Namespaces
namespace	ConsensusAlgorithms

namespace	internal

Classes
class	CollectiveMutex

class	CommunicationPatternBase

class	ConsensusAlgorithmsProcessTargets

class	DuplicatedCommunicator

struct	MinMaxAvg

class	MPI_InitFinalize

class	NoncontiguousPartitioner

class	Partitioner

class	ProcessGrid

class	RemotePointEvaluation

Functions
unsigned int	n_mpi_processes (const MPI_Comm &mpi_communicator)

unsigned int	this_mpi_process (const MPI_Comm &mpi_communicator)

const std::vector< unsigned int >	mpi_processes_within_communicator (const MPI_Comm &comm_large, const MPI_Comm &comm_small)

std::vector< unsigned int >	compute_point_to_point_communication_pattern (const MPI_Comm &mpi_comm, const std::vector< unsigned int > &destinations)

unsigned int	compute_n_point_to_point_communications (const MPI_Comm &mpi_comm, const std::vector< unsigned int > &destinations)

MPI_Comm	duplicate_communicator (const MPI_Comm &mpi_communicator)

void	free_communicator (MPI_Comm &mpi_communicator)

int	create_group (const MPI_Comm &comm, const MPI_Group &group, const int tag, MPI_Comm *new_comm)

std::vector< IndexSet >	create_ascending_partitioning (const MPI_Comm &comm, const IndexSet::size_type locally_owned_size)

IndexSet	create_evenly_distributed_partitioning (const MPI_Comm &comm, const IndexSet::size_type total_size)

template<class Iterator , typename Number = long double>
std::pair< Number, typename numbers::NumberTraits< Number >::real_type >	mean_and_standard_deviation (const Iterator begin, const Iterator end, const MPI_Comm &comm)

template<typename T >
T	sum (const T &t, const MPI_Comm &mpi_communicator)

template<typename T , typename U >
void	sum (const T &values, const MPI_Comm &mpi_communicator, U &sums)

template<typename T >
void	sum (const ArrayView< const T > &values, const MPI_Comm &mpi_communicator, const ArrayView< T > &sums)

template<int rank, int dim, typename Number >
SymmetricTensor< rank, dim, Number >	sum (const SymmetricTensor< rank, dim, Number > &local, const MPI_Comm &mpi_communicator)

template<int rank, int dim, typename Number >
Tensor< rank, dim, Number >	sum (const Tensor< rank, dim, Number > &local, const MPI_Comm &mpi_communicator)

template<typename Number >
void	sum (const SparseMatrix< Number > &local, const MPI_Comm &mpi_communicator, SparseMatrix< Number > &global)

template<typename T >
T	max (const T &t, const MPI_Comm &mpi_communicator)

template<typename T , typename U >
void	max (const T &values, const MPI_Comm &mpi_communicator, U &maxima)

template<typename T >
void	max (const ArrayView< const T > &values, const MPI_Comm &mpi_communicator, const ArrayView< T > &maxima)

template<typename T >
T	min (const T &t, const MPI_Comm &mpi_communicator)

template<typename T , typename U >
void	min (const T &values, const MPI_Comm &mpi_communicator, U &minima)

template<typename T >
void	min (const ArrayView< const T > &values, const MPI_Comm &mpi_communicator, const ArrayView< T > &minima)

template<typename T >
T	logical_or (const T &t, const MPI_Comm &mpi_communicator)

template<typename T , typename U >
void	logical_or (const T &values, const MPI_Comm &mpi_communicator, U &results)

template<typename T >
void	logical_or (const ArrayView< const T > &values, const MPI_Comm &mpi_communicator, const ArrayView< T > &results)

