Reference documentation for deal.II version 9.3.3
TrilinosWrappers::MPI::Vector Class Reference

#include <deal.II/lac/trilinos_vector.h>

Inheritance diagram for TrilinosWrappers::MPI::Vector:
[legend]

## Public Types

using value_type = TrilinosScalar

using real_type = TrilinosScalar

using size_type = ::types::global_dof_index

using iterator = value_type *

using const_iterator = const value_type *

using reference = internal::VectorReference

using const_reference = const internal::VectorReference

## Public Member Functions

1: Basic Object-handling
Vector ()

Vector (const Vector &v)

Vector (const IndexSet &parallel_partitioning, const MPI_Comm &communicator=MPI_COMM_WORLD)

Vector (const IndexSet &local, const IndexSet &ghost, const MPI_Comm &communicator=MPI_COMM_WORLD)

Vector (const IndexSet &parallel_partitioning, const Vector &v, const MPI_Comm &communicator=MPI_COMM_WORLD)

template<typename Number >
Vector (const IndexSet &parallel_partitioning, const ::Vector< Number > &v, const MPI_Comm &communicator=MPI_COMM_WORLD)

Vector (Vector &&v) noexcept

~Vector () override=default

void clear ()

void reinit (const Vector &v, const bool omit_zeroing_entries=false, const bool allow_different_maps=false)

void reinit (const IndexSet &parallel_partitioning, const MPI_Comm &communicator=MPI_COMM_WORLD, const bool omit_zeroing_entries=false)

void reinit (const IndexSet &locally_owned_entries, const IndexSet &ghost_entries, const MPI_Comm &communicator=MPI_COMM_WORLD, const bool vector_writable=false)

void reinit (const BlockVector &v, const bool import_data=false)

void compress (::VectorOperation::values operation)

Vectoroperator= (const TrilinosScalar s)

Vectoroperator= (const Vector &v)

Vectoroperator= (Vector &&v) noexcept

template<typename Number >
Vectoroperator= (const ::Vector< Number > &v)

void import_nonlocal_data_for_fe (const ::TrilinosWrappers::SparseMatrix &matrix, const Vector &vector)

void import (const LinearAlgebra::ReadWriteVector< double > &rwv, const VectorOperation::values operation)

bool operator== (const Vector &v) const

bool operator!= (const Vector &v) const

size_type size () const

size_type local_size () const

size_type locally_owned_size () const

std::pair< size_type, size_typelocal_range () const

bool in_local_range (const size_type index) const

IndexSet locally_owned_elements () const

bool has_ghost_elements () const

void update_ghost_values () const

TrilinosScalar operator* (const Vector &vec) const

real_type norm_sqr () const

TrilinosScalar mean_value () const

TrilinosScalar min () const

TrilinosScalar max () const

real_type l1_norm () const

real_type l2_norm () const

real_type lp_norm (const TrilinosScalar p) const

real_type linfty_norm () const

TrilinosScalar add_and_dot (const TrilinosScalar a, const Vector &V, const Vector &W)

bool all_zero () const

bool is_non_negative () const

2: Data-Access
reference operator() (const size_type index)

TrilinosScalar operator() (const size_type index) const

reference operator[] (const size_type index)

TrilinosScalar operator[] (const size_type index) const

void extract_subvector_to (const std::vector< size_type > &indices, std::vector< TrilinosScalar > &values) const

template<typename ForwardIterator , typename OutputIterator >
void extract_subvector_to (ForwardIterator indices_begin, const ForwardIterator indices_end, OutputIterator values_begin) const

iterator begin ()

const_iterator begin () const

iterator end ()

const_iterator end () const

3: Modification of vectors
void set (const std::vector< size_type > &indices, const std::vector< TrilinosScalar > &values)

void set (const std::vector< size_type > &indices, const ::Vector< TrilinosScalar > &values)

void set (const size_type n_elements, const size_type *indices, const TrilinosScalar *values)

void add (const std::vector< size_type > &indices, const std::vector< TrilinosScalar > &values)

void add (const std::vector< size_type > &indices, const ::Vector< TrilinosScalar > &values)

void add (const size_type n_elements, const size_type *indices, const TrilinosScalar *values)

Vectoroperator*= (const TrilinosScalar factor)

Vectoroperator/= (const TrilinosScalar factor)

Vectoroperator+= (const Vector &V)

