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Books on the topic 'Massive parallel computers'

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Published: 4 June 2021
Last updated: 8 February 2022

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1

1959-, Steyaert Michiel, ed. Analog VLSI integration of massive parallel signal processing systems. Kluwer Academic Publishers, 1997.

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2

Symposium on the Frontiers of Massively Parallel Computations (4th 1992 McLean, Va.). Fron tiers '92, the Fourth Symposium on the Frontiers of Massive Parallel Computation, October 19-21, 1992, McLean, Virginia. IEEE Computer Society Press, 1992.

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3

Models of massive parallelism: Analysis of cellular automata and neural networks. Springer, 1995.

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4

Bräunl, Thomas. Massiv parallele Programmierung mit dem Parallaxis-Modell. Springer-Verlag, 1990.

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5

Rishe, Naphtali. Storage and visualization of spatial data in a high-performance semantic database system: Technical report #95-15. National Aeronautics and Space Administration, 1995.

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6

Kogge, Peter M. Processor-In-Memory (PIM) based architectures for petaflops potential massively parallel processing: Final report, NASA grant NAG 5-2998. National Aeronautics and Space Administration, 1996.

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7

Zheng, Jianqing. CMOS VLSI layout and verification of a SIMD computer: A thesis ... National Aeronautics and Space Administration, 1996.

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8

Kotz, David. Dynamic file-access characteristics of a production parallel scientific workload. National Aeronautics and Space Administration, 1994.

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9

Sun, Xian-He. Efficient parallel kernel solvers for computational fluid dynamics applications: Final report, NASA contract no. NAG1-1672 (Jan. 1995-Aug. 97). National Aeronautics and Space Administration, 1997.

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10

Bokhari, Shahid H. Balancing contention and synchronization on the Intel Paragon. Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, 1996.

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11

Mavriplis, Dimitri. Three-dimensional high-lift analysis using a parallel unstructured multigrid solver. National Aeronautics and Space Administration, Langley Research Center, 1998.

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12

Kinget, Peter. Analog VLSI integration of massive parallel processing systems. 1997.

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13

Mango, Furnari Mario, and Workshop Massive Parallelism: Hardware, Software, and Applications (1994 : Capri, Italy), eds. Massive parallelism: Hardware, software, and applications : proceedings of the 2nd international workshop, Capri, Italy, 3-7 October, 1994. World Scientific, 1994.

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14

Frontiers '92, the Fourth Symposium on the Frontiers of Massive Parallel Computation, October 19-21, 1992, McLean, Virginia. IEEE Computer Society Press, 1992.

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15

J, Andersen, ed. Adapting the interior point method for the solution of LPs on serial, coarse grain parallel and massive parallel computers. Brunel University, Department of Mathematics and Statistics, 1990.

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16

Steyaert, Michiel, and Peter Kinget. Analog VLSI Integration of Massive Parallel Processing Systems (The Springer International Series in Engineering and Computer Science). Springer, 1996.

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17

Boden, Margaret A. 4. Artificial neural networks. Oxford University Press, 2018. http://dx.doi.org/10.1093/actrade/9780199602919.003.0004.

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Artificial neural networks (ANNs) are made up of many interconnected units, each one capable of computing only one thing. ANNs have myriad applications, from playing the stock market and monitoring currency fluctuations to recognizing speech or faces. ANNs are parallel-processing virtual machines implemented on classical computers. They are intriguing partly because they are very different from the virtual machines of symbolic AI. Sequential instructions are replaced by massive parallelism, top-down control by bottom-up processing, and logic by probability. ‘Artificial neural networks’ considers the wider implications of ANNs and discusses parallel distributed processing (PDP), learning in neural networks, back-propagation, deep learning, and hybrid systems.
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18

Geller, James. Innovative Applications of Massive Parallelism: Papers from the 1993 Spring Symposium. AAAI Press, 1993.

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19

Philip, Heidelberger, Nicol David, and Institute for Computer Applications in Science and Engineering., eds. Parallelized direct execution simulation of message-passing parallel programs. Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, 1994.

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20

Neuronale Topologiesynthese Fur Massiv Parallele Systeme (Europaische Hochschulschriften: Reihe 41, Informatik). Peter Lang Publishing, 2003.

