FAFNER is code developed by Lister which simulates by Monte Carlo methods the Neutral Beam Injection (NBI) technology, one of the most extended heating methods for fusion devices. To the date, FAFNER has been usually ...
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ISBN:
(纸本)9780769539393
FAFNER is code developed by Lister which simulates by Monte Carlo methods the Neutral Beam Injection (NBI) technology, one of the most extended heating methods for fusion devices. To the date, FAFNER has been usually run at CIEMAT adapted to the TJ-II helical axis stellarator on shared memory Cray architecture machines. From this version, FAFNER has been ported to the Grid in the framework of the EGEE Project. At the same time, this work is the first step of a more ambitious target since the code can be now coupled to many others such as ion transport tools. In this paper, all these preliminary advances are described as well as the performance and portability gains obtained.
The Adaptive Mesh Refinement is one of the main techniques used for the solution of Partial Differential Equations. Since 3-dimensional structures are more complex, there are few refinement methods especially for para...
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ISBN:
(纸本)0769526373
The Adaptive Mesh Refinement is one of the main techniques used for the solution of Partial Differential Equations. Since 3-dimensional structures are more complex, there are few refinement methods especially for parallel environments. On the other hand, many algorithms have been proposed for 2-dimensional structures. We analyzed the Rivara's longest-edge bisection algorithm, studied parallelization techniques for the problem, and presented a parallel methodology for the refinement of non-uniform tetrahedral meshes. The main goal of this research is to propose a practical refinement framework for real-life applications. We describe a usable data structure for distributed environments and present a utility capable of distributing the mesh data among processors to solve large mesh structures.
Deciding privacy-type properties of deterministic cryptographic protocols such as anonymity and strong secrecy can be reduced to deciding the symbolic equivalence of processes, where each process is described by a set...
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ISBN:
(纸本)9781728165820
Deciding privacy-type properties of deterministic cryptographic protocols such as anonymity and strong secrecy can be reduced to deciding the symbolic equivalence of processes, where each process is described by a set of possible symbolic traces. This equivalence is parameterized by a deduction system that describes which actions and observations an intruder can perform on a running system. We present in this paper a notion of finitary deduction systems. For this class of deduction system, we first reduce the problem of the equivalence of processes with no disequations to the resolution of reachability problem on each symbolic trace of one process, and then testing whether each solution found is solution of a related trace in the other process. We then extend this reduction to the case of generic deterministic finite processes in which symbolic traces may contain disequalities.
In many numerical applications resulting from computational science and engineering problems, the solution of sparse linear systems is the most prohibitively compute intensive task. Consequently, the linear solvers ne...
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ISBN:
(纸本)9780769539393
In many numerical applications resulting from computational science and engineering problems, the solution of sparse linear systems is the most prohibitively compute intensive task. Consequently, the linear solvers need to be carefully chosen and efficiently implemented in order to harness the available computing resources. Krylov subspace based iterative solvers have been widely used for solving large systems of linear equations. In this paper, we focus on the design of such iterative solvers to take advantage of massive parallelism of general purpose Graphics processing Units (GPU)s. We will consider Stabilized BiConjugate Gradient (BiCGStab) and Conjugate Gradient Squared (CGS) methods for the solutions of sparse linear systems with unsymmetric coefficient matrices. We discuss data structures and efficient implementation of these solvers on the NVIDIA's CUDA platform. We evaluate scalability and performance of our implementations in the context of a financial engineering problem of solving multidimensional option pricing PDEs using sparse grid combination technique.
Nowadays, image compression is one of the essential tool used in the digital imageprocessing. Data compression is used to minimize the quantity as well as undesirable information while maintaining useful information ...
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ISBN:
(纸本)9781509036691
Nowadays, image compression is one of the essential tool used in the digital imageprocessing. Data compression is used to minimize the quantity as well as undesirable information while maintaining useful information within the image. Transform based compression can be broadly used by image compression. However, transform based techniques expose obstructing artifacts within the resultant image. This paper recommends the new technique as a way to minimize obstructing artifacts within compressed image. The latest technique combined SVD-WDR compression along with Gradient-based optimization method for removing blocking and ringing artifacts from the compressed images.
The proceedings contain 74 papers. The topics discussed include: web services composition for distributed data mining;how to run scientific applications over web services;resource management services for a grid analys...
