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Journal articles on the topic 'Parallel computers'

Author: Grafiati

Published: 4 June 2021

Last updated: 29 July 2024

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1

YAMAGUCHI, YOSHINORI. "Parallel Computers and Parallel Computation." Journal of the Institute of Electrical Engineers of Japan 118, no. 9 (1998): 526–29. http://dx.doi.org/10.1541/ieejjournal.118.526.

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2

Zhao, Yongwei, Yunji Chen, and Zhiwei Xu. "Fractal Parallel Computing." Intelligent Computing 2022 (September 5, 2022): 1–10. http://dx.doi.org/10.34133/2022/9797623.

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As machine learning (ML) becomes the prominent technology for many emerging problems, dedicated ML computers are being developed at a variety of scales, from clouds to edge devices. However, the heterogeneous, parallel, and multilayer characteristics of conventional ML computers concentrate the cost of development on the software stack, namely, ML frameworks, compute libraries, and compilers, which limits the productivity of new ML computers. Fractal von Neumann architecture (FvNA) is proposed to address the programming productivity issue for ML computers. FvNA is scale-invariant to program, thus making the development of a family of scaled ML computers as easy as a single node. In this study, we generalize FvNA to the field of general-purpose parallel computing. We model FvNA as an abstract parallel computer, referred to as the fractal parallel machine (FPM), to demonstrate several representative general-purpose tasks that are efficiently programmable. FPM limits the entropy of programming by applying constraints on the control pattern of the parallel computing systems. However, FPM is still general-purpose and cost-optimal. We settle some preliminary results showing that FPM is as powerful as many fundamental parallel computing models such as BSP and alternating Turing machine. Therefore, FvNA is also generally applicable to various fields other than ML.

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3

MORIARTY, K. J. M., and T. TRAPPENBERG. "PROGRAMMING TOOLS FOR PARALLEL COMPUTERS." International Journal of Modern Physics C 04, no. 06 (December 1993): 1285–94. http://dx.doi.org/10.1142/s0129183193001002.

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Although software tools already have a place on serial and vector computers they are becoming increasingly important for parallel computing. Message passing libraries, parallel operating systems and high level parallel languages are the basic software tools necessary to implement a parallel processing program. These tools up to now have been specific to each parallel computer system and a short survey will be given. The aim of another class of software tools for parallel computers is to help in writing or rewriting application programs. Because automatic parallelization tools are not very successful, an interactive component has to be incorporated. We will concentrate here on the discussion of SPEFY, a parallel program development facility.

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4

Shafarenko, A. "Software for Parallel Computers." Computing & Control Engineering Journal 3, no. 4 (1992): 194. http://dx.doi.org/10.1049/cce:19920049.

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5

Lea, R. M., and I. P. Jalowiecki. "Associative massively parallel computers." Proceedings of the IEEE 79, no. 4 (April 1991): 469–79. http://dx.doi.org/10.1109/5.92041.

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6

Kallstrom, M., and S. S. Thakkar. "Programming three parallel computers." IEEE Software 5, no. 1 (January 1988): 11–22. http://dx.doi.org/10.1109/52.1990.

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7

Moulic, J. R., and M. Kumar. "Highly parallel computers: Perspectives." Computing Systems in Engineering 3, no. 1-4 (January 1992): 1–5. http://dx.doi.org/10.1016/0956-0521(92)90088-z.

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8

Wood, Alan. "Parallel computers and computations." European Journal of Operational Research 27, no. 3 (December 1986): 385–86. http://dx.doi.org/10.1016/0377-2217(86)90338-3.

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9

Popov, Oleksandr, and Oleksiy Chystiakov. "On the Efficiency of Algorithms with Multi-level Parallelism." Physico-mathematical modelling and informational technologies, no. 33 (September 5, 2021): 133–37. http://dx.doi.org/10.15407/fmmit2021.33.133.

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The paper investigates the efficiency of algorithms for solving computational mathematics problems that use a multilevel model of parallel computing on heterogeneous computer systems. A methodology for estimating the acceleration of algorithms for computers using a multilevel model of parallel computing is proposed. As an example, the parallel algorithm of the iteration method on a subspace for solving the generalized algebraic problem of eigenvalues of symmetric positive definite matrices of sparse structure is considered. For the presented algorithms, estimates of acceleration coefficients and efficiency were obtained on computers of hybrid architecture using graphics accelerators, on multi-core computers with shared memory and multi-node computers of MIMD-architecture.