MinMaxAvg	min_max_avg (const double my_value, const MPI_Comm &mpi_communicator)

std::vector< MinMaxAvg >	min_max_avg (const std::vector< double > &my_value, const MPI_Comm &mpi_communicator)

void	min_max_avg (const ArrayView< const double > &my_values, const ArrayView< MinMaxAvg > &result, const MPI_Comm &mpi_communicator)

bool	job_supports_mpi ()

template<typename T >
std::map< unsigned int, T >	some_to_some (const MPI_Comm &comm, const std::map< unsigned int, T > &objects_to_send)

template<typename T >
std::vector< T >	all_gather (const MPI_Comm &comm, const T &object_to_send)

template<typename T >
std::vector< T >	gather (const MPI_Comm &comm, const T &object_to_send, const unsigned int root_process=0)

template<typename T >
T	broadcast (const MPI_Comm &comm, const T &object_to_send, const unsigned int root_process=0)

template<typename T >
T	all_reduce (const T &local_value, const MPI_Comm &comm, const std::function< T(const T &, const T &)> &combiner)

std::vector< unsigned int >	compute_index_owner (const IndexSet &owned_indices, const IndexSet &indices_to_look_up, const MPI_Comm &comm)

template<typename T >
std::vector< T >	compute_set_union (const std::vector< T > &vec, const MPI_Comm &comm)

template<typename T >
std::set< T >	compute_set_union (const std::set< T > &set, const MPI_Comm &comm)

Detailed Description

A namespace for utility functions that abstract certain operations using the Message Passing Interface (MPI) or provide fallback operations in case deal.II is configured not to use MPI at all.

Function Documentation

◆ n_mpi_processes()

unsigned int Utilities::MPI::n_mpi_processes ( const MPI_Comm & mpi_communicator )

Return the number of MPI processes there exist in the given communicator object. If this is a sequential job (i.e., the program is not using MPI at all, or is using MPI but has been started with only one MPI process), then the communicator necessarily involves only one process and the function returns 1.

Definition at line 117 of file mpi.cc.

◆ this_mpi_process()

unsigned int Utilities::MPI::this_mpi_process ( const MPI_Comm & mpi_communicator )

Return the rank of the present MPI process in the space of processes described by the given communicator. This will be a unique value for each process between zero and (less than) the number of all processes (given by get_n_mpi_processes()).

Definition at line 128 of file mpi.cc.

◆ mpi_processes_within_communicator()

const std::vector< unsigned int > Utilities::MPI::mpi_processes_within_communicator	(	const MPI_Comm &	comm_large,
		const MPI_Comm &	comm_small
	)

Return a vector of the ranks (within comm_large) of a subset of processes specified by comm_small.

Definition at line 140 of file mpi.cc.

◆ compute_point_to_point_communication_pattern()

std::vector< unsigned int > Utilities::MPI::compute_point_to_point_communication_pattern	(	const MPI_Comm &	mpi_comm,
		const std::vector< unsigned int > &	destinations
	)

Consider an unstructured communication pattern where every process in an MPI universe wants to send some data to a subset of the other processors. To do that, the other processors need to know who to expect messages from. This function computes this information.

Parameters

mpi_comm	A communicator that describes the processors that are going to communicate with each other.
destinations	The list of processors the current process wants to send information to. This list need not be sorted in any way. If it contains duplicate entries that means that multiple messages are intended for a given destination.

Returns: A list of processors that have indicated that they want to send something to the current processor. The resulting list is not sorted. It may contain duplicate entries if processors enter the same destination more than once in their destinations list.

Definition at line 359 of file mpi.cc.

◆ compute_n_point_to_point_communications()

unsigned int Utilities::MPI::compute_n_point_to_point_communications	(	const MPI_Comm &	mpi_comm,
		const std::vector< unsigned int > &	destinations
	)

Simplified (for efficiency) version of the compute_point_to_point_communication_pattern() which only computes the number of processes in an MPI universe to expect communication from.

Parameters

mpi_comm	A communicator that describes the processors that are going to communicate with each other.
destinations	The list of processors the current process wants to send information to. This list need not be sorted in any way. If it contains duplicate entries that means that multiple messages are intended for a given destination.

Returns: A number of processors that want to send something to the current processor.

Definition at line 497 of file mpi.cc.

◆ duplicate_communicator()

MPI_Comm Utilities::MPI::duplicate_communicator ( const MPI_Comm & mpi_communicator )

Given a communicator, generate a new communicator that contains the same set of processors but that has a different, unique identifier.

This functionality can be used to ensure that different objects, such as distributed matrices, each have unique communicators over which they can interact without interfering with each other.

When no longer needed, the communicator created here needs to be destroyed using free_communicator().