Vectoroperator-= (const Vector &V)

void add (const TrilinosScalar s)

void add (const Vector &V, const bool allow_different_maps=false)

void add (const TrilinosScalar a, const Vector &V)

void add (const TrilinosScalar a, const Vector &V, const TrilinosScalar b, const Vector &W)

void sadd (const TrilinosScalar s, const Vector &V)

void sadd (const TrilinosScalar s, const TrilinosScalar a, const Vector &V)

void scale (const Vector &scaling_factors)

void equ (const TrilinosScalar a, const Vector &V)

## Related Functions

(Note that these are not member functions.)

void swap (Vector &u, Vector &v)

## 4: Mixed stuff

Epetra_CombineMode last_action

bool compressed

bool has_ghosts

std::unique_ptr< Epetra_FEVector > vector

std::unique_ptr< Epetra_MultiVector > nonlocal_vector

IndexSet owned_elements

class internal::VectorReference

const Epetra_MultiVector & trilinos_vector () const

Epetra_FEVector & trilinos_vector ()

const Epetra_BlockMap & trilinos_partitioner () const

void print (std::ostream &out, const unsigned int precision=3, const bool scientific=true, const bool across=true) const

void swap (Vector &v)

std::size_t memory_consumption () const

const MPI_Commget_mpi_communicator () const

static ::ExceptionBaseExcDifferentParallelPartitioning ()

static ::ExceptionBaseExcTrilinosError (int arg1)

static ::ExceptionBaseExcAccessToNonLocalElement (size_type arg1, size_type arg2, size_type arg3, size_type arg4)

## Subscriptor functionality

Classes derived from Subscriptor provide a facility to subscribe to this object. This is mostly used by the SmartPointer class.

std::atomic< unsigned intcounter

std::map< std::string, unsigned intcounter_map

std::vector< std::atomic< bool > * > validity_pointers

const std::type_info * object_info

void subscribe (std::atomic< bool > *const validity, const std::string &identifier="") const

void unsubscribe (std::atomic< bool > *const validity, const std::string &identifier="") const

unsigned int n_subscriptions () const

template<typename StreamType >
void list_subscribers (StreamType &stream) const

void list_subscribers () const

template<class Archive >
void serialize (Archive &ar, const unsigned int version)

static ::ExceptionBaseExcInUse (int arg1, std::string arg2, std::string arg3)

static ::ExceptionBaseExcNoSubscriber (std::string arg1, std::string arg2)

using map_value_type = decltype(counter_map)::value_type

using map_iterator = decltype(counter_map)::iterator

static std::mutex mutex

void check_no_subscribers () const noexcept

## Detailed Description

This class implements a wrapper to use the Trilinos distributed vector class Epetra_FEVector, the (parallel) partitioning of which is governed by an Epetra_Map. The Epetra_FEVector is precisely the kind of vector we deal with all the time - we probably get it from some assembly process, where also entries not locally owned might need to written and hence need to be forwarded to the owner.

The interface of this class is modeled after the existing Vector class in deal.II. It has almost the same member functions, and is often exchangeable. However, since Trilinos only supports a single scalar type (double), it is not templated, and only works with that type.

Note that Trilinos only guarantees that operations do what you expect if the function GlobalAssemble has been called after vector assembly in order to distribute the data. This is necessary since some processes might have accumulated data of elements that are not owned by themselves, but must be sent to the owning process. In order to avoid using the wrong data, you need to call Vector::compress() before you actually use the vectors.

### Parallel communication model

The parallel functionality of Trilinos is built on top of the Message Passing Interface (MPI). MPI's communication model is built on collective communications: if one process wants something from another, that other process has to be willing to accept this communication. A process cannot query data from another process by calling a remote function, without that other process expecting such a transaction. The consequence is that most of the operations in the base class of this class have to be called collectively. For example, if you want to compute the l2 norm of a parallel vector, all processes across which this vector is shared have to call the l2_norm function. If you don't do this, but instead only call the l2_norm function on one process, then the following happens: This one process will call one of the collective MPI functions and wait for all the other processes to join in on this. Since the other processes don't call this function, you will either get a time-out on the first process, or, worse, by the time the next a call to a Trilinos function generates an MPI message on the other processes, you will get a cryptic message that only a subset of processes attempted a communication. These bugs can be very hard to figure out, unless you are well-acquainted with the communication model of MPI, and know which functions may generate MPI messages.

One particular case, where an MPI message may be generated unexpectedly is discussed below.

### Accessing individual elements of a vector

Trilinos does of course allow read access to individual elements of a vector, but in the distributed case only to elements that are stored locally. We implement this through calls like d=vec(i). However, if you access an element outside the locally stored range, an exception is generated.

