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Journal articles on the topic 'Parallel and distributed algorithms'

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Published: 10 December 2022
Last updated: 28 January 2023

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1

Aupy, Guillaume, and Xueyan Tang. "Parallel and distributed algorithms." Concurrency and Computation: Practice and Experience 30, no. 17 (July 2, 2018): e4663. http://dx.doi.org/10.1002/cpe.4663.

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2

Subramanian, K., and M. Zargham. "Distributed and Parallel Demand Driven Logic Simulation Algorithms." VLSI Design 1, no. 2 (January 1, 1994): 169–79. http://dx.doi.org/10.1155/1994/12503.

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Based on the demand-driven approach, distributed and parallel simulation algorithms are proposed. Demand-driven simulation ties to minimize the number of component evaluations by restricting to only those component computations required for the watched output requests. For a specific output value request, the input line values that are required are requested to the respective component. The process continues until known signal values are needed (system input signal values). We present a distributed demand-driven algorithm with infinite memory requirement (but still the memory required at each process is no greater than the sequential demand-driven simulation), and a parallel demand-driven simulation with finite memory requirement. In our algorithms, each component is assigned a logical process.The algorithms have been implemented on the Sequent Balance 8000 Multi-processor machine. Several sample circuits were simulated. The algorithms were compared with the distributed discrete-event simulation. Our distributed algorithm performed many times faster than the discrete-event simulation for cases when few results were needed. Parallel algorithm performed 2 to 4 times faster than the distributed discrete-event simulation.
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3

Rine, David C. "Parallel and Distributed Processability of Objects." Fundamenta Informaticae 12, no. 3 (July 1, 1989): 317–56. http://dx.doi.org/10.3233/fi-1989-12304.

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Partitioning and allocating of software components are two important parts of software design in distributed software engineering. This paper presents two general algorithms that can, to a limited extent, be used as tools to assist in partitioning software components represented as objects in a distributed software design environment. One algorithm produces a partition (equivalence classes) of the objects, and a second algorithm allows a minimum amount of redundancy. Only binary relationships of actions (use or non-use) are considered in this paper.
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4

Ravikumar, C. P., Vikas Jain, and Anurag Dod. "Distributed Fault Simulation Algorithms on Parallel Virtual Machine." VLSI Design 12, no. 1 (January 1, 2001): 81–99. http://dx.doi.org/10.1155/2001/58303.

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In this paper, we describe distributed algorithms for combinational fault simulation assuming the classical stuck-at fault model. Our algorithms have been implemented on a network of Sun workstations under the Parallel Virtual Machine (PVM) environment. Two techniques are used for subdividing work among processors – test set partition and fault set partition. The sequential algorithm for fault simulation, used on individual nodes of the network, is based on a novel path compression technique proposed in this paper. We describe experimental results on a number of ISCAS′85 benchmark circuits.
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MIKI, Mitsunori, Tomoyuki HIROYASU, and Ikki OHMUKAI. "Hybridization Crossover for Parallel Distributed Genetic Algorithms." Proceedings of The Computational Mechanics Conference 2000.13 (2000): 299–300. http://dx.doi.org/10.1299/jsmecmd.2000.13.299.

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Hanuliak, Juraj, and Ivan Hanuliak. "To performance evaluation of distributed parallel algorithms." Kybernetes 34, no. 9/10 (October 2005): 1633–50. http://dx.doi.org/10.1108/03684920510614858.

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7

Alba, Enrique, and Jos� M. Troya. "A survey of parallel distributed genetic algorithms." Complexity 4, no. 4 (March 1999): 31–52. http://dx.doi.org/10.1002/(sici)1099-0526(199903/04)4:4<31::aid-cplx5>3.0.co;2-4.

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8

CHO, KILSEOK, ALAN D. GEORGE, RAJ SUBRAMANIYAN, and KEONWOOK KIM. "PARALLEL ALGORITHMS FOR ADAPTIVE MATCHED-FIELD PROCESSING ON DISTRIBUTED ARRAY SYSTEMS." Journal of Computational Acoustics 12, no. 02 (June 2004): 149–74. http://dx.doi.org/10.1142/s0218396x04002274.

