The spatial join operations combine two sets of spatial data by their spatial relationships. They are among the most important, yet most time-consuming operations in spatial databases. We consider the problem of binar...
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The spatial join operations combine two sets of spatial data by their spatial relationships. They are among the most important, yet most time-consuming operations in spatial databases. We consider the problem of binary polygon intersection joins based on the filter-and-refine strategy. Our objective is to minimize the I/O cost and the response time for the refinement step. First, a graph model is proposed to formalize the refinement cost and matrix-based sequential data partition algorithms are introduced. Then a parallel data partitioning algorithm is developed with a detailed complexity analysis. Based on the data partition results, a distribution algorithm is also proposed for scheduling parallel spatial join processing.
This paper proposes a parallel framework to distributedly solve robust convex programs over networks when the constraints are affected by uncertainty. To this end, we adopt a probabilistic approach based on randomly s...
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ISBN:
(纸本)9781467386838
This paper proposes a parallel framework to distributedly solve robust convex programs over networks when the constraints are affected by uncertainty. To this end, we adopt a probabilistic approach based on randomly sampling the uncertainty to obtain a standard convex optimization, which is called the scenario problem. However, the number of samples to attain high levels of probabilistic guarantee of robustness may be large, which results in a large number of constraints in the scenario problem. Instead of using a single processor, we resort to multiple processors that are distributed among different nodes of a network. We study recursive algorithms which parallelize the computational task across the nodes and collaboratively solve the problem very effectively. Under local communication links, we show that each node asymptotically provides a solution to the scenario optimization problem.
A few parallel algorithms for solving triangular systems resulting from parallel factorization of sparse linear systems have been proposed and implemented recently. We present a detailed analysis of parallel complexit...
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A few parallel algorithms for solving triangular systems resulting from parallel factorization of sparse linear systems have been proposed and implemented recently. We present a detailed analysis of parallel complexity and scalability of the best of these algorithms and the results of its implementation on up to 256 processors of the Cray T3D parallel computer. It has been a common belief that parallel sparse triangular solvers are quite unscalable due to a high communication to computation ratio. Our analysis and experiments show that, although not as scalable as the best parallel sparse Cholesky factorization algorithms, parallel sparse triangular solvers can yield reasonable speedups in runtime on hundreds of processors. We also show that for a wide class of problems, the sparse triangular solvers described in this paper are optimal and are asymptotically as scalable as a dense triangular solver.
A prototyping approach for systolic algorithms is presented. While systolic algorithms are considered simpler and more straight-forward than the other forms of parallel algorithms, they have served the purpose of illu...
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A prototyping approach for systolic algorithms is presented. While systolic algorithms are considered simpler and more straight-forward than the other forms of parallel algorithms, they have served the purpose of illustrating the prototyping aspect of the proposed paradigm. The systolic model provides a notation for specifying both systolic computation and the architecture. The communication requirements are specified. A cost function is developed to be used for comparison analysis. It is shown how the proposed model and language constructs can be easily interfaced to a high-level parallel language.< >
Operations an digital signals similar to those employed for Z transforms are used for producing parallel multidimensional algorithms. All operations are given in the time domain and are part of the Unified Signal Alge...
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Operations an digital signals similar to those employed for Z transforms are used for producing parallel multidimensional algorithms. All operations are given in the time domain and are part of the Unified Signal Algebra. algorithms are given using dataflow type diagrams and are presented for the parallel specification of convolution, triple convolution, Volterra convolution, and correlation.
parallel processing is a promising technology to obtain on-line dynamic security assessment and real time simulation of power system. Many kinds of parallel computers have been used to implement the present algorithms...
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parallel processing is a promising technology to obtain on-line dynamic security assessment and real time simulation of power system. Many kinds of parallel computers have been used to implement the present algorithms. With the advantages of low cost, upgradability and scalability, PC cluster is attracting more and more attentions for parallel processing. In this paper, several parallel algorithms for transient stability simulation are discussed. The advantages and disadvantages of these algorithms on a PC cluster are of special concern. The parallel processing technologies discussed in the paper include network partition, linear equation solution, Newton type algorithms and some parallel-in-time algorithms.
We propose parallel algorithms for detecting collisions among 3D objects in real-time. First, a basic algorithm of serial version is described. It can detect potential collisions among multiple objects with arbitrary ...
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We propose parallel algorithms for detecting collisions among 3D objects in real-time. First, a basic algorithm of serial version is described. It can detect potential collisions among multiple objects with arbitrary motion (translation and rotation) in 3D space. The algorithm can be used without modification for both convex and concave objects represented as polyhedra. This algorithm is efficient, simple to implement, and does not require any memory intensive auxiliary data structure to be precomputed and updated. Then, two parallel algorithms are proposed for MIMD multi-processors having a shared-memory; one uses a static and the other uses a dynamic method for proper load balancing. Experimental results demonstrate the performance of the proposed collision detection methods.
parallel computation of manipulator forward dynamics is investigated. Considering the three classes of algorithms for the problem, the authors show that the O(n/sup 3/) algorithms are the most efficient for parallel c...
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parallel computation of manipulator forward dynamics is investigated. Considering the three classes of algorithms for the problem, the authors show that the O(n/sup 3/) algorithms are the most efficient for parallel computation. parallel algorithms for computation of inertia matrix, the bias vector, and the linear system solution are developed which can be efficiently implemented on a unique architecture, a triangular array of n(n+1)/2 processors with a nearest neighbor interconnection. This architecture is highly suitable for VLSI and WSI implementation.< >
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