In contrast to read access, Trilinos (and the respective deal.II wrapper classes) allow to write (or add) to individual elements of vectors, even if they are stored on a different process. You can do this by writing into or adding to elements using the syntax vec(i)=d or vec(i)+=d, or similar operations. There is one catch, however, that may lead to very confusing error messages: Trilinos requires application programs to call the compress() function when they switch from performing a set of operations that add to elements, to performing a set of operations that write to elements. The reasoning is that all processes might accumulate addition operations to elements, even if multiple processes write to the same elements. By the time we call compress() the next time, all these additions are executed. However, if one process adds to an element, and another overwrites to it, the order of execution would yield non-deterministic behavior if we don't make sure that a synchronization with compress() happens in between.

In order to make sure these calls to compress() happen at the appropriate time, the deal.II wrappers keep a state variable that store which is the presently allowed operation: additions or writes. If it encounters an operation of the opposite kind, it calls compress() and flips the state. This can sometimes lead to very confusing behavior, in code that may for example look like this:

// do some write operations on the vector
for (size_type i=0; i<vector->size(); ++i)
vector(i) = i;
// do some additions to vector elements, but
// only for some elements
for (size_type i=0; i<vector->size(); ++i)
if (some_condition(i) == true)
vector(i) += 1;
// do another collective operation
const double norm = vector->l2_norm();
std::unique_ptr< Epetra_FEVector > vector
double norm(const FEValuesBase< dim > &fe, const ArrayView< const std::vector< Tensor< 1, dim > > > &Du)
Definition: divergence.h:472

This code can run into trouble: by the time we see the first addition operation, we need to flush the overwrite buffers for the vector, and the deal.II library will do so by calling compress(). However, it will only do so for all processes that actually do an addition – if the condition is never true for one of the processes, then this one will not get to the actual compress() call, whereas all the other ones do. This gets us into trouble, since all the other processes hang in the call to flush the write buffers, while the one other process advances to the call to compute the l2 norm. At this time, you will get an error that some operation was attempted by only a subset of processes. This behavior may seem surprising, unless you know that write/addition operations on single elements may trigger this behavior.

The problem described here may be avoided by placing additional calls to compress(), or making sure that all processes do the same type of operations at the same time, for example by placing zero additions if necessary.

### Ghost elements of vectors

Parallel vectors come in two kinds: without and with ghost elements. Vectors without ghost elements uniquely partition the vector elements between processors: each vector entry has exactly one processor that owns it. For such vectors, you can read those elements that the processor you are currently on owns, and you can write into any element whether you own it or not: if you don't own it, the value written or added to a vector element will be shipped to the processor that owns this vector element the next time you call compress(), as described above.

What we call a 'ghosted' vector (see vectors with ghost elements ) is simply a view of the parallel vector where the element distributions overlap. The 'ghosted' Trilinos vector in itself has no idea of which entries are ghosted and which are locally owned. In fact, a ghosted vector may not even store all of the elements a non- ghosted vector would store on the current processor. Consequently, for Trilinos vectors, there is no notion of an 'owner' of vector elements in the way we have it in the non-ghost case view.

This explains why we do not allow writing into ghosted vectors on the Trilinos side: Who would be responsible for taking care of the duplicated entries, given that there is not such information as locally owned indices? In other words, since a processor doesn't know which other processors own an element, who would it send a value to if one were to write to it? The only possibility would be to send this information to all other processors, but that is clearly not practical. Thus, we only allow reading from ghosted vectors, which however we do very often.

So how do you fill a ghosted vector if you can't write to it? This only happens through the assignment with a non-ghosted vector. It can go both ways (non-ghosted is assigned to a ghosted vector, and a ghosted vector is assigned to a non-ghosted one; the latter one typically only requires taking out the locally owned part as most often ghosted vectors store a superset of elements of non-ghosted ones). In general, you send data around with that operation and it all depends on the different views of the two vectors. Trilinos also allows you to get subvectors out of a big vector that way.

### Thread safety of Trilinos vectors

When writing into Trilinos vectors from several threads in shared memory, several things must be kept in mind as there is no built-in locks in this class to prevent data races. Simultaneous access to the same vector entry at the same time results in data races and must be explicitly avoided by the user. However, it is possible to access different entries of the vector from several threads simultaneously when only one MPI process is present or the vector has been constructed with an additional index set for ghost entries in write mode.

2008, 2009, 2017

Definition at line 403 of file trilinos_vector.h.

The documentation for this class was generated from the following files: