We present the first molecular simulations of the vapor-liquid surface tension of quantum,liquids. The path integral formalism of Feynman was used to account for the quantum mechanical behavior of both the liquid and ...
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We present the first molecular simulations of the vapor-liquid surface tension of quantum,liquids. The path integral formalism of Feynman was used to account for the quantum mechanical behavior of both the liquid and the vapor. A replica-data parallel algorithm was implemented to achieve good parallel performance of the simulation code on at least 32 processors. We have computed the surface tension and the vapor-liquid phase diagram of pure hydrogen over the temperature range 18-30 K and pure deuterium from 19 to 34 K. The simulation results for surface tension and vapor-liquid orthobaric densities are in very good agreement with experimental data. We have computed the interfacial properties of hydrogen-deuterium mixtures over the entire concentration range at 20.4 and 24 K. The calculated equilibrium compositions of the mixtures are in excellent agreement with experimental data. The computed mixture surface tension shows negative deviations from ideal solution behavior,,in agreement with experimental data and predictions from Prigogine's theory. The magnitude of the deviations at 20.4 K are substantially larger from simulations and from theory than from experiments. We conclude that the experimentally measured mixture surface tension values are systematically too high. Analysis of the concentration profiles in the interfacial region shows that the nonideal behavior can be described entirely by segregation of H-2 to the interface, indicating that H-2 acts as a surfactant in H-2-D-2 mixtures. (C) 2004 American Institute of Physics.
Many approaches have been described for the parallel loop scheduling problem for shared-memory systems, but little work has been done on the data-dependent loop scheduling problem (nested loops with loop carried depen...
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Many approaches have been described for the parallel loop scheduling problem for shared-memory systems, but little work has been done on the data-dependent loop scheduling problem (nested loops with loop carried dependencies). In this paper, we propose a general model for the data-dependent loop scheduling problem on distributed as well as shared memory systems. In order to achieve load balancing and low runtime scheduling and communication overhead, our model is based on a loop task graph and the notion of critical path. In addition, we develop a heuristic algorithm based on our model and on genetic algorithms to test the reliability of the model. We test our approach on different scenarios and benchmarks. The results are very encouraging and suggest a future parallel compiler implementation based on our model. (C) 2004 Elsevier Inc. All rights reserved.
Clustering is a fundamental and important technique in image processing, pattern recognition, data compression, etc. However, most recent clustering algorithms cannot deal with large, complex databases and do not alwa...
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Clustering is a fundamental and important technique in image processing, pattern recognition, data compression, etc. However, most recent clustering algorithms cannot deal with large, complex databases and do not always achieve high clustering results. This paper proposes a parallel clustering algorithm for categorical and mixed data which can overcome the above problems. Our contributions are: (1) improving the k-sets algorithm [3] to achieve highly accurate clustering results;and (2) applying parallel techniques to the improved approach to achieve a parallel algorithm. Experiments on a CRAY T3E show that the proposed algorithm can achieve higher accuracy than previous attempts and can reduce processing time;thus, it is practical for use with very large and complex databases.
Watershed segmentation/transform is a classical method for image segmentation in gray scale mathematical morphology. Nevertheless watershed algorithm has strong recursive nature, so straightforward parallel one has a ...
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ISBN:
(纸本)3540233881
Watershed segmentation/transform is a classical method for image segmentation in gray scale mathematical morphology. Nevertheless watershed algorithm has strong recursive nature, so straightforward parallel one has a very low efficiency. Firstly, the advantages and disadvantages of some existing parallel algorithms are analyzed. Then, a Further Optimized parallel Watershed Algorithm (FOPWA) is presented based on boundary components graph. As the experiments show, FOPWA optimizes both running time and relative speedup, and has more flexibility.
Efficient one-step addition and subtraction algorithms are proposed. The arithmetic operations use the negabinary modified signed-digit number representation, which is an extension of the negabinary number system. Tru...
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Efficient one-step addition and subtraction algorithms are proposed. The arithmetic operations use the negabinary modified signed-digit number representation, which is an extension of the negabinary number system. Truth tables representing the required symbolic substitution computation rules are determined. Then a reduction of the number of the symbolic substitution computation rules is achieved by considering the digits in the lower positions of the negabinary modified signed digits in the computation. The proposed algorithms are very suitable for optical holographic and nonholographic implementations. An incoherent correlation based on the efficient shared content-addressable memory (SCAM) is suggested as an optoelectronics implementation. (C) 2004 Society of Photo-Optical Instrumentation Engineers.
