First, we present two classes of sequential algorithms for minimum flow problem: decreasing path algorithms and preflow algorithms. Then we describe another approach of the minimum flow problem, that consists of apply...
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First, we present two classes of sequential algorithms for minimum flow problem: decreasing path algorithms and preflow algorithms. Then we describe another approach of the minimum flow problem, that consists of applying any maximum flow algorithm in a modified network. In section 5 we present several parallel preflow algorithms that solve the minimum flow problem. Finally, we present an application of the minimum flow problem.
We present parallel algorithms for constructing and maintaining balancedm-way search trees. These parallel algorithms have time complexity O(1) for ann processors configuration. The formal correctness of the algorithm...
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We present parallel algorithms for constructing and maintaining balancedm-way search trees. These parallel algorithms have time complexity O(1) for ann processors configuration. The formal correctness of the algorithms is given in detail.
The random-adversary technique is a general method for proving lower bounds on randomized parallel algorithms. The bounds apply Do the number of communication steps, and they apply regardless of the processors' in...
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The random-adversary technique is a general method for proving lower bounds on randomized parallel algorithms. The bounds apply Do the number of communication steps, and they apply regardless of the processors' instruction sets, the lengths of messages, etc. This paper introduces the random-adversary technique and shows how it can be used to obtain lower bounds on randomized parallel algorithms for load balancing, compaction, padded sorting, and finding Hamiltonian cycles in random graphs. Using the random-adversary technique, we obtain the first lower bounds for randomized parallel algorithms which are provably faster than their deterministic counterparts (specifically, for load balancing and related problems).
This paper introduces a model for parallel computation, called thedistributed randomaccess machine (DRAM), in which the communication requirements of parallel algorithms can be evaluated. A DRAM is an abstraction of a...
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This paper introduces a model for parallel computation, called thedistributed randomaccess machine (DRAM), in which the communication requirements of parallel algorithms can be evaluated. A DRAM is an abstraction of a parallel computer in which memory accesses are implemented by routing messages through a communication network. A DRAM explicitly models the congestion of messages across cuts of the network.
A family of intervals on the real line provides a natural model for a vast number of scheduling and VLSI problems. Recently, a number of parallel algorithms to solve a variety of practical problems on such a family of...
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A family of intervals on the real line provides a natural model for a vast number of scheduling and VLSI problems. Recently, a number of parallel algorithms to solve a variety of practical problems on such a family of intervals have been proposed in the literature. We develop computational tools and show how they can be used for the purpose of devising cost-optimal parallel algorithms for a number of interval-related problems including finding a largest subset of pairwise nonoverlapping intervals, a minimum dominating subset of intervals, along with algorithms to compute the shortest path between a pair of intervals and, based on the shortest path, a parallel algorithm to find the center of the family of intervals. More precisely, with an arbitrary family of n intervals as input, all our algorithms run in O(log n) time using O(n) processors in the EREW-PRAM model of computation.
A probabilistic model of a class of parallel programs is used to investigate the counterintuitive behavior observed for some parallel algorithms. Two main points are made: 1) it may, in general, be beneficial to consi...
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A probabilistic model of a class of parallel programs is used to investigate the counterintuitive behavior observed for some parallel algorithms. Two main points are made: 1) it may, in general, be beneficial to consider using more logical processes than physical processors in a parallel algorithm; and 2) results from order statistics are useful tools in analyzing parallel systems.
In this note some comments on the paper: D.J. Evans and Shao-wen Mai, Two parallel algorithms for the convex hull problem in a two dimensional space, parallel Computing2 (1985) 313–326 are given.
In this note some comments on the paper: D.J. Evans and Shao-wen Mai, Two parallel algorithms for the convex hull problem in a two dimensional space, parallel Computing2 (1985) 313–326 are given.
Yen-Cook, Miranker-Liniger, and Stability Region Placement (SRP) methods are evaluated on the basis of their applicability to real-time simulation using the VU68K-HDG parallel processing system as the target machine. ...
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Yen-Cook, Miranker-Liniger, and Stability Region Placement (SRP) methods are evaluated on the basis of their applicability to real-time simulation using the VU68K-HDG parallel processing system as the target machine. Because of stability-computation time trade-offs, the SRP method, which allows selection of step size independent of the system eigenvalue, is shown to be the only one of the three methods applicable to the real-time simulation of a particular stiff, nonlinear, third-order test system.
The article presents a mathematical research which develops and examines the properties of high-performance smoothed particles hydrodynamics (SPH)-based algorithms for solving continuum mechanics problems in central p...
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The article presents a mathematical research which develops and examines the properties of high-performance smoothed particles hydrodynamics (SPH)-based algorithms for solving continuum mechanics problems in central processing unit (CPU) and hybrid architectures. Details include the advantages of using SPH, estimates of errors in the case of SPH-type approximations, and the selection of parameters. Also mentioned are parallel algorithms for SPH, including the computation time and acceleration.
We present a sequential and a parallel algorithm to solve the maximum-weight independent set problem on a permutation graph. Our input data is a permutation pi = [pi1, pi2,...,pi(n)] and the weights of these vertices....
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We present a sequential and a parallel algorithm to solve the maximum-weight independent set problem on a permutation graph. Our input data is a permutation pi = [pi1, pi2,...,pi(n)] and the weights of these vertices. our sequential algorithm takes O(n log log n) time and our parallel algorithm is of O(log2n) time and O(n3/(log n)) processors under the CREW PRAM model.
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