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The proceedings contain 74 papers. The topics discussed include: web services composition for distributed data mining;how to run scientific applications over web services;resource management services for a grid analysis environment;a comparison of two methods for building astronomical image mosaics on a grid;gene sequence alignment on a public computing platform;parallel module network learning on distributed memory multiprocessors;a scalable parallel poisson solver in three dimensions with infinite-domain boundary conditions;factoring solution sets of polynomial systems in parallel;a programmable array processor architecture for flexible approximate string matching algorithms;and speculative parallel threading architecture and compilation.
The processing of three-dimensional (3-D) objects from 3-D digital image data is an important task in the imageprocessing and the computer vision fields. The distance transform (DT) is extensively applied in the imag...
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ISBN:
(纸本)0780378407
The processing of three-dimensional (3-D) objects from 3-D digital image data is an important task in the imageprocessing and the computer vision fields. The distance transform (DT) is extensively applied in the imageprocessing and computer vision areas as a key operation. In a two or three-dimensional image array, the computation of distance transform (DT) is an important task, With the increasing application of 3D voxel images, it is useful to consider the distance transform of a 3D digital image array. In order to provide the efficient transform computations, parallelism is employed. We develop parallel algorithms for the three-dimensional Euclidean distance transform (3D-EDT) on the SIMD hypercube computer. The time complexity of our parallel algorithm is O{log(2) N} for an N x N x N image array using N-3 processors. A generalized parallel algorithm for the 3D-EDT is also proposed and it runs O((N/p)(3) log(N) + (N/p)(2) log(2) p) time for an N x N x N binary image array on the SIMD hypercube computer using p(3) PE's, where 1 less than or equal to p less than or equal to N.
For the solutions of large and sparse linear systems of equations with unsymmetric coefficient matrices, we propose an improved version of the Conjugate Gradient Squared method (ICGS) method. The algorithm is derived ...
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ISBN:
(纸本)0769512607
For the solutions of large and sparse linear systems of equations with unsymmetric coefficient matrices, we propose an improved version of the Conjugate Gradient Squared method (ICGS) method. The algorithm is derived such that all inner products, matrix-vector multiplications and vector updates of a single iteration step are independent and communication time required for inner product can be overlapped efficiently with computation time of vector updates. Therefore, the cost of global communication on paralleldistributed memory computers can be significantly reduced. The resulting ICGS algorithm maintains the favorable properties of the algorithm while not increasing computational costs. Data distribution suitable for both irregularly and regularly structured matrices based on the analysis of the non-zero matrix elements is also presented. Communication scheme is supported by overlapping execution of computation and communication to reduce waiting times. The efficiency of this method is demonstrated by numerical experimental results carried out on a massively paralleldistributed memory system.
In the present era, huge amount of data is being produced every single day. A significant portion of this massive data or big data is contributed by images. Besides the amount of data, the size and resolution of indiv...
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ISBN:
(纸本)9781509020287
In the present era, huge amount of data is being produced every single day. A significant portion of this massive data or big data is contributed by images. Besides the amount of data, the size and resolution of individual images is also increasing at a very fast pace, leading to more and more complex imageprocessing algorithms which in turn pose great demand to computation power. This paper provides a solution to one such imageprocessing application which analyzes the image-processing kernels from an industrial application: Organic-Light-Emitting-Diode (OLED) Printing for OLED center detection. The application uses Hadoop and Hadoop imageprocessing Interface(HIPI) for parallelizing the processing. Hadoop provides the parallelprocessing paradigm, which when used along with HIPI can provide significant performance improvements for processingimages.
Sequence comparison i.e., the assessment of how similar two biological sequences are to each other, is a fundamental and routine task in Computational Biology and Bioinformatics. Classically, alignment methods are the...
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ISBN:
(纸本)9781467375894
Sequence comparison i.e., the assessment of how similar two biological sequences are to each other, is a fundamental and routine task in Computational Biology and Bioinformatics. Classically, alignment methods are the de facto standard for such an assessment. In fact, considerable research efforts for the development of efficient algorithms, both on classic and parallel architectures, has been carried out in the past 50 years. Due to the growing amount of sequence data being produced, a new class of methods has emerged: Alignment-free methods. Research in this ares has become very intense in the past few years, stimulated by the advent of Next Generation Sequencing technologies, since those new methods are very appealing in terms of computational resources needed and biological relevance. Despite such an effort and in contrast with sequence alignment methods, no systematic investigation of how to take advantage of distributed architectures to speed up alignment-free methods, has taken place. We provide a contribution of that kind, by evaluating the possibility of using the Hadoop distributed framework to speed up the running times of these methods, compared to their original sequential formulation.
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