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10

Bevilacqua, A., and E. Loli Piccolomini. "Parallel image restoration on parallel and distributed computers." Parallel Computing 26, no. 4 (March 2000): 495–506. http://dx.doi.org/10.1016/s0167-8191(99)00115-5.

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11

Hattori, Masamitsu, Nobuhiro Ito, Wei Chen, and Koichi Wada. "Parallel matrix-multiplication algorithm for distributed parallel computers." Systems and Computers in Japan 36, no. 4 (April 2005): 48–59. http://dx.doi.org/10.1002/scj.10551.

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12

Caraiman, Simona, and Vasile Manta. "Parallel Simulation of Quantum Search." International Journal of Computers Communications & Control 5, no. 5 (December 1, 2010): 634. http://dx.doi.org/10.15837/ijccc.2010.5.2219.

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Simulation of quantum computers using classical computers is a computationally hard problem, requiring a huge amount of operations and storage. Parallelization can alleviate this problem, allowing the simulation of more qubits at the same time or the same number of qubits to be simulated in less time. A promising approach is represented by executing these simulators in Grid systems that can provide access to high performance resources. In this paper we present a parallel implementation of the QC-lib quantum computer simulator deployed as a Grid service. Using a specific scheme for partitioning the terms describing quantum states and efficient parallelization of the general singe qubit operator and of the controlled operators, very good speed-ups were obtained for the simulation of the quantum search problem.

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13

Groma, I., M. Verdier, P. Balogh, and B. Bakó. "Dislocation dynamics on parallel computers." Modelling and Simulation in Materials Science and Engineering 7, no. 5 (September 1, 1999): 795–803. http://dx.doi.org/10.1088/0965-0393/7/5/311.

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14

Qiu, B. "Book Review: Parallel Computers 2." International Journal of Electrical Engineering & Education 27, no. 1 (January 1990): 89. http://dx.doi.org/10.1177/002072099002700131.

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15

Ševčíková, Hana. "Statistical Simulations on Parallel Computers." Journal of Computational and Graphical Statistics 13, no. 4 (December 2004): 886–906. http://dx.doi.org/10.1198/106186004x12605.

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16

Li, H., and Q. F. Stout. "Reconfigurable SIMD massively parallel computers." Proceedings of the IEEE 79, no. 4 (April 1991): 429–43. http://dx.doi.org/10.1109/5.92038.

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17

Jevtovic, Milojko. "Parallel networking of the computers." Vojnotehnicki glasnik 55, no. 2 (2007): 198–205. http://dx.doi.org/10.5937/vojtehg0702198j.

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18

Deo, Narsingh. "Parallel Computers in Signal Processing." Defence Science Journal 35, no. 3 (January 24, 1985): 375–82. http://dx.doi.org/10.14429/dsj.35.6031.

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19

Venkataraman, G. "Parallel Computers : A Personal Overview." Defence Science Journal 46, no. 4 (January 1, 1996): 257–67. http://dx.doi.org/10.14429/dsj.46.4086.

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20

Parkinson, D. "Computers: More parallel than others." Nature 316, no. 6031 (August 1985): 765–66. http://dx.doi.org/10.1038/316765a0.

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21

Anderson, Alun. "Why should computers go parallel?" Nature 317, no. 6038 (October 1985): 565. http://dx.doi.org/10.1038/317565b0.

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22

Thole, Clemens-August, and Klaus Stüben. "Industrial simulation on parallel computers." Parallel Computing 25, no. 13-14 (December 1999): 2015–37. http://dx.doi.org/10.1016/s0167-8191(99)00065-4.

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23

Neta, Beny, and Heng-Ming Tai. "LU factorization on parallel computers." Computers & Mathematics with Applications 11, no. 6 (June 1985): 573–79. http://dx.doi.org/10.1016/0898-1221(85)90039-2.

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24

Nelson, Mark E., and James M. Bower. "Brain maps and parallel computers." Trends in Neurosciences 13, no. 10 (October 1990): 403–8. http://dx.doi.org/10.1016/0166-2236(90)90119-u.

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25

Gropp, William D., and David E. Keyes. "Domain decomposition on parallel computers." IMPACT of Computing in Science and Engineering 1, no. 4 (December 1989): 421–39. http://dx.doi.org/10.1016/0899-8248(89)90003-7.

