_elements()
to compute this.
Definition at line 674 of file partitioner.h.

private 
An array that contains information which processors my ghost indices belong to and how many those indices are
Definition at line 680 of file partitioner.h.

private 
The set of (local) indices that we are importing during compress(), i.e., others' ghosts that belong to the local range. Similar structure as in an IndexSet, but tailored to be iterated over, and some indices may be duplicates.
Definition at line 688 of file partitioner.h.

mutableprivate 
The set of (local) indices that we are importing during compress(), i.e., others' ghosts that belong to the local range. The data stored is the same than in import_indices_data but the data is expanded in plain arrays. This variable is only used when using CUDAaware MPI.
Definition at line 702 of file partitioner.h.

private 
A variable caching the number of ghost indices. It would be expensive to compute it by iterating over the import indices and accumulate them.
Definition at line 708 of file partitioner.h.

private 
The set of processors and length of data field which send us their ghost data
Definition at line 714 of file partitioner.h.

private 
An array that caches the number of chunks in the import indices per MPI rank. The length is import_indices_data.size()+1.
Definition at line 720 of file partitioner.h.

private 
A variable caching the number of ghost indices in a larger set of indices given by the optional argument to set_ghost_indices().
Definition at line 726 of file partitioner.h.

private 
An array that caches the number of chunks in the import indices per MPI rank. The length is ghost_indices_subset_data.size()+1.
Definition at line 732 of file partitioner.h.

private 
The set of indices that appear for an IndexSet that is a subset of a larger set. Similar structure as in an IndexSet within all ghost indices, but tailored to be iterated over.
Definition at line 740 of file partitioner.h.

private 
The ID of the current processor in the MPI network
Definition at line 745 of file partitioner.h.

private 
The total number of processors active in the problem
Definition at line 750 of file partitioner.h.

private 
The MPI communicator involved in the problem
Definition at line 755 of file partitioner.h.

private 
A variable storing whether the ghost indices have been explicitly set.
Definition at line 760 of file partitioner.h.