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Matched-field processing (MFP) localizes sources more accurately than plane-wave beamforming by employing full-wave acoustic propagation models for the cluttered ocean environment. The minimum variance distortionless response MFP (MVDR–MFP) algorithm incorporates the MVDR technique into the MFP algorithm to enhance beamforming performance. Such an adaptive MFP algorithm involves intensive computational and memory requirements due to its complex acoustic model and environmental adaptation. The real-time implementation of adaptive MFP algorithms for large surveillance areas presents a serious computational challenge where high-performance embedded computing and parallel processing may be required to meet real-time constraints. In this paper, three parallel algorithms based on domain decomposition techniques are presented for the MVDR–MFP algorithm on distributed array systems. The parallel performance factors in terms of execution times, communication times, parallel efficiencies, and memory capacities are examined on three potential distributed systems including two types of digital signal processor arrays and a cluster of personal computers. The performance results demonstrate that these parallel algorithms provide a feasible solution for real-time, scalable, and cost-effective adaptive beamforming on embedded, distributed array systems.
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9

Gravvanis, George A., and Hamid R. Arabnia. "The Journal of Parallel Algorithms and Applications: Special Issue on Parallel and Distributed Algorithms." Parallel Algorithms and Applications 19, no. 2-3 (June 2004): 77–78. http://dx.doi.org/10.1080/10637190410001725445.

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10

IRONY, DROR, and SIVAN TOLEDO. "TRADING REPLICATION FOR COMMUNICATION IN PARALLEL DISTRIBUTED-MEMORY DENSE SOLVERS." Parallel Processing Letters 12, no. 01 (March 2002): 79–94. http://dx.doi.org/10.1142/s0129626402000847.

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We present new communication-efficient parallel dense linear solvers: a solver for triangular linear systems with multiple right-hand sides and an LU factorization algorithm. These solvers are highly parallel and they perform a factor of 0.4P1/6 less communication than existing algorithms, where P is number of processors. The new solvers reduce communication at the expense of using more temporary storage. Previously, algorithms that reduce communication by using more memory were only known for matrix multiplication. Our algorithms are recursive, elegant, and relatively simple to implement. We have implemented them using MPI, a message-passing libray, and tested them on a cluster of workstations.
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11

Jacobson, Peter, Bo Kågström, and Mikael Rännar. "Algorithm Development for Distributed Memory Multicomputers Using CONLAB." Scientific Programming 1, no. 2 (1992): 185–203. http://dx.doi.org/10.1155/1992/365325.

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CONLAB (CONcurrent LABoratory) is an environment for developing algorithms for parallel computer architectures and for simulating different parallel architectures. A user can experimentally verify and obtain a picture of the real performance of a parallel algorithm executing on a simulated target architecture. CONLAB gives a high-level support for expressing computations and communications in a distributed memory multicomputer (DMM) environment. A development methodology for DMM algorithms that is based on different levels of abstraction of the problem, the target architecture, and the CONLAB language itself is presented and illustrated with two examples. Simulotion results for and real experiments on the Intel iPSC/2 hypercube are presented. Because CONLAB is developed to run on uniprocessor UNIX workstations, it is an educational tool that offers interactive (simulated) parallel computing to a wide audience.
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12

LEE, SANGYOON, CHAN-IK PARK, and CHAN-MO PARK. "AN IMPROVED PARALLEL ALGORITHM FOR DELAUNAY TRIANGULATION ON DISTRIBUTED MEMORY PARALLEL COMPUTERS." Parallel Processing Letters 11, no. 02n03 (June 2001): 341–52. http://dx.doi.org/10.1142/s0129626401000634.

