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The Owner Concept for PRAMs
, 1991
"... We analyze the owner concept for PRAMs. In OROWPRAMs each memory cell has one distinct processor that is the only one allowed to write into this memory cell and one distinct processor that is the only one allowed to read from it. By symmetric pointer doubling, a new proof technique for OROWPRAMs, ..."
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Cited by 17 (5 self)
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We analyze the owner concept for PRAMs. In OROWPRAMs each memory cell has one distinct processor that is the only one allowed to write into this memory cell and one distinct processor that is the only one allowed to read from it. By symmetric pointer doubling, a new proof technique for OROWPRAMs, it is shown that list ranking can be done in O(log n) time by an OROWPRAM and that LOGSPACE ` OROWTIME(log n). Then we prove that OROWPRAMs are a fairly robust model and recognize the same class of languages when the model is modified in several ways and that all kinds of PRAMs intertwine with the NC hierarchy without timeloss. Finally it is shown that EREWPRAMs can be simulated by OREWPRAMs and ERCWPRAMs by ORCWPRAMs. 3 This research was partially supported by the Deutsche Forschungsgemeinschaft, SFB 342, Teilprojekt A4 "Klassifikation und Parallelisierung durch Reduktionsanalyse" y Email: rossmani@lan.informatik.tumuenchen.dbp.de Introduction Fortune and Wyllie introduced in...
Dynamic parallel tree contraction
 In Proceedings 5th Annual ACM Symp. on Parallel Algorithms and Architectures
, 1994
"... Parallel tree contraction has been found to be a useful and quite powerful tool for the design of a wide class of efficient graph algorithms. We propose a corresponding technique for the parallel solution of problems with incremental changes in the data. In dynamic tree contraction problems, we are ..."
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Cited by 15 (2 self)
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Parallel tree contraction has been found to be a useful and quite powerful tool for the design of a wide class of efficient graph algorithms. We propose a corresponding technique for the parallel solution of problems with incremental changes in the data. In dynamic tree contraction problems, we are given an initial tree T, and then an online algorithm processes requests regarding T. Requests may be either incremental changes to T or requests for certain values computed using the tree. A simple example is maintaining the preorder numbering on a tree. The online algorithm would then have to handle incremental changes to the tree, and would also have to quickly answer queries about the preorder number of any tree node. Our dynamic algorithms are based on the prior parallel tree contraction algorithms, and hence we call such algorithms incremental tree contraction algorithms. By maintaining the connection between our incremental algorithms and the parallel tree contraction algorithm, we create incremental algorithms for tree contraction. We consider a dynamic binary tree T of ≤ n nodes and unbounded depth. We describe a procedure, which we call the dynamic parallel tree contraction algorithm, which incrementally processes various parallel modification requests and queries: (1) parallel requests to add or delete leaves of T, or modify labels of internal nodes or leaves of T, and also (2) parallel tree contraction queries which require recomputing values at specified nodes. Each modification or query is with respect to a set of nodes U in T. We make use of a random splitting tree as an aid
Extended Fibonacci cubes
 IEEE Trans. Parallel Distr. Systems
, 1997
"... Abstract—The Fibonacci Cube is an interconnection network that possesses many desirable properties that are important in network design and application. The Fibonacci Cube can efficiently emulate many hypercube algorithms and uses fewer links than the comparable hypercube, while its size does not in ..."
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Cited by 15 (2 self)
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Abstract—The Fibonacci Cube is an interconnection network that possesses many desirable properties that are important in network design and application. The Fibonacci Cube can efficiently emulate many hypercube algorithms and uses fewer links than the comparable hypercube, while its size does not increase as fast as the hypercube. However, most Fibonacci Cubes (more than 2/3 of all) are not Hamiltonian. In this paper, we propose an Extended Fibonacci Cube (EFC 1) with an even number of nodes. It is defined based on the same sequence F(i) = F(i 1) + F(i 2) as the regular Fibonacci sequence; however, its initial conditions are different. We show that the Extended Fibonacci Cube includes the Fibonacci Cube as a subgraph and maintains its sparsity property. In addition, it is Hamiltonian and is better in emulating other topologies. Specifically, the Extended Fibonacci Cube can embed binary trees more efficiently than the regular Fibonacci Cube and is almost as efficient as the hypercube, even though the Extended Fibonacci Cube is a much sparser network than the hypercube. We also propose a series of Extended Fibonacci Cubes with even number of nodes. Any Extended Fibonacci Cube (EFC k, with k ≥ 1) in the series contains the node set of any other cube that precedes EFC k in the series. We show that any Extended Fibonacci Cube maintains virtually all the desirable properties of the Fibonacci Cube. The EFC k s can be considered as flexible versions of incomplete hypercubes, which eliminates their restriction on the number of nodes, and, thus, makes it possible to construct parallel machines with arbitrary sizes. Index Terms—Fibonacci numbers, Hamiltonian graphs, graph embedding, hypercubes, interconnection topologies.
