ML Version of the Day
MLAPI can be used for both serial and parallel computations.
Class MLAPI::MultiVector contains one or more vectors. The general usage is as follows:
Space MySpace(128); MultiVector V; int NumVectors = 1; V.Reshape(V, NumVectors);
A new vector can be added to the already allocated ones as follows:
Equivalently, one vector can be deleted. For example, to delete the second multivector, one can write
meaning that now
V contains just one vector.
One of the most powerful feature of C and C++ if the capability of allocating memory. Unfortunately, this is also the area where most bugs are found -- not to mention memory leaks. We have adopted smart pointers to manage memory. MLAPI objects should never be allocated using
new, and therefore never free them using
delete. The code will automatically delete memory when it is no longer referenced by any object. Besides, functions or methods that need to return MLAPI objects, should always return an instance of the required object, and not a pointer or a reference.
Let us consider three generic MLAPI objects. The assignment A = B means the following: all smart pointers contained by B are copied in A, both A and B point to the same memory location. However, A and B are not aliases: we can still write
B = C
meaning that A contains what was contained in B, and both B and C point to the same memory location. Should we need to create a copy of C in B, we will use the instruction
B = Duplicate(C)
which is instead an expensive operation, as new memory needs to be allocated, then all elements in C need to be copied in B.
Although MLAPI is design to be a framework for development of new preconditioners, two classes already defines ready-to-use preconditioners:
An MLAPI::InverseOperator (and therefore preconditioners MLAPI::MultiLevelSA and MLAPI::MultiLevelAdaptiveSA) can be easily wrapped as an Epetra_Operator derived class, then used within, for instance, AztecOO. An example is reported in EpetraInterface.cpp.