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Delaunay triangulation has been much used in such applications as volume rendering, shape representation, terrain modeling and so on. The main disadvantage of Delaunay triangulation is large computation time required to obtain the triangulation on an input points sets. This time can be reduced by using more than one processor, and several parallel algorithms for Delaunay triangulation have been proposed. In this paper, we propose an improved parallel algorithm for Delaunay triangulation, which partitions the bounding convex region of the input points set into a number of regions by using Delaunay edges and generates Delaunay triangles in each region by applying an incremental construction approach. Partitioning by Delaunay edges makes it possible to eliminate merging step required for integrating subresults. It is shown from the experiments that the proposed algorithm has good load balance and is more efficient than Cignoni et al.'s algorithm and our previous algorithm.
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13

GEORGE, ALAN D., and KEONWOOK KIM. "PARALLEL ALGORITHMS FOR SPLIT-APERTURE CONVENTIONAL BEAMFORMING." Journal of Computational Acoustics 07, no. 04 (December 1999): 225–44. http://dx.doi.org/10.1142/s0218396x99000151.

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Quiet submarine threats and high clutter in the littoral undersea environment increase the processing demands on beamforming arrays, particularly for applications which require in-array autonomous operation. Whereas traditional single-aperture beamforming approaches may falter, the Split-Aperture Conventional Beamforming (SA-CBF) algorithm can be used to meet stringent requirements for more precise bearing estimation. Moreover, by coupling each transducer node with a microprocessor, parallel processing of the split-aperture beamformer on a distributed system can glean advantages in execution speed, fault tolerance, scalability, and cost. In this paper, parallel algorithms for SA-CBF are introduced using coarse-grained and medium-grained forms of decomposition. Performance results from parallel and sequential algorithms are presented using a distributed system testbed comprised of a cluster of workstations connected by a high-speed network. The execution times, parallel efficiencies, and memory requirements of each parallel algorithm are presented and analyzed. The results of these analyses demonstrate that parallel in-array processing holds the potential to meet the needs of future advanced sonar beamforming algorithms in a scalable fashion.
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SINHA, PRIYABRATA, ALAN D. GEORGE, and KEONWOOK KIM. "PARALLEL ALGORITHMS FOR ROBUST BROADBAND MVDR BEAMFORMING." Journal of Computational Acoustics 10, no. 01 (March 2002): 69–96. http://dx.doi.org/10.1142/s0218396x02001565.

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Rapid advancements in adaptive sonar beamforming algorithms have greatly increased the computation and communication demands on beamforming arrays, particularly for applications that require in-array autonomous operation. By coupling each transducer node in a distributed array with a microprocessor, and networking them together, embedded parallel processing for adaptive beamformers can significantly reduce execution time, power consumption and cost, and increase scalability and dependability. In this paper, the basic narrowband Minimum Variance Distortionless Response (MVDR) beamformer is enhanced by incorporating broadband processing, a technique to enhance the robustness of the algorithm, and speedup of the matrix inversion task using sequential regression. Using this Robust Broadband MVDR (RB-MVDR) algorithm as a sequential baseline, two novel parallel algorithms are developed and analyzed. Performance results are included, among them execution time, scaled speedup, parallel efficiency, result latency and memory utilization. The testbed used is a distributed system comprised of a cluster of personal computers connected by a conventional network.
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15

KIM, KEONWOOK, and ALAN D. GEORGE. "PARALLEL SUBSPACE PROJECTION BEAMFORMING FOR AUTONOMOUS, PASSIVE SONAR SIGNAL PROCESSING." Journal of Computational Acoustics 11, no. 01 (March 2003): 55–74. http://dx.doi.org/10.1142/s0218396x0300181x.