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26

Crenell, K. M. "Molecular dynamics on parallel computers." Journal of Molecular Graphics 9, no. 2 (June 1991): 128–30. http://dx.doi.org/10.1016/0263-7855(91)85012-n.

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27

Levinthal, Adam, Pat Hanrahan, Mike Paquette, and Jim Lawson. "Parallel computers for graphics applications." ACM SIGARCH Computer Architecture News 15, no. 5 (November 1987): 193–98. http://dx.doi.org/10.1145/36177.36202.

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28

Levinthal, Adam, Pat Hanrahan, Mike Paquette, and Jim Lawson. "Parallel computers for graphics applications." ACM SIGOPS Operating Systems Review 21, no. 4 (October 1987): 193–98. http://dx.doi.org/10.1145/36204.36202.

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29

Levinthal, Adam, Pat Hanrahan, Mike Paquette, and Jim Lawson. "Parallel computers for graphics applications." ACM SIGPLAN Notices 22, no. 10 (October 1987): 193–98. http://dx.doi.org/10.1145/36205.36202.

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30

Lickly, Daniel J., and Philip J. Hatcher. "C++ and Massively Parallel Computers." Scientific Programming 2, no. 4 (1993): 193–202. http://dx.doi.org/10.1155/1993/450517.

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Our goal is to apply the software engineering advantages of object-oriented programming to the raw power of massively parallel architectures. To do this we have constructed a hierarchy of C++ classes to support the data-parallel paradigm. Feasibility studies and initial coding can be supported by any serial machine that has a C++ compiler. Parallel execution requires an extended Cfront, which understands the data-parallel classes and generates C*code. (C*is a data-parallel superset of ANSI C developed by Thinking Machines Corporation). This approach provides potential portability across parallel architectures and leverages the existing compiler technology for translating data-parallel programs onto both SIMD and MIMD hardware.

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31

Fincham, David. "Parallel Computers and Molecular Simulation." Molecular Simulation 1, no. 1-2 (November 1987): 1–45. http://dx.doi.org/10.1080/08927028708080929.

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32

Carlson, D. A. "Modified-mesh connected parallel computers." IEEE Transactions on Computers 37, no. 10 (1988): 1315–21. http://dx.doi.org/10.1109/12.5998.

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33

de O. Cruz, Adriano J. "Parallel algorithms for SIMD computers." Microprocessing and Microprogramming 28, no. 1-5 (March 1990): 85–90. http://dx.doi.org/10.1016/0165-6074(90)90154-2.

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34

Ramesh, R., R. M. Verma, T. Krishnaprasad, and I. V. Ramakrishnan. "Term matching on parallel computers." Journal of Logic Programming 6, no. 3 (May 1989): 213–28. http://dx.doi.org/10.1016/0743-1066(89)90014-9.

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35

Blech, R. A., E. J. Milner, A. Quealy, and S. E. Townsend. "Turbomachinery CFD on parallel computers." Computing Systems in Engineering 3, no. 6 (December 1992): 613–23. http://dx.doi.org/10.1016/0956-0521(92)90013-9.

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36

Doyle, John. "Serial, parallel and neural computers." Futures 23, no. 6 (July 1991): 577–93. http://dx.doi.org/10.1016/0016-3287(91)90080-l.

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37

Li, Keqin. "Scalable Parallel Matrix Multiplication on Distributed Memory Parallel Computers." Journal of Parallel and Distributed Computing 61, no. 12 (December 2001): 1709–31. http://dx.doi.org/10.1006/jpdc.2001.1768.

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38

Chronopoulos, Anthony Theodore, and Gang Wang. "Traffic Flow Simulation through Parallel Processing." Transportation Research Record: Journal of the Transportation Research Board 1566, no. 1 (January 1996): 31–38. http://dx.doi.org/10.1177/0361198196156600104.

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Numerical methods for solving traffic flow continuum models have been studied and efficiently implemented in traffic simulation codes in the past. Explicit and implicit methods have been used in traffic simulation codes in the past. Implicit methods allow a much larger time step size than explicit methods to achieve the same accuracy. However, at each time step a nonlinear system must be solved. The Newton method, coupled with a linear iterative method (Orthomin), is used. The efficient implementation of explicit and implicit numerical methods for solving the high-order flow conservation traffic model on parallel computers was studied. Simulation tests were run with traffic data from an 18-mile freeway section in Minnesota on the nCUBE2 parallel computer. These tests gave the same accuracy as past tests, which were performed on one-processor computers, and the overall execution time was significantly reduced.