This function is equivalent to calling MPI_Comm_dup(mpi_communicator, &return_value);.

Definition at line 160 of file mpi.cc.

◆ free_communicator()

void Utilities::MPI::free_communicator ( MPI_Comm & mpi_communicator )

Free the given communicator mpi_communicator that was duplicated using duplicate_communicator().

The argument is passed by reference and will be invalidated and set to the MPI null handle. This function is equivalent to calling MPI_Comm_free(&mpi_communicator);.

Definition at line 171 of file mpi.cc.

◆ create_group()

int Utilities::MPI::create_group	(	const MPI_Comm &	comm,
		const MPI_Group &	group,
		const int	tag,
		MPI_Comm *	new_comm
	)

If comm is an intracommunicator, this function returns a new communicator newcomm with communication group defined by the group argument. The function is only collective over the group of processes that actually want to create the communicator, i.e., that are named in the group argument. If multiple threads at a given process perform concurrent create_group() operations, the user must distinguish these operations by providing different tag or comm arguments.

This function was introduced in the MPI-3.0 standard. If available, the corresponding function in the provided MPI implementation is used. Otherwise, the implementation follows the one described in the following publication:

@inproceedings{dinan2011noncollective,
  title        = {Noncollective communicator creation in MPI},
  author       = {Dinan, James and Krishnamoorthy, Sriram and Balaji,
                  Pavan and Hammond, Jeff R and Krishnan, Manojkumar and
                  Tipparaju, Vinod and Vishnu, Abhinav},
  booktitle    = {European MPI Users' Group Meeting},
  pages        = {282--291},
  year         = {2011},
  organization = {Springer}
}

Definition at line 181 of file mpi.cc.

◆ create_ascending_partitioning()

std::vector< IndexSet > Utilities::MPI::create_ascending_partitioning	(	const MPI_Comm &	comm,
		const IndexSet::size_type	locally_owned_size
	)

Given the number of locally owned elements locally_owned_size, create a 1:1 partitioning of the of elements across the MPI communicator comm. The total size of elements is the sum of locally_owned_size across the MPI communicator. Each process will store contiguous subset of indices, and the index set on process p+1 starts at the index one larger than the last one stored on process p.

Definition at line 263 of file mpi.cc.

◆ create_evenly_distributed_partitioning()

IndexSet Utilities::MPI::create_evenly_distributed_partitioning	(	const MPI_Comm &	comm,
		const IndexSet::size_type	total_size
	)

Given the total number of elements total_size, create an evenly distributed 1:1 partitioning of the elements across the MPI communicator comm. Uses comm to determine number of partitions and processor ID to call the create_evenly_distributed_partitioning() function above.

Definition at line 285 of file mpi.cc.

◆ mean_and_standard_deviation()

template<class Iterator , typename Number = long double>

std::pair< Number, typename numbers::NumberTraits< Number >::real_type > Utilities::MPI::mean_and_standard_deviation	(	const Iterator	begin,
		const Iterator	end,
		const MPI_Comm &	comm
	)

Calculate mean and standard deviation across the MPI communicator comm for values provided as a range [begin,end). The mean is computed as \(\bar x=\frac 1N \sum x_k\) where the \(x_k\) are the elements pointed to by the begin and end iterators on all processors (i.e., each processor's [begin,end) range points to a subset of the overall number of elements). The standard deviation is calculated as \(\sigma=\sqrt{\frac {1}{N-1} \sum |x_k -\bar x|^2}\), which is known as unbiased sample variance.

Template Parameters

Number specifies the type to store the mean value. The standard deviation is stored as the corresponding real type. This allows, for example, to calculate statistics from integer input values.

◆ sum() [1/6]

template<typename T >

T Utilities::MPI::sum	(	const T &	t,
		const MPI_Comm &	mpi_communicator
	)

Return the sum over all processors of the value t. This function is collective over all processors given in the communicator. If deal.II is not configured for use of MPI, this function simply returns the value of t. This function corresponds to the MPI_Allreduce function, i.e. all processors receive the result of this operation.

