Routing, merging, and sorting on parallel models of computation
Journal of Computer and System Sciences
Optimal parallel merging and sorting without memory conflicts
IEEE Transactions on Computers
A complexity theory of efficient parallel algorithms
Theoretical Computer Science - Special issue: Fifteenth international colloquium on automata, languages and programming, Tampere, Finland, July 1988
Optimal merging and sorting on the EREW PRAM
Information Processing Letters
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Parallel algorithms for shared-memory machines
Handbook of theoretical computer science (vol. A)
Information and Computation
An optimal parallel algorithm for merging using multiselection
Information Processing Letters
Parallel computation: models and methods
Parallel computation: models and methods
The art of computer programming, volume 3: (2nd ed.) sorting and searching
The art of computer programming, volume 3: (2nd ed.) sorting and searching
Parallel Computing
Parallel Sorting Algorithms
Merging, sorting and matrix operations on the SOME-bus multiprocessor architecture
Future Generation Computer Systems - Special issue: Advanced services for clusters and internet computing
Recognizing and representing proper interval graphs in parallel using merging and sorting
Discrete Applied Mathematics
Merging data records on EREW PRAM
ICA3PP'10 Proceedings of the 10th international conference on Algorithms and Architectures for Parallel Processing - Volume Part II
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In this paper, we study the merging of two sorted arrays $A=(a_{1},a_{2},\ldots, a_{n_{1}})$ and $B=(b_{1},b_{2},\ldots,b_{n_{2}})$ on EREW PRAM with two restrictions: (1) The elements of two arrays are taken from the integer range [1,n], where n=Max(n 1,n 2). (2) The elements are taken from either uniform distribution or non-uniform distribution such that $\#\{a\in A\,\mbox{and}\,b\in B\,\mbox{s.t.}\,a,b\in [(i-1)\frac{n}{p}+1,i\,\frac{n}{p}]\}=O(\frac{n}{p})$ , for 1驴i驴p驴(number of processors). We give a new optimal deterministic algorithm runs in $O(\frac{n}{p})$ time using p processors on EREW PRAM. For $p=\frac{n}{\log^{(g)}{n}}$ ; the running time of the algorithm is O(log驴(g) n) which is faster than the previous results, where log驴(g) n=log驴log驴(g驴1) n for g1 and log驴(1) n=log驴n. We also extend the domain of input data to [1,n k ], where k is a constant.