A number of recent studies have revealed that the Optical Transpose Interconnection Systems (or OTIS) are promising candidates for future high-performance parallel computers. In this paper, we present and evaluate two...
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A number of recent studies have revealed that the Optical Transpose Interconnection Systems (or OTIS) are promising candidates for future high-performance parallel computers. In this paper, we present and evaluate two general methods for algorithm development on the OTIS. The proposed methods are general in the sense that no specific factor network or problem domain is assumed. The proposed methods allow efficient mapping of a wide class of algorithms into the OTIS. These methods are based on grids and pipelines as popular structures that support a vast body of parallel applications including linear algebra, divide-and-conquer type of algorithms, sorting, and FFT computation. Timing models for measuring the performance of the proposed methods are also provided. Through these models, the performance of various algorithms on the OTIS are evaluated and compared with their counterparts on conventional electronic interconnection systems. This study confirms the viability of the OTIS as an attractive alternative for large-scale parallel architectures. Finally, we show how the proposed methods can be used to design parallel algorithms for linear algebra on the OTIS.
The kernel function of the Diffie-Hellman (DH) protocol is a modular exponentiation over finite field with high computational complexity. In this paper, we propose a novel key generation algorithm for DH agreement tha...
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Estimation of Distribution algorithms (EDAs) are a set of optimization techniques that have been successfully applied to different kinds of problems. In this paper, we deal with the creation of multivariate calibratio...
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Correct prediction of reservoir performance is an important issue for future reservoir development and exploration planning. This leads to the problem of accurate estimation of the rock and fluid properties used in co...
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Correct prediction of reservoir performance is an important issue for future reservoir development and exploration planning. This leads to the problem of accurate estimation of the rock and fluid properties used in construction of reservoir model. Usually, prior reservoir models are built by means of geostatistical algorithms and static information obtained from different sources and then adjusted for better production history matching. However, in some cases, static data can be very sparse and insufficient or misleading, for meaningful prior model construction. Under this scenario, adjusting of prior model would lead to a local minimum in automatic history matching algorithms and hence we will obtain results which may not be meaningful. This paper presents a new methodology for parallel Production data Processing. It assumes limited prior knowledge about static information and emphasizes the importance of production data. This methodology begins with several initial models which represent large amount of uncertainty in prior models. The method goes through a process of cyclic parallel adjustment of multiple reservoir models and reliable information gathering for further models improvements. The algorithm seeks common trends developed in the prior models as a result of partial incorporation of production data. Every generation of multiple models is adjusted by means of gradient-based automatic history matching technique. By using common trends gathered from prior models, new set of solutions (alternate realizations) are proposed. Generalized Changes Map (GCM) is the main source of information in this method. It provides the trend of common changes for all the models and serves as a basis of reliable parameters and spatial relationship selection. GCM captures two types of information: maximum changes occurred in prior model due to integration of production data, and convergence of values towards a common value in multiple models. These parameters form Generalized Dis
We investigate the numerical solution of stable Sylvester equations via iterative schemes proposed for computing the sign function of a matrix. In particular, we discuss how the rational iterations for the matrix sign...
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We investigate the numerical solution of stable Sylvester equations via iterative schemes proposed for computing the sign function of a matrix. In particular, we discuss how the rational iterations for the matrix sign function can efficiently be adapted to the special structure implied by the Sylvester equation. For Sylvester equations with factored constant term as those arising in model reduction or image restoration, we derive an algorithm that computes the solution in factored form directly. We also suggest convergence criteria for the resulting iterations and compare the accuracy and performance of the resulting methods with existing Sylvester solvers. The algorithms proposed here are easy to parallelize. We report on the parallelization of those algorithms and demonstrate their high efficiency and scalability using experimental results obtained on a cluster of Intel Pentium Xeon processors.
A parallel LOD algorithms for solving the 3D problem with nonlocal boundary condition is considered. The algorithm is implemented using the parallel array object tool ParSol, then a parallel algorithm follows semi-aut...
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Biclustering refers to simultaneously capturing correlations present among subsets of attributes (columns) and records (rows). It is widely used in data mining applications including biological data analysis, financia...
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Analyzing gene expression patterns is becoming a highly relevant task in the Bioinformatics area. This analysis makes it possible to determine the behavior patterns of genes under various conditions, a fundamental inf...
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This paper presents a chaos-based encryption algorithm for colors images in spatial domain. Firstly, the pretreatment is carried out to three chaotic sequences produced by Lorenz system. Then these sequences are used ...
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Clustering analysis plays an important role in scientific research and commercial application. K-means algorithm is a widely used partition method in clustering. As the dataset's scale increases rapidly, it is dif...
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