Interconnection networks for large-scale parallel processing: theory and case studies
Interconnection networks for large-scale parallel processing: theory and case studies
On permutations passable by the Gamma network
Journal of Parallel and Distributed Computing
Performance and fault tolerance improvements in the Inverse Augmented Data Manipulator network
ISCA '82 Proceedings of the 9th annual symposium on Computer Architecture
The Gamma network: A multiprocessor interconnection network with redundant paths
ISCA '82 Proceedings of the 9th annual symposium on Computer Architecture
Study of multistage SIMD interconnection networks
ISCA '78 Proceedings of the 5th annual symposium on Computer architecture
Banyan networks for partitioning multiprocessor systems
ISCA '73 Proceedings of the 1st annual symposium on Computer architecture
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A “state model” is proposed for solving the problem of routing and rerouting messages in the Inverse Augmented Data Manipulator (IADM) network. Using this model, necessary and sufficient conditions for the reroutability of messages are established, and then destination tag schemes are derived. These schemes are simpler, more efficient and require less complex hardware than previously proposed routing schemes. Two destination tag schemes are proposed. For one of the schemes, rerouting is totally transparent to the sender of the message and any blocked link of a given type can be avoided. Compared with previous works that deal with the same type of blockage, the timexspace complexity is reduced from &Ogr;(logN) to &Ogr;(1). For the other scheme, rerouting is possible for any type of link blockage. A universal rerouting algorithm is constructed based on the second scheme, which finds a blockage-free path for any combination of multiple blockages if there exists such a path, and indicates absence of such a path if there exists none. In addition, the state model is used to derive constructively a lower bound on the number of subgraphs which are isomorphic to the Indirect Binary n-Cube network in the IADM network. This knowledge can be used to characterize properties of the IADM networks and for permutation routing in the IADM networks.