Sparse approximate inverse (SAI) techniques have recently emerged as a new class of parallel preconditioning techniques for solving large sparse linear systems on highperformancecomputers. The choice of the sparsity...
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The gene section ordering on solving traveling salesman problems is analyzed by numerical experiments. Some improved crossover operations are presented. Several combinations of genetic operations are examined and the ...
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The gene section ordering on solving traveling salesman problems is analyzed by numerical experiments. Some improved crossover operations are presented. Several combinations of genetic operations are examined and the functions of these operations are analyzed. The essentiality of the ordering of the gene section and the significance of the evolutionary inversion operation are discussed. Some results and conclusions are obtained and given, which provide useful information for the implementation of the genetic operations for solving the traveling salesman problem.
The optimization of agents' initial properties enables agents to perform their assigned tasks more perfectly. This paper presents an optimizing method using the combination of radial basis function (RBF) neural ne...
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The optimization of agents' initial properties enables agents to perform their assigned tasks more perfectly. This paper presents an optimizing method using the combination of radial basis function (RBF) neural network and genetic algorithm (GA). In the land combat simulation, the method can ensure that the agents optimized defeat the agents not optimized absolutely. Compared with the optimization based on support vector machines (SVM), the proposed method improves the efficiency more than twenty times, so it suits the cases where the speed as well as performance is required.
We investigate the use of the multistep successive preconditioning strategies (MSP) to construct a class of parallel multilevel sparse approximate inverse (SAI) preconditioners. We do not use independent set ordering,...
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We present a class of parallel preconditioning strategies built on a multilevel block incomplete LU (ILU)factorization technique to solve large sparse linear systems on distributed memory parallel computers. The preco...
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The increasing complexity, heterogeneity and dynamism of networks, systems, services applications have made our computational/information infrastructure brittle, unmanageable and insecure. This has necessitated the in...
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Photonic band-gap (PBG) materials are periodic dielectric crystals that exhibit a photonic band-gap analogous to the electronic band-gap present in semiconductors. Their fabrication, however, requires extremely high r...
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Gossip protocols and services provide a means by which failures can be detected in large, distributed systems in an asynchronous manner without the limits associated with reliable multicasting for group communications...
Gossip protocols and services provide a means by which failures can be detected in large, distributed systems in an asynchronous manner without the limits associated with reliable multicasting for group communications. Extending the gossip protocol such that a system reaches consensus on detected faults can be performed via a flat structure, or it can be hierarchically distributed across cooperating layers of nodes. In this paper, the performance of gossip services employing flat and hierarchical schemes is analyzed on an experimental testbed in terms of consensus time, resource utilization and scalability. performance associated with a hierarchically arranged gossip scheme is analyzed with varying group sizes and is shown to scale well. Resource utilization of the gossip-style failure detection and consensus service is measured in terms of network bandwidth utilization and CPU utilization. Analytical models are developed for resource utilization and performance projections are made for large system sizes.
Advanced sonar algorithms with complex-wave propagation processing are of critical importance in the field of acoustic signal processing, as they possess the ability to localize signal sources more precisely in a clut...
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Advanced sonar algorithms with complex-wave propagation processing are of critical importance in the field of acoustic signal processing, as they possess the ability to localize signal sources more precisely in a cluttered environment. Conventional Matched-Field Processing (CMFP) is such an algorithm that provides range and depth results by employing environmental parameters. However, the enhancement of features is limited by the extensive computational and memory requirements of the algorithm even for a small problem size. high-performancecomputing, when applied to matched-field processing on a microprocessor-based distributed system, can provide solutions for this challenging problem in performance, scalability, and cost. Based on domain decomposition techniques, two parallel algorithms for matched-field processing are introduced in this paper for in-situ signal processing. performance results collected on low-power distributed embedded systems are presented in terms of execution times and parallel efficiencies. The results of these analyses demonstrate that parallel in-situ processing holds the potential to meet the needs of matched-field processing in a scalable fashion.
A new hybrid finite-difference time-domain-mixed potential integral equation method (FDTD-MPIE) is proposed for modeling and simulating multilayer planar problems with possible locally inhomogeneous geometries. By usi...
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