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Cooperative scheduling of multi-core and cloud resources: multi-thread-based MCC offloading strategy

IET COMMUNICATIONS 2019年第14期13卷 2146-2154页

作者： Wang, Zhaoyang Han, Zhuo Yan, Lei Yang, Shouyi Zhengzhou Univ Sch Informat Engn 100 Sci Ave Zhengzhou 450001 Henan Peoples R China

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.

关键词： mobile computing multi-threading cloud computing fading channels search problems telecommunication scheduling NP problem multithreaded computing cloud computing local MTC strategy single-threaded MCC offloading strategy cloud resources modern multicore mobile devices mobile cloud MCC offloading strategy fading channel state optimal data transmission scheme linear time searching algorithm NP NP

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Acceleration of integration in parametric integral equations system using CUDA

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COMPUTERS & STRUCTURES 2015年第May期152卷 113-124页

作者： Kuzelewski, Andrzej Zieniuk, Eugeniusz Kapturczak, Marta Univ Bialystok Fac Math & Informat PL-15245 Bialystok Poland

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.

关键词： PIES GPU CUDA multithreaded computing Boundary value problems

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GPU-based acceleration of computations in elasticity problems solving by parametric integral equations system

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ADVANCES IN ENGINEERING SOFTWARE 2015年 79卷 27-35页

作者： Kuzelewski, Andrzej Zieniuk, Eugeniusz Univ Bialystok Fac Math & Informat PL-15887 Bialystok Poland

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.

关键词： Parametric integral equations system Elasticity problems Graphics Processing Unit GPGPU CUDA multithreaded computing

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Fast implementation for semidefinite programs with positive matrix completion

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OPTIMIZATION METHODS & SOFTWARE 2015年第5期30卷 1030-1049页

作者： Yamashita, Makoto Nakata, Kazuhide Tokyo Inst Technol Dept Math & Comp Sci Meguro Ku Tokyo 1528552 Japan Tokyo Inst Technol Dept Ind Engn & Management Meguro Ku Tokyo 1528552 Japan

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.

关键词： semidefinite programs interior-point methods matrix completion multithreaded computing

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Petri Nets State Space Analysis in the Cloud

Petri Nets State Space Analysis in the Cloud

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34th International Conference on Software Engineering (ICSE)

作者： Camilli, Matteo Univ Milan Dipartimento Informat & Comunicaz Milan Italy

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.

关键词： Petri nets Real-Time systems State-space explosion State-space parallel exploration multithreaded computing Distributed computing Cloud computing MapReduce

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Space-efficient scheduling of multithreaded computations

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SIAM JOURNAL ON computing 1998年第1期27卷 202-229页

作者： Blumofe, RD Leiserson, CE Univ Texas Dept Comp Sci Austin TX 78712 USA MIT Comp Sci Lab Cambridge MA 02139 USA

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.

关键词： parallel computing multithreaded computing parallel algorithms scheduling algorithms randomized algorithms strict execution stack memory

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