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Adaptive techniques can be applied to improve performance of a beamformer in a cluttered environment. The sequential implementation of an adaptive beamformer, for many sensors and over a wide band of frequencies, presents a serious computational challenge. By coupling each transducer node with a microprocessor, in-situ parallel processing applied to an adaptive beamformer on a distributed system can glean advantages in execution speed, fault tolerance, scalability, and cost. In this paper, parallel algorithms for Subspace Projection Beamforming (SPB), using QR decomposition on distributed systems, are introduced for in-situ signal processing. Performance results from parallel and sequential algorithms are presented using a distributed system testbed comprised of a cluster of computers connected by a network. The execution times, parallel efficiencies, and memory requirements of each parallel algorithm are presented and analyzed. The results of these analyses demonstrate that parallel in-situ processing holds the potential to meet the needs of future advanced beamforming algorithms in a scalable fashion.
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16

Bogle, Ian, George M. Slota, Erik G. Boman, Karen D. Devine, and Sivasankaran Rajamanickam. "Parallel graph coloring algorithms for distributed GPU environments." Parallel Computing 110 (May 2022): 102896. http://dx.doi.org/10.1016/j.parco.2022.102896.

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17

HIROYASU, Tomoyuki, and Mitsunori MIKI. "Parallel Distributed Genetic Algorithms on PC Cluster Systems." Proceedings of OPTIS 2000.4 (2000): 305–10. http://dx.doi.org/10.1299/jsmeoptis.2000.4.305.

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18

Kushner, H. J., and G. Yin. "Stochastic approximation algorithms for parallel and distributed processing." Stochastics 22, no. 3-4 (November 1987): 219–50. http://dx.doi.org/10.1080/17442508708833475.

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19

Eklund, Sven E. "A massively parallel architecture for distributed genetic algorithms." Parallel Computing 30, no. 5-6 (May 2004): 647–76. http://dx.doi.org/10.1016/j.parco.2003.12.009.

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20

Alba, Enrique, and José M. Troya. "Analyzing synchronous and asynchronous parallel distributed genetic algorithms." Future Generation Computer Systems 17, no. 4 (January 2001): 451–65. http://dx.doi.org/10.1016/s0167-739x(99)00129-6.

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21

Strandmark, Petter, Fredrik Kahl, and Thomas Schoenemann. "Parallel and distributed vision algorithms using dual decomposition." Computer Vision and Image Understanding 115, no. 12 (December 2011): 1721–32. http://dx.doi.org/10.1016/j.cviu.2011.06.012.

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22

Gravvanis, George A., and Hamid R. Arabnia. "Special section: Parallel and distributed algorithms and systems." Future Generation Computer Systems 22, no. 1-2 (January 2006): 32–33. http://dx.doi.org/10.1016/j.future.2004.11.013.

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23

Kishimoto, Akihiro, Alex Fukunaga, and Adi Botea. "Scalable, Parallel Best-First Search for Optimal Sequential Planning." Proceedings of the International Conference on Automated Planning and Scheduling 19 (October 16, 2009): 201–8. http://dx.doi.org/10.1609/icaps.v19i1.13350.

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Large-scale, parallel clusters composed of commodity processors are increasingly available, enabling the use of vast processing capabilities and distributed RAM to solve hard search problems. We investigate parallel algorithms for optimal sequential planning, with an emphasis on exploiting distributed memory computing clusters. In particular, we focus on an approach which distributes and schedules work among processors based on a hash function of the search state. We use this approach to parallelize the A* algorithm in the optimal sequential version of the Fast Downward planner. The scaling behavior of the algorithm is evaluated experimentally on clusters using up to 128 processors, a significant increase compared to previous work in parallelizing planners. We show that this approach scales well, allowing us to effectively utilize the large amount of distributed memory to optimally solve problems which require hundreds of gigabytes of RAM to solve. We also show that this approach scales well for a single, shared-memory multicore machine.
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LOULERGUE, F. "DISTRIBUTED EVALUATION OF FUNCTIONAL BSP PROGRAMS." Parallel Processing Letters 11, no. 04 (December 2001): 423–37. http://dx.doi.org/10.1142/s0129626401000701.