Note: Sometimes, not all processors need a result and in that case one would call the MPI_Reduce function instead of the MPI_Allreduce function. The latter is at most twice as expensive, so if you are concerned about performance, it may be worthwhile investigating whether your algorithm indeed needs the result everywhere.; This function is only implemented for certain template arguments T, namely float, double, int, unsigned int.

◆ sum() [2/6]

template<typename T , typename U >

void Utilities::MPI::sum	(	const T &	values,
		const MPI_Comm &	mpi_communicator,
		U &	sums
	)

Like the previous function, but take the sums over the elements of an array of type T. In other words, the i-th element of the results array is the sum over the i-th entries of the input arrays from each processor. T and U must decay to the same type, e.g. they just differ by one of them having a const type qualifier and the other not.

Input and output arrays may be the same.

◆ sum() [3/6]

template<typename T >

void Utilities::MPI::sum	(	const ArrayView< const T > &	values,
		const MPI_Comm &	mpi_communicator,
		const ArrayView< T > &	sums
	)

Like the previous function, but take the sums over the elements of an array as specified by the ArrayView arguments. In other words, the i-th element of the results array is the sum over the i-th entries of the input arrays from each processor.

Input and output arrays may be the same.

◆ sum() [4/6]

template<int rank, int dim, typename Number >

SymmetricTensor< rank, dim, Number > Utilities::MPI::sum	(	const SymmetricTensor< rank, dim, Number > &	local,
		const MPI_Comm &	mpi_communicator
	)

Perform an MPI sum of the entries of a symmetric tensor.

◆ sum() [5/6]

template<int rank, int dim, typename Number >

Tensor< rank, dim, Number > Utilities::MPI::sum	(	const Tensor< rank, dim, Number > &	local,
		const MPI_Comm &	mpi_communicator
	)

Perform an MPI sum of the entries of a tensor.

◆ sum() [6/6]

template<typename Number >

void Utilities::MPI::sum	(	const SparseMatrix< Number > &	local,
		const MPI_Comm &	mpi_communicator,
		SparseMatrix< Number > &	global
	)

Perform an MPI sum of the entries of a SparseMatrix.

Note: local and global should have the same sparsity pattern and it should be the same for all MPI processes.

◆ max() [1/3]

template<typename T >

T Utilities::MPI::max	(	const T &	t,
		const MPI_Comm &	mpi_communicator
	)

Return the maximum over all processors of the value t. This function is collective over all processors given in the communicator. If deal.II is not configured for use of MPI, this function simply returns the value of t. This function corresponds to the MPI_Allreduce function, i.e. all processors receive the result of this operation.

Note: Sometimes, not all processors need a result and in that case one would call the MPI_Reduce function instead of the MPI_Allreduce function. The latter is at most twice as expensive, so if you are concerned about performance, it may be worthwhile investigating whether your algorithm indeed needs the result everywhere.; This function is only implemented for certain template arguments T, namely float, double, int, unsigned int.

◆ max() [2/3]

template<typename T , typename U >

void Utilities::MPI::max	(	const T &	values,
		const MPI_Comm &	mpi_communicator,
		U &	maxima
	)

Like the previous function, but take the maximum over the elements of an array of type T. In other words, the i-th element of the results array is the maximum over the i-th entries of the input arrays from each processor. T and U must decay to the same type, e.g. they just differ by one of them having a const type qualifier and the other not.

Input and output vectors may be the same.

◆ max() [3/3]

template<typename T >

void Utilities::MPI::max	(	const ArrayView< const T > &	values,
		const MPI_Comm &	mpi_communicator,
		const ArrayView< T > &	maxima
	)

Like the previous function, but take the maximum over the elements of an array as specified by the ArrayView arguments. In other words, the i-th element of the results array is the maximum over the i-th entries of the input arrays from each processor.

Input and output arrays may be the same.

◆ min() [1/3]

template<typename T >

T Utilities::MPI::min	(	const T &	t,
		const MPI_Comm &	mpi_communicator
	)

Return the minimum over all processors of the value t. This function is collective over all processors given in the communicator. If deal.II is not configured for use of MPI, this function simply returns the value of t. This function corresponds to the MPI_Allreduce function, i.e. all processors receive the result of this operation.

