Abstract
In parallel and distributes systems we have to consider aspects of communication, synchronization and data movement besides the actual algorithmic solution. In order to enable the clear separation of these aspects [29] we have to separate the structure or shape of data structures from the actual data elements themselves. While the latter can be used to describe algorithmic aspects, the former enables us to derive the communication [27].In this paper we formalize the notion of shape and data elements and how they can be separated as well as used to reconstruct a valid data structure again for arbitrary container types, i.e. parameterized, nested algebraic data types and arrays. The shape function removes the data elements from the container type and replaces them by a dummy element * (or alternatively, their identifier). The data function extracts the list of data elements contained in the data structure; while a re_cover function can be used to reconstruct a data structure from its shape and a consistent list of data elements. All these functions can be characterized as special cases of a general traversal function operating on the data structure or its shape. We have used this as the semantical basis for handling overlapping data distributions not only over arrays as in commonly used approaches but over arbitrary (container) types.
Keywords
- Data Element
- Type List
- Shape Vector
- Container Type
- Algorithmic Skeleton
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Nitsche, T. (2007). Separation of Shape and Data. In: Sobh, T. (eds) Innovations and Advanced Techniques in Computer and Information Sciences and Engineering. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-6268-1_81
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DOI: https://doi.org/10.1007/978-1-4020-6268-1_81
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