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21

C, Hill Kueichien, and United States. National Aeronautics and Space Administration., eds. The EMCC/DARPA massively parallel electromagnetic scattering project. National Aeronautics and Space Administration, 1996.

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22

Storage and visualization of spatial data in a high-performance semantic database system: Technical report #95-15. National Aeronautics and Space Administration, 1995.

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23

Design considerations for parallel graphics libraries. Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, 1994.

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24

Bisseling, Rob H. Parallel Scientific Computation. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198788348.001.0001.

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This book explains how to use the bulk synchronous parallel (BSP) model to design and implement parallel algorithms in the areas of scientific computing and big data. Furthermore, it presents a hybrid BSP approach towards new hardware developments such as hierarchical architectures with both shared and distributed memory. The book provides a full treatment of core problems in scientific computing and big data, starting from a high-level problem description, via a sequential solution algorithm to a parallel solution algorithm and an actual parallel program written in the communication library BSPlib. Numerical experiments are presented for parallel programs on modern parallel computers ranging from desktop computers to massively parallel supercomputers. The introductory chapter of the book gives a complete overview of BSPlib, so that the reader already at an early stage is able to write his/her own parallel programs. Furthermore, it treats BSP benchmarking and parallel sorting by regular sampling. The next three chapters treat basic numerical linear algebra problems such as linear system solving by LU decomposition, sparse matrix-vector multiplication (SpMV), and the fast Fourier transform (FFT). The final chapter explores parallel algorithms for big data problems such as graph matching. The book is accompanied by a software package BSPedupack, freely available online from the author’s homepage, which contains all programs of the book and a set of test programs.
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25

United States. National Aeronautics and Space Administration., ed. Processor-In-Memory (PIM) based architectures for petaflops potential massively parallel processing: Final report, NASA grant NAG 5-2998. National Aeronautics and Space Administration, 1996.

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26

United States. National Aeronautics and Space Administration., ed. Processor-In-Memory (PIM) based architectures for petaflops potential massively parallel processing: Final report, NASA grant NAG 5-2998. National Aeronautics and Space Administration, 1996.

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27

United States. National Aeronautics and Space Administration., ed. Processor-In-Memory (PIM) based architectures for petaflops potential massively parallel processing: Final report, NASA grant NAG 5-2998. National Aeronautics and Space Administration, 1996.

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28

Institute for Computer Applications in Science and Engineering., ed. Runtime volume visualization for parallel CFD. Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, 1995.

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29

CMOS VLSI layout and verification of a SIMD computer: A thesis ... National Aeronautics and Space Administration, 1996.

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30

F, Knight Norman, and United States. National Aeronautics and Space Administration., eds. Nonlinear structural response using adaptive dynamic relaxation on a massively-parallel-processing system. National Aeronautics and Space Administration, 1994.

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31

Dynamic file-access characteristics of a production parallel scientific workload. National Aeronautics and Space Administration, 1994.

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32

Dynamic file-access characteristics of a production parallel scientific workload. National Aeronautics and Space Administration, 1994.

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33

David, Nicol, and Institute for Computer Applications in Science and Engineering., eds. Empirical study of parallel LRU simulation algorithms. Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, 1994.

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34

David, Nicol, and Institute for Computer Applications in Science and Engineering., eds. Empirical study of parallel LRU simulation algorithms. Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, 1994.

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35

Directions in parallel proogramming: HPF, shared virtual memory and object parallelism in pC++. Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, 1994.

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36

Efficient parallel kernel solvers for computational fluid dynamics applications: Final report, NASA contract no. NAG1-1672 (Jan. 1995-Aug. 97). National Aeronautics and Space Administration, 1997.

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37

S, Rhodes George, and United States. National Aeronautics and Space Administration., eds. Portable parallel stochastic optimization for the design of aeropropulsion components. Applied Research Associates, Inc., 1994.

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38

EUPDF, an Eulerian-based Monte Carlo probability density function (PDF) solver: User's manual. National Aeronautics and Space Administration, Lewis Research Center, 1998.

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39

Center, Lewis Research, ed. EUPDF, an Eulerian-based Monte Carlo probability density function (PDF) solver: User's manual. National Aeronautics and Space Administration, Lewis Research Center, 1998.