Note: Sometimes, not all processors need a result and in that case one would call the MPI_Reduce function instead of the MPI_Allreduce function. The latter is at most twice as expensive, so if you are concerned about performance, it may be worthwhile investigating whether your algorithm indeed needs the result everywhere.; This function is only implemented for certain template arguments T, namely float, double, int, unsigned int.

◆ min() [2/3]

template<typename T , typename U >

void Utilities::MPI::min	(	const T &	values,
		const MPI_Comm &	mpi_communicator,
		U &	minima
	)

Like the previous function, but take the minima over the elements of an array of type T. In other words, the i-th element of the results array is the minimum of the i-th entries of the input arrays from each processor. T and U must decay to the same type, e.g. they just differ by one of them having a const type qualifier and the other not.

Input and output arrays may be the same.

◆ min() [3/3]

template<typename T >

void Utilities::MPI::min	(	const ArrayView< const T > &	values,
		const MPI_Comm &	mpi_communicator,
		const ArrayView< T > &	minima
	)

Like the previous function, but take the minimum over the elements of an array as specified by the ArrayView arguments. In other words, the i-th element of the results array is the minimum over the i-th entries of the input arrays from each processor.

Input and output arrays may be the same.

◆ logical_or() [1/3]

template<typename T >

T Utilities::MPI::logical_or	(	const T &	t,
		const MPI_Comm &	mpi_communicator
	)

Performs a logical or operation over all processors of the value t. The logical or operator || returns the boolean value true if either or all operands are true and returns false otherwise. If the provided value t corresponds to 0 in its associated data type T, it will be interpreted as false, and true otherwise. Data type T must be of type integral, i.e., bool, char, short, int, long, or any of their variations.

This function is collective over all processors given in the communicator. If deal.II is not configured for use of MPI, this function simply returns the value of value. This function corresponds to the MPI_Allreduce function, i.e., all processors receive the result of this operation.

Note: Sometimes, not all processors need a result and in that case one would call the MPI_Reduce function instead of the MPI_Allreduce function. The latter is at most twice as expensive, so if you are concerned about performance, it may be worthwhile investigating whether your algorithm indeed needs the result everywhere.

◆ logical_or() [2/3]

template<typename T , typename U >

void Utilities::MPI::logical_or	(	const T &	values,
		const MPI_Comm &	mpi_communicator,
		U &	results
	)

Like the previous function, but performs the logical or operation on each element of an array. In other words, the i-th element of the results array is the result of the logical or operation applied on the i-th entries of the input arrays from each processor. T and U must decay to the same type, e.g., they just differ by one of them having a const type qualifier and the other not.

Input and output arrays may be the same.

Note: Depending on your standard library, this function may not work with specializations of std::vector for the data type bool. In that case, use a different container or data type.

◆ logical_or() [3/3]

template<typename T >

void Utilities::MPI::logical_or	(	const ArrayView< const T > &	values,
		const MPI_Comm &	mpi_communicator,
		const ArrayView< T > &	results
	)

Like the previous function, but performs the logical or operation on each element of an array as specified by the ArrayView arguments. In other words, the i-th element of the results array is the result of the logical or operation applied on the i-th entries of the input arrays from each processor.

Input and output arrays may be the same.

◆ min_max_avg() [1/3]

MinMaxAvg Utilities::MPI::min_max_avg	(	const double	my_value,
		const MPI_Comm &	mpi_communicator
	)

Return sum, average, minimum, maximum, processor id of minimum and maximum as a collective operation of on the given MPI communicator mpi_communicator. Each processor's value is given in my_value and the result will be returned. The result is available on all machines.

Note: Sometimes, not all processors need a result and in that case one would call the MPI_Reduce function instead of the MPI_Allreduce function. The latter is at most twice as expensive, so if you are concerned about performance, it may be worthwhile investigating whether your algorithm indeed needs the result everywhere.

Definition at line 91 of file mpi.cc.

◆ min_max_avg() [2/3]

std::vector< MinMaxAvg > Utilities::MPI::min_max_avg	(	const std::vector< double > &	my_value,
		const MPI_Comm &	mpi_communicator
	)

Same as above but returning the sum, average, minimum, maximum, process id of minimum and maximum as a collective operation on the given MPI communicator mpi_communicator for each entry of the vector.