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The BS λp-calculus is a calculus of functional bulk synchronous parallel (BSP) programs. It is the basis for the design of a bulk synchronous parallel ML language. For data-parallel languages, there are two points of view: the programming model where a program is seen as a sequence of operations on parallel vectors, and the execution model where the program is a parallel composition of programs run on each processor of the parallel machine. BSP algorithms are defined by data-parallel algorithms with explicit (physical) processes in order to allow their parallel execution time to be estimated. We present here a distributed evaluation minimally synchronous for BSP execution (which corresponds to the execution model). This distributed evaluation is correct w.r.t. the call-by-value strategy of the BS λp-calculus (which corresponds to the programming model).
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25

Likhoded, N. A., and A. A. Tolstsikau. "Locality estimation of parallel algorithm for distributed memory computers." Doklady of the National Academy of Sciences of Belarus 64, no. 6 (December 31, 2020): 647–56. http://dx.doi.org/10.29235/1561-8323-2020-64-6-647-656.

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Locality is an algorithm characteristic describing a usage level of fast access memory. For example, in case of distributed memory computers we focus on memory of each computational node. To achieve the high performance of algorithm implementation one should choose the best possible locality option. Studying the parallel algorithm locality is to estimate the number and volume of data communications. In this work, we formulate and prove the statements for computers with distributed memory that allow us to estimate the asymptotic volume of data communication operations. These estimation results are useful while comparing alternative versions of parallel algorithms during data communication cost analysis.
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Likhoded, N. A., and A. A. Tolstsikau. "Locality estimation of parallel algorithm for distributed memory computers." Doklady of the National Academy of Sciences of Belarus 64, no. 6 (December 31, 2020): 647–56. http://dx.doi.org/10.29235/1561-8323-2020-64-6-647-656.

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Locality is an algorithm characteristic describing a usage level of fast access memory. For example, in case of distributed memory computers we focus on memory of each computational node. To achieve the high performance of algorithm implementation one should choose the best possible locality option. Studying the parallel algorithm locality is to estimate the number and volume of data communications. In this work, we formulate and prove the statements for computers with distributed memory that allow us to estimate the asymptotic volume of data communication operations. These estimation results are useful while comparing alternative versions of parallel algorithms during data communication cost analysis.
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27

Ngoko, Yanik, Christophe Cérin, and Denis Trystram. "Solving Sat in a Distributed Cloud: A Portfolio Approach." International Journal of Applied Mathematics and Computer Science 29, no. 2 (June 1, 2019): 261–74. http://dx.doi.org/10.2478/amcs-2019-0019.

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Abstract We introduce a new parallel and distributed algorithm for the solution of the satisfiability problem. It is based on an algorithm portfolio and is intended to be used for servicing requests in a distributed cloud. The core of our contribution is the modeling of the optimal resource sharing schedule in parallel executions and the proposition of heuristics for its approximation. For this purpose, we reformulate a computational problem introduced in a prior work. The main assumption is that it is possible to learn optimal resource sharing from traces collected on past executions on a representative set of instances. We show that the learning can be formalized as a set coverage problem. Then we propose to solve it by approximation and dynamic programming algorithms based on classical greedy algorithms for the maximum coverage problem. Finally, we conduct an experimental evaluation for comparing the performance of the various algorithms proposed. The results show that some algorithms become more competitive if we intend to determine the trade-off between their quality and the runtime required for their computation.
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Peibo, Duan, Zhang Changsheng, and Zhang Bin. "A Local Stability Supported Parallel Distributed Constraint Optimization Algorithm." Scientific World Journal 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/734975.

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This paper presents a new distributed constraint optimization algorithm called LSPA, which can be used to solve large scale distributed constraint optimization problem (DCOP). Different from the access of local information in the existing algorithms, a new criterion called local stability is defined and used to evaluate which is the next agent whose value needs to be changed. The propose of local stability opens a new research direction of refining initial solution by finding key agents which can seriously effect global solution once they modify assignments. In addition, the construction of initial solution could be received more quickly without repeated assignment and conflict. In order to execute parallel search, LSPA finds final solution by constantly computing local stability of compatible agents. Experimental evaluation shows that LSPA outperforms some of the state-of-the-art incomplete distributed constraint optimization algorithms, guaranteeing better solutions received within ideal time.
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Sharma, Manik, and Smriti Smriti. "STATIC AND DYNAMIC BNP PARALLEL SCHEDULING ALGORITHMS FOR DISTRIBUTED DATABASE." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 1, no. 1 (December 30, 2011): 10–15. http://dx.doi.org/10.24297/ijct.v1i1.2601.