A library of constructive skeletons for sequential style of parallel programming
 In InfoScale ’06: Proceedings of the 1st international conference on Scalable information systems, volume 152 of ACM International Conference Proceeding Series
, 2006
"... With the increasing popularity of parallel programming environments such as PC clusters, more and more sequential programmers, with little knowledge about parallel architectures and parallel programming, are hoping to write parallel programs. Numerous attempts have been made to develop highlevel pa ..."
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Cited by 14 (8 self)
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With the increasing popularity of parallel programming environments such as PC clusters, more and more sequential programmers, with little knowledge about parallel architectures and parallel programming, are hoping to write parallel programs. Numerous attempts have been made to develop highlevel parallel programming libraries that use abstraction to hide lowlevel concerns and reduce difficulties in parallel programming. Among them, libraries of parallel skeletons have emerged as a promising way towards this direction. Unfortunately, these libraries are not well accepted by sequential programmers, because of incomplete elimination of lowerlevel details, adhoc selection of library functions, unsatisfactory performance, or lack of convincing application examples. This paper addresses principle of designing skeleton libraries of parallel programming and reports implementation details and practical applications of a skeleton library SkeTo. The SkeTo library is unique in its feature that it has a solid theoretical foundation based on the theory of Constructive Algorithmics, and is practical to be used to describe various parallel computations in a sequential manner. 1.
Linear Time Algorithm to Recognize Clustered Planar Graphs and its Parallelization
 98, 3rd Latin American symposium on theoretical informatics
, 1998
"... We develop a linear time algorithm for the following problem: Given a graph G and a hierarchical clustering of the vertices, such that all clusters induce connected subgraphs, determine whether G can be embedded into the plane, such that no cluster has a hole. This is an improvement to the O(n 2 )a ..."
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Cited by 14 (0 self)
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We develop a linear time algorithm for the following problem: Given a graph G and a hierarchical clustering of the vertices, such that all clusters induce connected subgraphs, determine whether G can be embedded into the plane, such that no cluster has a hole. This is an improvement to the O(n 2 )algorithm of Q.W. Feng et al. [6] and the algorithm of Lengauer [12].
Sorting on a parallel pointer machine with applications to set expression evaluation
 Proc. 29th IEEE Symp. on Foundations of Computing
, 1989
"... ..."
Optimal Routing of Parentheses on the Hypercube
 IN PROCEEDINGS OF THE SYMPOSIUM ON PARALLEL ARCHITECTURES AND ALGORITHMS
, 1994
"... We consider a new class of routing requests or partial permutations for which we give optimal online routing algorithms on the hypercube and shuffleexchange network. For wellformed words of parentheses our algorithm establishes communication between all matching pairs in logarithmic time. It can ..."
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Cited by 14 (6 self)
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We consider a new class of routing requests or partial permutations for which we give optimal online routing algorithms on the hypercube and shuffleexchange network. For wellformed words of parentheses our algorithm establishes communication between all matching pairs in logarithmic time. It can be applied to the membership problem for Dyck languages and a number of problems for algebraic expressions.
Parallel and Distributed Finite Constraint Satisfaction: Complexity, Algorithms and Experiments
, 1992
"... This paper explores the parallel complexity of finite constraint satisfaction problems (FCSPs) by developing three algorithms for deriving minimal constraint networks in parallel. The first is a parallel algorithm for the EREW PRAM model, the second is a distributed algorithm for finegrain intercon ..."
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Cited by 12 (1 self)
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This paper explores the parallel complexity of finite constraint satisfaction problems (FCSPs) by developing three algorithms for deriving minimal constraint networks in parallel. The first is a parallel algorithm for the EREW PRAM model, the second is a distributed algorithm for finegrain interconnected networks, and the third is a distributed algorithm for coarsegrain interconnected networks. Our major results are: given an FCSP represented by an acyclic constraint network (or a join tree) of size n with treewidth bounded by a constant, then (1) the parallel algorithm takes O(log n) time using O(n) processors, (2) there is an equivalent network, of size poly(n) with treewidth also bounded by a constant, which can be solved by the finegrain distributed algorithm in O(log n) time using poly(n) processors and (3) the distributed algorithm for coarsegrain interconnected networks has linear speedup and linear scaleup. In addition, we have simulated the finegrain distributed algorit...