Note: This function performs a single reduction sweep.

Precondition: Size of the input vector has to be the same on all processes.

Definition at line 104 of file mpi.cc.

◆ min_max_avg() [3/3]

void Utilities::MPI::min_max_avg	(	const ArrayView< const double > &	my_values,
		const ArrayView< MinMaxAvg > &	result,
		const MPI_Comm &	mpi_communicator
	)

Same as above but returning the sum, average, minimum, maximum, process id of minimum and maximum as a collective operation on the given MPI communicator mpi_communicator for each entry of the ArrayView.

Note: This function performs a single reduction sweep.

Precondition: Size of the input ArrayView has to be the same on all processes and the input and output ArrayVew have to have the same size.

Definition at line 604 of file mpi.cc.

◆ job_supports_mpi()

bool Utilities::MPI::job_supports_mpi ( )

Return whether (i) deal.II has been compiled to support MPI (for example by compiling with CXX=mpiCC) and if so whether (ii) MPI_Init() has been called (for example using the Utilities::MPI::MPI_InitFinalize class). In other words, the result indicates whether the current job is running under MPI.

Note: The function does not take into account whether an MPI job actually runs on more than one processor or is, in fact, a single-node job that happens to run under MPI.

Definition at line 1097 of file mpi.cc.

◆ some_to_some()

template<typename T >

std::map< unsigned int, T > Utilities::MPI::some_to_some	(	const MPI_Comm &	comm,
		const std::map< unsigned int, T > &	objects_to_send
	)

Initiate a some-to-some communication, and exchange arbitrary objects (the class T should be serializable using boost::serialize) between processors.

Parameters

[in]	comm	MPI communicator.
[in]	objects_to_send	A map from the rank (unsigned int) of the process meant to receive the data and the object to send (the type `T` must be serializable for this function to work properly). If this map contains an entry with a key equal to the rank of the current process (i.e., an instruction to a process to send data to itself), then this data item is simply copied to the returned object.

Returns: A map from the rank (unsigned int) of the process which sent the data and object received.

◆ all_gather()

template<typename T >

std::vector< T > Utilities::MPI::all_gather	(	const MPI_Comm &	comm,
		const T &	object_to_send
	)

A generalization of the classic MPI_Allgather function, that accepts arbitrary data types T, as long as boost::serialize accepts T as an argument.

Parameters

[in]	comm	MPI communicator.
[in]	object_to_send	An object to send to all other processes

Returns: A vector of objects, with size equal to the number of processes in the MPI communicator. Each entry contains the object received from the processor with the corresponding rank within the communicator.

◆ gather()

template<typename T >

std::vector< T > Utilities::MPI::gather	(	const MPI_Comm &	comm,
		const T &	object_to_send,
		const unsigned int	root_process = `0`
	)

A generalization of the classic MPI_Gather function, that accepts arbitrary data types T, as long as boost::serialize accepts T as an argument.

Parameters

[in]	comm	MPI communicator.
[in]	object_to_send	an object to send to the root process
[in]	root_process	The process, which receives the objects from all processes. By default the process with rank 0 is the root process.

Returns: The root_process receives a vector of objects, with size equal to the number of processes in the MPI communicator. Each entry contains the object received from the processor with the corresponding rank within the communicator. All other processes receive an empty vector.

◆ broadcast()

template<typename T >

T Utilities::MPI::broadcast	(	const MPI_Comm &	comm,
		const T &	object_to_send,
		const unsigned int	root_process = `0`
	)

Sends an object object_to_send from the process root_process to all other processes.

A generalization of the classic MPI_Bcast function that accepts arbitrary data types T, as long as Utilities::pack() (which in turn uses boost::serialize, see in Utilities::pack() for details) accepts T as an argument.

Parameters

[in]	comm	MPI communicator.
[in]	object_to_send	An object to send to all processes.
[in]	root_process	The process that sends the object to all processes. By default the process with rank 0 is the root process.

Returns: On the root process, return a copy of object_to_send. On every other process, return a copy of the object sent by the root_process.

