Faster optimal parallel prefix sums and list ranking
Information and Computation
A bridging model for parallel computation
Communications of the ACM
Parallel algorithms for shared-memory machines
Handbook of theoretical computer science (vol. A)
An introduction to parallel algorithms
An introduction to parallel algorithms
List ranking and list scan on the Cray C-90
SPAA '94 Proceedings of the sixth annual ACM symposium on Parallel algorithms and architectures
Better trade-offs for parallel list ranking
Proceedings of the ninth annual ACM symposium on Parallel algorithms and architectures
Scalable Parallel Implementations of List Ranking on Fine-Grained Machines
IEEE Transactions on Parallel and Distributed Systems
Practical parallel list ranking
Journal of Parallel and Distributed Computing
Ultimate Parallel List Ranking?
HiPC '99 Proceedings of the 6th International Conference on High Performance Computing
Randomized Parallel List Ranking for Distributed Memory Multiprocessors
ASIAN '96 Proceedings of the Second Asian Computing Science Conference on Concurrency and Parallelism, Programming, Networking, and Security
Efficient Parallel Graph Algorithms For Coarse Grained Multicomputers and BSP
ICALP '97 Proceedings of the 24th International Colloquium on Automata, Languages and Programming
The complexity of parallel computations
The complexity of parallel computations
PRO: a model for the design and analysis of efficient and scalable parallel algorithms
Nordic Journal of Computing
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
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We present and analyze two portable algorithms for the List Ranking Problem in the Coarse Grained Multicomputer model (CGM). We report on implementations of these algorithms and experiments that were done with these on a variety of parallel and distributed architectures, ranging from PC clusters to a mainframe parallel machine. With these experiments, we validate the chosen CGM model, and also show the possible gains and limits of such algorithms.