Generic Software Components for Scientific Computing by Guntram Berti

By Guntram Berti

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The development of a sparse BLAS, however, has to cope with difficulties, most notably the great diversification of data structures for representing sparse matrices, which enforces a corresponding proliferation of different implementations for the basic operations — in [SL98], the authors note that the NIST sparse BLAS [RP97] contains about 10000 routines. 2 As noted before, this might also be accomplished by registering the matrix-type DA and the preconditioner-type DPP to the solver component, e.

Identify the mathematical structures that are the object of interest • Identify the classes of algorithms for which generic implementations are sought • Set up a taxonomy of data structures used to represent the abstract mathematical structures, corresponding to the functionality they support • Analyze a representative number of algorithms for their essential requirements on mathematical as well as on data structures. This step will be carried out in chapter 3. Abstraction The next step consists in uncovering commonalities and variabilities [Cop98] in requirements and functionality of algorithms and data structures, respectively.

But this turns out to be rather impractical, as standardization processes are slow, if successful at all. The more complicated a domain is, the less probable it is that there is consensus even on the question what belongs to the domain, not to mention such details like interfaces. Perhaps the most difficult problem is cross-domain syntax and semantics (the ‘viewx-as-y’ relationship). 4. In general, the best what can be hoped for is a de-facto standard. In the C++ community, the ‘standard’ set by the STL seems to approach this state for the domain of sequences.

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