◆ all_reduce()

template<typename T >

T Utilities::MPI::all_reduce	(	const T &	local_value,
		const MPI_Comm &	comm,
		const std::function< T(const T &, const T &)> &	combiner
	)

A function that combines values local_value from all processes via a user-specified binary operation combiner and distributes the result back to all processes. As such this function is similar to MPI_Allreduce (if it were implemented by a global reduction followed by a broadcast step) but due to the user-specified binary operation also general object types, including ones that store variable amounts of data, can be handled.

◆ compute_index_owner()

std::vector< unsigned int > Utilities::MPI::compute_index_owner	(	const IndexSet &	owned_indices,
		const IndexSet &	indices_to_look_up,
		const MPI_Comm &	comm
	)

Given a partitioned index set space, compute the owning MPI process rank of each element of a second index set according to the partitioned index set. A natural usage of this function is to compute for each ghosted degree of freedom the MPI rank of the process owning that index.

One might think: "But we know which rank a ghost DoF belongs to based on the subdomain id of the cell it is on". But this heuristic fails for DoFs on interfaces between ghost cells with different subdomain_ids, or between a ghost cell and an artificial cell. Furthermore, this function enables a completely abstract exchange of information without the help of the mesh in terms of neighbors.

The first argument passed to this function, owned_indices, must uniquely partition an index space between all processes. Otherwise, there are no limitations on this argument: In particular, there is no need in partitioning the index space into contiguous subsets. Furthermore, there are no limitations on the second index set indices_to_look_up as long as the size matches the first one. It can be chosen arbitrarily and independently on each process. In the case that the second index set also contains locally owned indices, these indices will be treated correctly and the rank of this process is returned for those entries.

Note: This is a collective operation: all processes within the given communicator have to call this function. Since this function does not use MPI_Alltoall or MPI_Allgather, but instead uses non-blocking point-to-point communication instead, and only a single non-blocking barrier, it reduces the memory consumption significantly. This function is suited for large-scale simulations with >100k MPI ranks.

Parameters

[in]	owned_indices	Index set with indices locally owned by this process.
[in]	indices_to_look_up	Index set containing indices of which the user is interested the rank of the owning process.
[in]	comm	MPI communicator.

Returns: List containing the MPI process rank for each entry in the index set indices_to_look_up. The order coincides with the order within the ElementIterator.

Definition at line 1113 of file mpi.cc.

◆ compute_set_union() [1/2]

template<typename T >

std::vector< T > Utilities::MPI::compute_set_union	(	const std::vector< T > &	vec,
		const MPI_Comm &	comm
	)

Compute the union of the input vectors vec of all processes in the MPI communicator comm.

Note: This is a collective operation. The result will available on all processes.

◆ compute_set_union() [2/2]

template<typename T >

std::set< T > Utilities::MPI::compute_set_union	(	const std::set< T > &	set,
		const MPI_Comm &	comm
	)

The same as above but for std::set.

Namespaces

Classes

Functions

Detailed Description

Function Documentation

◆ n_mpi_processes()

◆ this_mpi_process()

◆ mpi_processes_within_communicator()

◆ compute_point_to_point_communication_pattern()

◆ compute_n_point_to_point_communications()

◆ duplicate_communicator()

◆ free_communicator()

◆ create_group()

◆ create_ascending_partitioning()

◆ create_evenly_distributed_partitioning()

◆ mean_and_standard_deviation()

◆ sum() [1/6]

◆ sum() [2/6]

◆ sum() [3/6]

◆ sum() [4/6]

◆ sum() [5/6]

◆ sum() [6/6]

◆ max() [1/3]

◆ max() [2/3]

◆ max() [3/3]

◆ min() [1/3]

◆ min() [2/3]

◆ min() [3/3]

◆ logical_or() [1/3]

◆ logical_or() [2/3]

◆ logical_or() [3/3]

◆ min_max_avg() [1/3]

◆ min_max_avg() [2/3]

◆ min_max_avg() [3/3]

◆ job_supports_mpi()

◆ some_to_some()

◆ all_gather()

◆ gather()

◆ broadcast()

◆ all_reduce()

◆ compute_index_owner()

◆ compute_set_union() [1/2]

◆ compute_set_union() [2/2]