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Parallel processing is a technique of executing the multiple tasksconcurrently on different processors. Parallel processing is usedto solve the complex problems that require vast amount ofprocessing time. Task scheduling is one of the major problemsof parallel processing. The objective of this study is to analyzethe performance of static (HLFET) and dynamic (DLS) BNPparallel scheduling algorithm for allocating the tasks ofdistributed database over number of processors. In the wholestudy the focus will be given on measuring the impact ofnumber of processors on different metrics of performance likemakespan, speed up and processor utilization by using HLFETand DLS BNP task scheduling algorithms.
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30

BENNER, PETER, JOSÉ M. CLAVER, and ENRIQUE S. QUINTANA-ORTI. "PARALLEL DISTRIBUTED SOLVERS FOR LARGE STABLE GENERALIZED LYAPUNOV EQUATIONS." Parallel Processing Letters 09, no. 01 (March 1999): 147–58. http://dx.doi.org/10.1142/s0129626499000165.

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In this paper we study the solution of stable generalized Lyapunov matrix equations with large-scale, dense coefficient matrices. Our iterative algorithms, based on the matrix sign function, only require scalable matrix algebra kernels which are highly efficient on parallel distributed architectures. This approach avoids therefore the difficult parallelization of direct methods based on the QZ algorithm. The experimental analtsis reports a remarkable performance of our solvers on an IBM SP2 platform.
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31

Mader, G., and F. H. Uhlmann. "A parallel implementation of a 3D-BEM-algorithm using distributed memory algorithms." IEEE Transactions on Magnetics 33, no. 2 (March 1997): 1796–99. http://dx.doi.org/10.1109/20.582624.

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32

Xu, Wencai. "Efficient Distributed Image Recognition Algorithm of Deep Learning Framework TensorFlow." Journal of Physics: Conference Series 2066, no. 1 (November 1, 2021): 012070. http://dx.doi.org/10.1088/1742-6596/2066/1/012070.

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Abstract Deep learning requires training on massive data to get the ability to deal with unfamiliar data in the future, but it is not as easy to get a good model from training on massive data. Because of the requirements of deep learning tasks, a deep learning framework has also emerged. This article mainly studies the efficient distributed image recognition algorithm of the deep learning framework TensorFlow. This paper studies the deep learning framework TensorFlow itself and the related theoretical knowledge of its parallel execution, which lays a theoretical foundation for the design and implementation of the TensorFlow distributed parallel optimization algorithm. This paper designs and implements a more efficient TensorFlow distributed parallel algorithm, and designs and implements different optimization algorithms from TensorFlow data parallelism and model parallelism. Through multiple sets of comparative experiments, this paper verifies the effectiveness of the two optimization algorithms implemented in this paper for improving the speed of TensorFlow distributed parallel iteration. The results of research experiments show that the 12 sets of experiments finally achieved a stable model accuracy rate, and the accuracy rate of each set of experiments is above 97%. It can be seen that the distributed algorithm of using a suitable deep learning framework TensorFlow can be implemented in the goal of effectively reducing model training time without reducing the accuracy of the final model.
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Gao, Nan, Cheng Xu, Xin Peng, Haibo Luo, Wufei Wu, and Guoqi Xie. "Energy-Efficient Scheduling Optimization for Parallel Applications on Heterogeneous Distributed Systems." Journal of Circuits, Systems and Computers 29, no. 13 (March 25, 2020): 2050203. http://dx.doi.org/10.1142/s0218126620502035.