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39

Ayuningtyas, Astika. "Pemrosesan Paralel pada Low Pass Filtering Menggunakan Transform Cosinus di MPI (Message Passing Interface)." Conference SENATIK STT Adisutjipto Yogyakarta 2 (November 15, 2016): 115. http://dx.doi.org/10.28989/senatik.v2i0.68.

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Parallel processing is a process of calculating two or more tasks simultaneously through the optimization of the computer system resource, one treatment models is a desktop system. The model allows to perform parallel processing between computers with specifications different computers. An implementation of a model network of workstations using MPI (Message Passing Interface). In this study, applied to the case of the low-pass filtering (LPF), a process in the image or the image of the shape of the filter that retrieves the data at low frequencies. filtering programs lowpass using the cosine transform MPI implemented by modifying the algorithm in the process on each node (computer). Depending on the test results, so that the processing speed of a parallel system is influenced by the number of nodes / processes and the number of frequency components that are processed. In the treatment of single larger process, the time it takes more and more and the value prop affects only the amount of high frequency data is filtered on the field. While parallel processing of more and more computers involved in the filter calculation process low-pass, plus the time required to perform the calculation.

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40

Hanuliak, Peter, and Michal Hanuliak. "Unique Analytical Model of Parallel Computers." International Review on Modelling and Simulations (IREMOS) 9, no. 4 (August 31, 2016): 246. http://dx.doi.org/10.15866/iremos.v9i4.9716.

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41

Byun, Chansup, and Guru P. Guruswamy. "Wing-body aeroelasticity on parallel computers." Journal of Aircraft 33, no. 2 (March 1996): 421–28. http://dx.doi.org/10.2514/3.46954.

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42

S., L. R., Philip J. Hatcher, Michael J. Quinn, Piyush Mehrotra, Joel Saltz, Robert Voigt, and Horst D. Simon. "Data-Parallel Programming on MIMD Computers." Mathematics of Computation 63, no. 207 (July 1994): 424. http://dx.doi.org/10.2307/2153588.

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43

Barbosa, V. C., R. Donangelo, and R. S. Wedemann. "A BUU Code for Parallel Computers." International Journal of Modern Physics C 09, no. 04 (June 1998): 573–83. http://dx.doi.org/10.1142/s0129183198000479.

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We have developed a distributed algorithm for the simulation of systems that evolve continuously, but which are also subject to discrete events that affect their evolution. As an example we consider the description through the BUU equations of the time evolution of a highly excited nuclear system, and show that this algorithm attains almost optimal speedups.

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44

EVANGELINOS, CONSTANTINOS, and GEORGE EM KARNIADAKIS. "PARALLEL CFD BENCHMARKS ON CRAY COMPUTERS." Parallel Algorithms and Applications 9, no. 3-4 (June 1996): 273–98. http://dx.doi.org/10.1080/10637199608915581.

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45

LI, TAO, and LIXIN TAO. "TOPOLOGICAL FEATURE MAPS ON PARALLEL COMPUTERS." International Journal of High Speed Computing 07, no. 04 (December 1995): 531–46. http://dx.doi.org/10.1142/s0129053395000294.

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46

Petersen, Johnny. "Computation on Parallel Message-Passing Computers." Physica Scripta T38 (January 1, 1991): 33. http://dx.doi.org/10.1088/0031-8949/1991/t38/006.

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47

Tanimoto, S. L. "Memory systems for highly parallel computers." Proceedings of the IEEE 79, no. 4 (April 1991): 403–15. http://dx.doi.org/10.1109/5.92036.

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48

Madisetti, V. K., and D. G. Messerschmitt. "Seismic migration algorithms on parallel computers." IEEE Transactions on Signal Processing 39, no. 7 (July 1991): 1642–54. http://dx.doi.org/10.1109/78.134401.

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49

Bailey, David H. "How Useful are Today's Parallel Computers?" Computers in Physics 6, no. 2 (1992): 216. http://dx.doi.org/10.1063/1.4823066.

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50

America, P. H. M., B. J. A. Hulshof, E. A. M. Odijk, F. Sijstermans, R. A. H. van Twist, and R. h. H. Wester. "Parallel computers for advanced information processing." IEEE Micro 10, no. 6 (December 1990): 12–15. http://dx.doi.org/10.1109/40.62724.

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