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40

EUPDF, an Eulerian-based Monte Carlo probability density function (PDF) solver: User's manual. National Aeronautics and Space Administration, Lewis Research Center, 1998.

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41

Center, Lewis Research, ed. EUPDF, an Eulerian-based Monte Carlo probability density function (PDF) solver: User's manual. National Aeronautics and Space Administration, Lewis Research Center, 1998.

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42

Eriksson, Olle, Anders Bergman, Lars Bergqvist, and Johan Hellsvik. Implementation. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198788669.003.0007.

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In this chapter, we will present the technical aspects of atomistic spin dynamics, in particular how the method can be implemented in an actual computer software. This involves calculation of effective field and creation of neighbour lists for setting up the geometry of the system of interest as well as choosing a suitable integrator scheme for the SLL (or SLLG) equation. We also give examples of extraction and processing of relevant observables that are common output from simulations. Atomistic spin dynamics simulations could be a computationally heavy tool but it is also very well adapted for modern computer architectures like massive parallel computing and/or graphics processing units and we provide examples how to utilize these architectures in an efficient manner. We use our own developed software UppASD as example, but the discussion could be applied to any other atomistic spin dynamics software.
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43

W, Crockett Thomas, and Langley Research Center, eds. Parallel visualization of large-scale aerodynamics calculations: A case study on the Cray T3E. National Aeronautics and Space Administration, Langley Research Center, 1999.

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44

W, Crockett Thomas, and Langley Research Center, eds. A scalable parallel cell-projection volume rendering algorithm for three-dimensional unstructured data. National Aeronautics and Space Administration, Langley Research Center, 1997.

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45

W, Crockett Thomas, and Langley Research Center, eds. A scalable parallel cell-projection volume rendering algorithm for three-dimensional unstructured data. National Aeronautics and Space Administration, Langley Research Center, 1997.

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46

W, Crockett Thomas, and Langley Research Center, eds. A scalable parallel cell-projection volume rendering algorithm for three-dimensional unstructured data. National Aeronautics and Space Administration, Langley Research Center, 1997.

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47

W, Crockett Thomas, and Langley Research Center, eds. A scalable parallel cell-projection volume rendering algorithm for three-dimensional unstructured data. National Aeronautics and Space Administration, Langley Research Center, 1997.

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48

F, Rohrbach, and European Organization for Nuclear Research., eds. The MPPC Project final report: Massively Parallel Processing Collaboration. CERN, European Organization for Nuclear Research, 1993.

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49

R, Biswas, and United States. National Aeronautics and Space Administration., eds. Unstructured adaptive grid computations on an array of SMPs. Research Institute for Advanced Computer Science, NASA Ames Research Center, 1996.

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50

Mesinger, Fedor, Miodrag Rančić, and R. James Purser. Numerical Methods in Atmospheric Models. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228620.013.617.

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The astonishing development of computer technology since the mid-20th century has been accompanied by a corresponding proliferation in the numerical methods that have been developed to improve the simulation of atmospheric flows. This article reviews some of the numerical developments concern the ongoing improvements of weather forecasting and climate simulation models. Early computers were single-processor machines with severely limited memory capacity and computational speed, requiring simplified representations of the atmospheric equations and low resolution. As the hardware evolved and memory and speed increased, it became feasible to accommodate more complete representations of the dynamic and physical atmospheric processes. These more faithful representations of the so-called primitive equations included dynamic modes that are not necessarily of meteorological significance, which in turn led to additional computational challenges. Understanding which problems required attention and how they should be addressed was not a straightforward and unique process, and it resulted in the variety of approaches that are summarized in this article. At about the turn of the century, the most dramatic developments in hardware were the inauguration of the era of massively parallel computers, together with the vast increase in the amount of rapidly accessible memory that the new architectures provided. These advances and opportunities have demanded a thorough reassessment of the numerical methods that are most successfully adapted to this new computational environment. This article combines a survey of the important historical landmarks together with a somewhat speculative review of methods that, at the time of writing, seem to hold out the promise of further advancing the art and science of atmospheric numerical modeling.
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