The authors have proposed a parallel optical computation technique making effective use of available hardware resources without decreasing the processing efficiency in optical array logic (OAL), a paradigm for paralle...
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The authors have proposed a parallel optical computation technique making effective use of available hardware resources without decreasing the processing efficiency in optical array logic (OAL), a paradigm for parallel digital optical computing allowing arbitrary neighborhood logical operations. No methodology for effectively arranging numerical data in the input image in digital optical computing has been reported, and so the parallel algorithms in OAL proposed in past studies required large-scale hardware for their implementation. The proposed technique segments the data to be processed and regularly arranges them in several rows in the input images. The validity of the technique has been demonstrated by applying it to two parallel operations. The authors have verified the advantages of the proposed technique in terms of processing efficiency compared to the conventional technique. (C) 2004 Elsevier Inc. All rights reserved.
We provide a theoretical analysis of the communication requirements of parallel algorithms for molecular dynamic simulations. We describe two commonly used algorithms, space decomposition and force decomposition, and ...
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We provide a theoretical analysis of the communication requirements of parallel algorithms for molecular dynamic simulations. We describe two commonly used algorithms, space decomposition and force decomposition, and analyze their communication requirements;each is better in a distinct computation regime. We next introduce a new hybrid algorithm that further reduces communication. We show that the new algorithm is optimal, by providing a matching lower bound.
Dynamic programming is a widely applied algorithm design technique in many areas such as computational biology and scientific computing. Typical applications using this technique are compute-intensive and suffer from ...
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ISBN:
(纸本)3540241299
Dynamic programming is a widely applied algorithm design technique in many areas such as computational biology and scientific computing. Typical applications using this technique are compute-intensive and suffer from long runtimes on sequential architectures. Therefore, many parallel algorithms for both fine-grained and coarse-grained architectures have been introduced. However, the commonly used data partitioning scheme can not be efficiently applied to irregular dynamic programming applications, i.e. dynamic programming applications with an uneven computational load density. In this paper we present an efficient coarse-grained parallel algorithm for such kind of applications. This new algorithm can balance the load among processors using a tunable block-cyclic data partitioning scheme. We present a theoretical analysis and experimentally show that it leads to significant runtime savings for several irregular dynamic programming applications on PC clusters.
Smart antennas are becoming one of the promising technologies to meet the rapidly increasing demands for more capacity of satellite communication systems. A main component in a smart antenna system is beamforming. Bec...
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Smart antennas are becoming one of the promising technologies to meet the rapidly increasing demands for more capacity of satellite communication systems. A main component in a smart antenna system is beamforming. Because of the limitations of analog beamforming, digital beamforming will be employed in future satellite communication systems. We evaluate the performance of various digital beamforming strategies proposed in the literature for satellite communications: 1) single fixed beam/single user, 2) single fixed beam/multiple users, 3) single adaptive beam/single user, and 4) single Chebyshev dynamic beam/multiple users. Multiple criteria including coverage, system capacity, signal-to-interference-plus-noise ratio (SINR), and computation complexity are used to evaluate these satellite communication beamforming strategies. In particular, a Ka-band satellite communication system is used to address the various issues of these beamforming strategies. For the adaptive beamforming approach, subarray structure is used to obtain the weights of a large 2D antenna array, and a globally convergent recurrent neural network (RNN) is proposed to realize the adaptive beamforming algorithm in parallel. The new subarray-based neural beamforming algorithm can reduce the computation complexity greatly, and is more effective than the conventional least mean square (LMS) beamforming approach. It is shown that the single adaptive beam/single user approach has the highest system capacity.
The major portion of computing time in a computational fluid dynamic (CFD) flow solver is consumed by the solution of the linear equations, the use of an efficient matrix solver is critical to obtain high performance ...
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ISBN:
(纸本)1581138709
The major portion of computing time in a computational fluid dynamic (CFD) flow solver is consumed by the solution of the linear equations, the use of an efficient matrix solver is critical to obtain high performance flow solver. Currently, Algebraic Multi-Grid (AMG) is recognized in CFD community as one of the best choices to achieve high efficiency for solving linear algebraic equations. As AMG is increasingly used for large problems, its parallelization is highly demanded. In this paper, a parallel algorithm for AMG is proposed with regard to unique characteristics of partial differential equations governing fluid flows. An implicit block coupling method was developed and implemented into AMG. The parallelized AMG has demonstrated excellent performance on many test cases. Copyright 2004 ACM.
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