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Dynamic load balancing strategies for conservative parallel simulations

Dynamic load balancing strategies for conservative parallel ...

引用

Workshop on Principles of Advanced and distributed Simulation (PADS)

作者： A. Boukerche S.K. Das Center for Research in Parallel and Distributed Computing Department of Computer Science University of North Texas Denton TX USA

This paper studies the problem of load balancing for conservative parallel simulations for execution on a multicomputer. The synchronization protocol makes use of Chandy-Misra (1979) null-messages. We propose a dynamic load balancing algorithm which assumes no compile time knowledge about the workload parameters. It is based upon a process migration mechanism, and the notion of CPU-queue length, which indicates the workload at each processor. We examine two variations for the algorithm which we refer to as centralized and multi-level hierarchical methods, in the context of queueing network simulation of a torus. The torus was chosen because its many cycles aid in the formation of deadlock, making it a stress test for any conservative synchronization protocols. Our experiments indicate that our dynamic load balancing schemes significantly reduce the run time of an optimized version of Chandy-Misra null message approach, and decreases by 30-40% the synchronization overhead when compared to the use of a static partitioning algorithm. Significantly, the results obtained also indicate that the multi-level scheme always outperforms both the centralized load balancing approach and the static partitioning algorithm.

关键词： Load management Computational modeling Protocols distributed computing Stress Testing Partitioning algorithms Discrete event simulation Computer simulation Concurrent computing

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Cost-optimal parallel algorithms for finitely inductive sequences

Cost-optimal parallel algorithms for finitely inductive sequ...

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Annual Hawaii International Conference on System Sciences (HICSS)

作者： S.K. Das P.S. Fisher H. Zhang Center for Research in Parallel and Distributed Computing Department of Computer Science University of North Texas Denton TX USA

Finitely inductive (FI) sequences are a class of sequences, finite or infinite, which are amenable to a certain mathematical representation which has direct significance to pattern recognition and string matching. The authors propose cost-optimal parallel algorithms for FI sequence processing. These algorithms include parallel factoring and following by bucket packing and by hashing on an exclusive-read and exclusive-write (EREW), parallel random access machine (PRAM) model. Each of the algorithms achieves cost-optimal speedup under certain conditions as detailed.< >

关键词： parallel algorithms Pattern recognition Pattern matching distributed computing Computer science Impedance matching Phase change random access memory Multiple signal classification Signal detection Motion detection

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Biomedical image processing with GPGPU using CUDA

Biomedical image processing with GPGPU using CUDA

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作者： Firstauthor, A.R. Secondauthor, I.B. Thirdauthor, G.V. Fourthauthor, P.B. Fifthauthor, M.K. Buda University John von Neumann Faculty of Informatics Budapest Hungary Semmelweiss University 2nd Department of Internal Medicine Budapest Hungary MTA SZTAKI Laboratory of Parallel and Distributed Computing Budapest Hungary

ISBN: (纸本)9789532330670

The main aim of this work is to show, how the GPGPUs can be used to speed up certain image processing methods. The algorithm explained in this paper is used to detect nuclei on (HE hematoxilin eosin) stained colon tissue sample images, and includes a Gauss blurring, an RGB-HSV color space conversion, a fixed binarization, an ultimate erode procedure and a local maximum search. Since the images retrieved from the digital slides require significant storage space (up to few hundred megapixels), the usage of GPGPUs to speed up image processing operations is necessary in the interest of achieving reasonable processing time. The CUDA software development kit was used to develop algorithms to GPUs made by NVIDIA. This work focuses on how to achieve coalesced global memory access when working with three-channel RGB images, and how to use the on-die shared memory efficiently. The exact test algorithm also included a linear connected component labeling, which was running on the CPU, and with iterative optimization of the GPU code, we managed to achieve significant speed up in well defined test environment. © 2011 MIPRO.

关键词： Iterative methods

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An experimental study of the emergent behaviors of self-organized agent communities

An experimental study of the emergent behaviors of self-orga...

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Congress on Evolutionary Computation

作者： Shufeng Wang Hong Zhu National Laboratory for Parallel and Distributed Processing Changsha China Department of Computing Oxford Brookes University Oxford UK

Emergent behavior is an essential feature in multi-agent systems and plays a significant role in the applications of agent technology. Because of the huge gap between individual agents' behaviors and those of the whole system, specifying and reasoning about emergent behaviors are notoriously difficult. Simulation has been the essential method to study emergent behaviors in multi-agent systems. In this paper, we report an experimental study of the emergent behaviors of self-organized agent communities, in which emergent behaviors play a crucial role. The experiments confirmed the results of a theoretical analysis of agent communities using a formal theory called scenario calculus. It further provided insight into the dynamic features of the system that were very difficult to obtain by using formal logic, such as the speed of convergence to the emergent states and the relationships between the convergence time and various parameters of self-organized agent communities systems.

关键词： Evolutionary computation

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Cost-optimal parallel algorithms for traversing trees

Cost-optimal parallel algorithms for traversing trees

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IEEE Southeastcon

作者： S.K. Das C.C.-Y. Chen Center of Research in Parallel and Distributed Computing Department of Computer Science University of North Texas Denton TX USA

The authors present cost-optimal parallel algorithms for depth-order (e.g., pre-, in-, and post-order) and level-order (e.g., breadth-first and breadth-depth) traversals of general trees with n nodes. Each of the algo... 详细信息

关键词： parallel algorithms Phase change random access memory Computer science Binary trees distributed computing Classification tree analysis Algorithm design and analysis

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A novel compilation framework for supporting semi-regular distributions in hybrid applications

A novel compilation framework for supporting semi-regular di...