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Designing energy-efficient scheduling algorithms on heterogeneous distributed systems is increasingly becoming the focus of research. State-of-the-art works have studied scheduling by combining dynamic voltage and frequency scaling (DVFS) technology and turning off the appropriate processors to reduce dynamic and static energy consumptions. However, the methods for turning off processors are ineffective. In this study, we propose a novel method to assign priorities to processors for facilitating effective selection of turned-on processors to decrease static energy consumption. An energy-efficient scheduling algorithm based on bisection (ESAB) is proposed on this basis, and this algorithm directly turns on the most energy-efficient processors depending on the idea of bisection to reduce static energy consumption while dynamic energy consumption is decreased by using DVFS technology. Experiments are performed on fast Fourier transform, Gaussian elimination, and randomly generated parallel applications. Results show that our ESAB algorithm makes a better trade-off between reducing energy consumption and low computation time of task assignment (CTTA) than existing algorithms under different scale conditions, deadline constraints, and degrees of parallelism and heterogeneity.
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Damiand, Guillaume, Aldo Gonzalez-Lorenzo, Florence Zara, and Florent Dupont. "Distributed Combinatorial Maps for Parallel Mesh Processing." Algorithms 11, no. 7 (July 13, 2018): 105. http://dx.doi.org/10.3390/a11070105.

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We propose a new strategy for the parallelization of mesh processing algorithms. Our main contribution is the definition of distributed combinatorial maps (called n-dmaps), which allow us to represent the topology of big meshes by splitting them into independent parts. Our mathematical definition ensures the global consistency of the meshes at their interfaces. Thus, an n-dmap can be used to represent a mesh, to traverse it, or to modify it by using different mesh processing algorithms. Moreover, an nD mesh with a huge number of elements can be considered, which is not possible with a sequential approach and a regular data structure. We illustrate the interest of our solution by presenting a parallel adaptive subdivision method of a 3D hexahedral mesh, implemented in a distributed version. We report space and time performance results that show the interest of our approach for parallel processing of huge meshes.
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35

Chaves, Roberto Poveda, Orlando Garcia Hurtado, and Eduardo Cardenas Gomez. "Assignation problems' solutions by parallel and distributed genetic algorithms." Applied Mathematical Sciences 8 (2014): 5185–94. http://dx.doi.org/10.12988/ams.2014.46481.

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36

Gregor, Douglas, and Andrew Lumsdaine. "Lifting sequential graph algorithms for distributed-memory parallel computation." ACM SIGPLAN Notices 40, no. 10 (October 12, 2005): 423–37. http://dx.doi.org/10.1145/1103845.1094844.

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37

Elenbogen, B. S., D. H. Yoon, K. Akingbehin, B. R. Maxim, and L. Tsui. "Parallel and distributed algorithms laboratory assignments in Joyce/Linda." Engineering Science & Education Journal 8, no. 2 (April 1, 1999): 81–88. http://dx.doi.org/10.1049/esej:19990209.

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38

Yang, Jiyan, Xiangrui Meng, and Michael W. Mahoney. "Implementing Randomized Matrix Algorithms in Parallel and Distributed Environments." Proceedings of the IEEE 104, no. 1 (January 2016): 58–92. http://dx.doi.org/10.1109/jproc.2015.2494219.

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39

Cahon, S., N. Melab, and E. G. Talbi. "Building with ParadisEO reusable parallel and distributed evolutionary algorithms." Parallel Computing 30, no. 5-6 (May 2004): 677–97. http://dx.doi.org/10.1016/j.parco.2003.12.010.

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Benkahla, O., C. Aktouf, and C. Robach. "Performance evaluation of distributed diagnosis algorithms in parallel systems." Parallel Computing 24, no. 8 (August 1998): 1205–22. http://dx.doi.org/10.1016/s0167-8191(98)00048-9.

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41

Brochard, Luigi. "Efficiency of some parallel numerical algorithms on distributed systems." Parallel Computing 12, no. 1 (October 1989): 21–44. http://dx.doi.org/10.1016/0167-8191(89)90004-5.

