Methods of solving boundary value problems are expected to achieve high accuracy results in shortest possible time of calculations. In the previous papers the authors solved boundary value problems with high accuracy ...
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Methods of solving boundary value problems are expected to achieve high accuracy results in shortest possible time of calculations. In the previous papers the authors solved boundary value problems with high accuracy using parametric integral equations system. However, time of calculations was unsatisfactory. The most time consuming part of the method is calculation of integrals. In this paper the authors present approach to accelerate numerical integration in PIES by nVidia CUDA. The speed of integration increased up to 250 times whereas high accuracy of solutions was maintained. Examples included in this paper concern solving three-dimensional elasticity problems. (C) 2015 Elsevier Ltd. All rights reserved.
Application of techniques for modelling of boundary value problems implies two conflicting requirements: obtaining high accuracy of the results and speed of the solution. Accurate results can be obtained only by using...
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Application of techniques for modelling of boundary value problems implies two conflicting requirements: obtaining high accuracy of the results and speed of the solution. Accurate results can be obtained only by using appropriate models and algorithms. In the previous papers the authors applied the parametric integral equations system ( PIES) in modelling and solving boundary value problems. The first requirement was satisfied - the results were obtained with very high accuracy. This paper fulfils the second requirement by novel approach to accelerate PIES. Graphics cards ( GPUs) programming for numerical calculations in general purpose applications ( GPGPU) using NVIDIA CUDA is used for this purpose. The speed of calculations increased up to 80 times whereas high accuracy of the solutions wasmaintained. Examples included in this paper concern solving elasticity problems which are modelled by three- dimensional Navier- Lame equations.
Modern multi-core mobile devices are the main application objects of mobile cloud computing (MCC). In previous works, researchers have formulated various heuristic algorithms to solve the NP problem. This work combine...
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Modern multi-core mobile devices are the main application objects of mobile cloud computing (MCC). In previous works, researchers have formulated various heuristic algorithms to solve the NP problem. This work combines MCC with multi-threaded computing (MTC) of multi-core mobile devices to avoid NP problems and proposes an MTC-based MCC offloading strategy. First, the authors design an MTC strategy for the application model of cloud computing. Then, they use the data transmission scheme that is dynamically adjusted according to the fading channel state. Finally, based on the MTC strategy and the optimal data transmission scheme, they obtain the MTC-based MCC offloading strategy through a linear time searching algorithm. Simulation results show that compared with the local MTC strategy and the single-threaded MCC offloading strategy, the MTC-based MCC offloading strategy can significantly reduce energy consumption and improve the computing ability in multi-threaded applications.
Solving semidefinite programs (SDPs) in a short time is the key to managing various mathematical optimization problems. The matrix-completion primal-dual interior-point method (MC-PDIPM) extracts a sparse structure of...
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Solving semidefinite programs (SDPs) in a short time is the key to managing various mathematical optimization problems. The matrix-completion primal-dual interior-point method (MC-PDIPM) extracts a sparse structure of input SDP by factorizing the variable matrices. In this paper, we propose a new factorization based on the inverse of the variable matrix to enhance the performance of MC-PDIPM. We also use multithreaded parallel computing to deal with the major bottlenecks in MC-PDIPM. Numerical results show that the new factorization and multithreaded computing reduce the computation time for SDPs that have structural sparsity.
This paper considers the problem of scheduling dynamic parallel computations to achieve linear speedup without using significantly more space per processor than that required for a single-processor execution. Utilizin...
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This paper considers the problem of scheduling dynamic parallel computations to achieve linear speedup without using significantly more space per processor than that required for a single-processor execution. Utilizing a new graph-theoretic model of multithreaded computation, execution efficiency is quantified by three important measures: T-1 is the time required for executing the computation on a 1 processor, T-infinity is the time required by an infinite number of processors, and S-1 is the space required to execute the computation on a 1 processor. A computation executed on P processors is time-efficient if the time is O(T-1/P + T-infinity), that is, it achieves linear speedup when P = O(T-1/T-infinity), and it is space-efficient if it uses O(S1P) total space, that is, the space per processor is within a constant factor of that required for a 1-processor execution. The first result derived from this model shows that there exist multithreaded computations such that no execution schedule can simultaneously achieve efficient time and efficient space. But by restricting attention to "strict" computations-those in which all arguments to a procedure must be available before the procedure can be invoked-much more positive results are obtainable. Specifically, for any strict multithreaded computation, a simple online algorithm can compute a schedule that is both time-efficient and space-efficient. Unfortunately, because the algorithm uses a global queue, the overhead of computing the schedule can be substantial. This problem is overcome by a decentralized algorithm that can compute and execute a P-processor schedule online in expected time O(T-1/P + T-infinity lg P) and worst-case space O(S1P lg P), including overhead costs.
Several techniques for addressing the state space explosion problem in model checking have been studied. One of these is to use distributed memory and computation for storing and exploring the state space of the model...
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ISBN:
(纸本)9781467310673
Several techniques for addressing the state space explosion problem in model checking have been studied. One of these is to use distributed memory and computation for storing and exploring the state space of the model of a system. In this report, we present and compare different multi-thread, distributed, and cloud approaches to face the state-space explosion problem. The experiments report shows the convenience (in particular) of cloud approaches.
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