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International Symposium on parallel Processing

作者： D.R. Chakrabarti P. Banerjee Center for Parallel and Distributed Computing ECE Department Tech.Institute Northwestern University Evanston IL USA

This paper explains how efficient support for semi-regular distributions can be incorporated in a uniform compilation framework for hybrid applications. The key focus of this work is in showing how, unlike other existing schemes, our scheme is able to minimize preprocessing overheads and maintain sophisticated communication optimizations (such as reduction of inter-processor communication during schedule generation and sharing of communicated information between regular and irregular accesses) even in the presence of semi-regular distributions. It is only natural that preprocessing overheads associated with semi-regular distributions be intermediate between those involved for regular and irregular distributions. This paper shows how various properties can be inferred for semi-regular distributions. These allow the use of the interval representation which in turn reduces the preprocessing overhead and makes possible compatible code generation for hybrid references. Experimental results on a 16-processor IBM SP-2 for a number of sparse applications using semi-regular distributions show that our scheme is feasible.

关键词： Sparse matrices Runtime Hybrid power systems Contracts Concurrent computing Costs Processor scheduling

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Scenario mechanism in agent-oriented programming

Scenario mechanism in agent-oriented programming

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Asia-Pacific Conference on Software Engineering

作者： Rui Shen Ji Wang H. Zhu National Laboratory for Parallel and Distributed Processing Changsha China Department of Computing Oxford Brookes University Oxford UK

Scenario has been used to describe agent behaviors in the context of environment situations in the specification languages for agent-based systems, such as SLABS. It becomes an important language facility in the declaration of an agent for specifying its behaviors in its environment. Therefore, towards agent-oriented programming, it is necessary to introduce and implement scenario mechanism in programming languages. This paper reports our attempts to support the language facility from the view of programming languages, and presents an approach to facilitating the scenario mechanism in agent-oriented programming. The basic idea is to extend object-oriented programming language to support agent-oriented programming, where Java is chosen as the base language. Firstly, the language framework of agent-oriented programming, SLABSp is presented, mostly conforming to SLABS, whose syntax is extended based on Java. Scenario mechanism is introduced as the new feature in the programming language. Secondly, the underlying object models are defined to serve as the semantics of the language, where agents are modeled by a couple of objects. A compiler has been built to compile the agent-oriented programs into Java. A running platform has been constructed as the multiagent runtime environment of SLABSp.

关键词： Slabs Computer languages Java Object oriented modeling Object oriented programming Multiagent systems Specification languages Program processors Runtime environment Formal specifications

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Some fast parallel algorithms for parentheses matching 3rd

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3rd International Conference on computing and Information, ICCI 1991

作者： Das, Sajal K. Chen, Calvin C.-Y. Lewis, Gene Prasad, Sushil Center for Research in Parallel and Distributed Computing Department of Computer Science University of North Texas P.O. Box 13886 DentonTX76203-3886 United States Department of Mathematics and Computer Science Georgia State University AtlantaGA30303 United States

ISBN: (纸本)9783540540298

The parentheses matching problem is to determine the mate of each parenthesis in a balanced string of n parentheses. In this paper, we present three novel and elegant parallel algorithms for this problem on parallel random-access machine (PRAM) models. Each of our algorithms has polylog-time complexity and two of them are cost-optimal. © Springer-Verlag Berlin Heidelberg 1991.

关键词： parallel algorithms

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A cost-optimal parallel algorithm for the parentheses matching problem on an EREW PRAM

A cost-optimal parallel algorithm for the parentheses matchi...

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International Symposium on parallel Processing

作者： C.C.-Y. Chen S.K. Das Center for Research in Parallel and Distributed Computing Department of Computer Sciences University of North Texas Denton TX USA

The article presents a cost-optimal parallel algorithm for the parentheses matching problem on the EREW PRAM model. For n parentheses, the algorithm requires O(n/p+log n) time and O(n+p log p) space, employing p proce... 详细信息

关键词： parallel algorithms Phase change random access memory Data structures distributed computing Concurrent computing Arithmetic Binary trees Partitioning algorithms Read-write memory Algorithm design and analysis

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MatrixMap: Programming Abstraction and Implementation of Matrix Computation for Big Data Applications

MatrixMap: Programming Abstraction and Implementation of Mat...

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International Conference on parallel and distributed Systems (ICPADS)

作者： Yaguang Huangfu Jiannong Cao Hongliang Lu Guanqing Liang Department of Computing The Hong Kong Polytechnic University Parallel and Distributed Processing Laboratory National University of Defense Technology

The computation core of many big data applications can be expressed as general matrix computations, including linear algebra operations and irregular matrix operations. However, existing parallel programming systems such as Spark do not have programming abstraction and efficient implementation for general matrix computations. In this paper, we present MatrixMap, a unified and efficient data-parallel system for general matrix computations. MatrixMap provides powerful yet simple abstraction, consisting of a distributed data structure called bulk key matrix and a computation interface defined by matrix patterns. Users can easily load data into bulk key matrices and program algorithms into parallel matrix patterns. MatrixMap outperforms current state-of-the-art systems by employing three key techniques: matrix patterns with lambda functions for irregular and linear algebra matrix operations, asynchronous computation pipeline with optimized data shuffling strategies for specific matrix patterns and in-memory data structure reusing data in iterations. Moreover, it can automatically handle the parallelization and distribute execution of programs on a large cluster. The experiment results show that MatrixMap is 12 times faster than Spark.

关键词： Data structures Programming Linear algebra Machine learning algorithms Sparks Sparse matrices Computational modeling

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