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42

Choi, Jaeyoung, Jack J. Dongarra, and David W. Walker. "Parallel matrix transpose algorithms on distributed memory concurrent computers." Parallel Computing 21, no. 9 (September 1995): 1387–405. http://dx.doi.org/10.1016/0167-8191(95)00016-h.

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43

Shuai, Dianxun. "New heuristic distributed parallel algorithms for searching and planning." Journal of Computer Science and Technology 10, no. 4 (July 1995): 354–74. http://dx.doi.org/10.1007/bf02943504.

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44

Leiserson, Charles E., and Bruce M. Maggs. "Communication-efficient parallel algorithms for distributed random-access machines." Algorithmica 3, no. 1-4 (November 1988): 53–77. http://dx.doi.org/10.1007/bf01762110.

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45

Zapata, EL, OG Plata, and FF Rivera. "Design of parallel algorithms for a distributed memory hypercube." Microprocessors and Microsystems 16, no. 9 (January 1992): 463–70. http://dx.doi.org/10.1016/0141-9331(92)90107-5.

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46

Koutis, Ioannis, and Shen Chen Xu. "Simple Parallel and Distributed Algorithms for Spectral Graph Sparsification." ACM Transactions on Parallel Computing 3, no. 2 (August 8, 2016): 1–14. http://dx.doi.org/10.1145/2948062.

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47

Elenborgen, Bruce S. "Parallel and distributed algorithms: laboratory assignments in Joyce/Linda." ACM SIGCSE Bulletin 28, no. 1 (March 1996): 14–18. http://dx.doi.org/10.1145/236462.236478.

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48

Chung, Yongwha, Cho-Li Wang, and Viktor K. Prasanna. "Parallel Algorithms for Perceptual Grouping on Distributed Memory Machines." Journal of Parallel and Distributed Computing 50, no. 1-2 (April 1998): 123–43. http://dx.doi.org/10.1006/jpdc.1998.1438.

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49

Altman, Eitan, Tamer Başar, Tania Jiménez, and Nahum Shimkin. "Routing into Two Parallel Links: Game-Theoretic Distributed Algorithms." Journal of Parallel and Distributed Computing 61, no. 9 (September 2001): 1367–81. http://dx.doi.org/10.1006/jpdc.2001.1754.

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50

Ferner, Clayton S., and Robert G. Babb II. "Automatic Choice of Scheduling Heuristics for Parallel/Distributed Computing." Scientific Programming 7, no. 1 (1999): 47–65. http://dx.doi.org/10.1155/1999/898723.

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Abstract:
Task mapping and scheduling are two very difficult problems that must be addressed when a sequential program is transformed into a parallel program. Since these problems are NP‐hard, compiler writers have opted to concentrate their efforts on optimizations that produce immediate gains in performance. As a result, current parallelizing compilers either use very simple methods to deal with task scheduling or they simply ignore it altogether. Unfortunately, the programmer does not have this luxury. The burden of repartitioning or rescheduling, should the compiler produce inefficient parallel code, lies entirely with the programmer. We were able to create an algorithm (called a metaheuristic), which automatically chooses a scheduling heuristic for each input program. The metaheuristic produces better schedules in general than the heuristics upon which it is based. This technique was tested on a suite of real scientific programs written in SISAL and simulated on four different network configurations. Averaged over all of the test cases, the metaheuristic out‐performed all eight underlying scheduling algorithms; beating the best one by 2%, 12%, 13%, and 3% on the four separate network configurations. It is able to do this, not always by picking the best heuristic, but rather by avoiding the heuristics when they would produce very poor schedules. For example, while the metaheuristic only picked the best algorithm about 50% of the time for the 100 Gbps Ethernet, its worst decision was only 49% away from optimal. In contrast, the best of the eight scheduling algorithms was optimal 30% of the time, but its worst decision was 844% away from optimal.
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