The fundamental problem of minimum computation has several well-studied generalizations such as prefix minima, range minima, and all nearest smaller values computations. Recent papers introduced parallel algorithms fo...
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The fundamental problem of minimum computation has several well-studied generalizations such as prefix minima, range minima, and all nearest smaller values computations. Recent papers introduced parallel algorithms for these problems when the n input elements are given from an integer domain [1..s], obtaining O(lglglgs) running time and linear work for s ≥ n. However, most of these algorithms have the running time of O(lglglgn) (rather than O(lglglgs)) for all values of s &le n, except for the case of s = O(1) in which case the running time is O(α(n)). In this paper we focus on the range s &le n and provide linear-work algorithms whose running time is O(lglglgs + f(n)) for all s ≥ 0, where f(n) is either one of the slow growing functions lg*n or α(n). We show how to generalize our algorithms to the case that the domain size s is unknown, with the same complexities. (All previous algorithms work only under the assumption that the domain size s is known.) Moreover, we make our algorithms output-sensitive with the lglglgs term replaced by lglglgM, where M is the maximum input value. In fact, for the minimum computation problem the running time is O(lglglgm) for all s ≥ 0, where m is the minimum input value.
The Boolean circuit has been an important model of parallel computation, but not many parallel algorithms have been designed on this model because it is 'awkward to program.' To overcome this drawback, we prop...
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The Boolean circuit has been an important model of parallel computation, but not many parallel algorithms have been designed on this model because it is 'awkward to program.' To overcome this drawback, we propose a description language for designing parallel algorithms on the Boolean circuit. This description language is to parallel algorithms what the pseudo-code is to sequential algorithms. Through example codes, we show that the description language is a convenient tool to design parallel algorithms due to its general iterative and recursive structures and the ease of modular design. (C) 2009 Elsevier B.V. All rights reserved.
This review study gives a widespread overview of the solutions of several engineering problems based on some multidimensional partial differential equations like parabolic, hyperbolic, elliptic, AGE, IADE equations. D...
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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 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.
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