Randomized permutations in a coarse grained parallel environment
Proceedings of the fifteenth annual ACM symposium on Parallel algorithms and architectures
PRO: a model for the design and analysis of efficient and scalable parallel algorithms
Nordic Journal of Computing
Efficient sampling of random permutations
Journal of Discrete Algorithms
Towards realistic implementations of external memory algorithms using a coarse grained paradigm
ICCSA'03 Proceedings of the 2003 international conference on Computational science and its applications: PartII
The parXXL environment: scalable fine grained development for large coarse grained platforms
PARA'06 Proceedings of the 8th international conference on Applied parallel computing: state of the art in scientific computing
Algorithm engineering: bridging the gap between algorithm theory and practice
Algorithm engineering: bridging the gap between algorithm theory and practice
Bounded arboricity to determine the local structure of sparse graphs
WG'06 Proceedings of the 32nd international conference on Graph-Theoretic Concepts in Computer Science
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We present a new parallel computation model that enables the design of resource-optimal scalable parallel algorithms and simplifies their analysis. The model rests on the novel idea of incorporating relative optimality as an integral part and measuring the quality of a parallel algorithm in terms of granularity.