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1

Corporation, Microsoft, ed. Parallel programming with Microsoft .NET: Design patterns for decomposition and coordination on multicore architectures. [S.l: Microsoft], 2010.

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2

LINQ to objects using C♯ 4.0: Using and extending LINQ to objects and parallel LINQ (PLINQ). Upper Saddle River, NJ: Addison-Wesley, 2010.

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3

Boudreau, Joseph F., and Eric S. Swanson. Parallel computing. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198708636.003.0009.

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This chapter describes various approaches to concurrency, or “parallel programming”. An overview of high performance computing is followed with a review of Flynn’s taxonomy of parallel computing. Three methods for implementing parallel code using the frameworks provided by MPI, openMP, and C++ threads are presented. The use of the C++ constructs mutex and future to resolve issues of synchronization are discussed. All methods are illustrated with an embarrassingly parallel application to a Monte Carlo integral and common pitfalls are presented. The chapter closes with a discussion and example of the utility of forking processes and the use of C++ sockets and their application in a client/server environment.
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Hillar, Gastón C. Professional Parallel Programming with C#: Master Parallel Extensions With . NET 4. Wiley & Sons, Incorporated, John, 2010.

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5

Professional Parallel Programming with C#: Master Parallel Extensions with .NET 4. John Wiley, 2010.

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Hillar, Gastón C. Professional Parallel Programming with C#: Master Parallel Extensions With . NET 4. Wiley & Sons, Incorporated, John, 2010.

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7

Hillar, Gastón C. Professional Parallel Programming with C#: Master Parallel Extensions With . NET 4. Wiley & Sons, Incorporated, John, 2010.

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8

Blewett, Richard, Andrew Clymer, and Rock Solid Knowledge Ltd. Pro Asynchronous Programming With . NET. Springer, 2014.

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9

Pro Asynchronous Programming with .NET. Apress, 2013.

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10

Shasha, Dennis E., and Jessica P. Chang. Storing Clocked Programs Inside DNA: A Simplifying Framework for Nanocomputing. Morgan & Claypool Publishers, 2011.

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11

Konar, Amit, Alakananda Bhattacharya, and Ajit K. Mandal. Parallel and Distributed Logic Programming: Towards the Design of a Framework for the Next Generation Database Machines. Springer London, Limited, 2006.

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Konar, Amit, Alakananda Bhattacharya, and Ajit K. Mandal. Parallel and Distributed Logic Programming: Towards the Design of a Framework for the Next Generation Database Machines. Springer, 2010.

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Parallel and Distributed Logic Programming: Towards the Design of a Framework for the Next Generation Database Machines (Studies in Computational Intelligence). Springer, 2006.

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14

Delphi GUI Programming with FireMonkey: Unleash the Full Potential of the FMX Framework to Build Exciting Cross-Platform Apps with Embarcadero Delphi. Packt Publishing, Limited, 2020.

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15

Bäck, Thomas. Evolutionary Algorithms in Theory and Practice. Oxford University Press, 1996. http://dx.doi.org/10.1093/oso/9780195099713.001.0001.

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This book presents a unified view of evolutionary algorithms: the exciting new probabilistic search tools inspired by biological models that have immense potential as practical problem-solvers in a wide variety of settings, academic, commercial, and industrial. In this work, the author compares the three most prominent representatives of evolutionary algorithms: genetic algorithms, evolution strategies, and evolutionary programming. The algorithms are presented within a unified framework, thereby clarifying the similarities and differences of these methods. The author also presents new results regarding the role of mutation and selection in genetic algorithms, showing how mutation seems to be much more important for the performance of genetic algorithms than usually assumed. The interaction of selection and mutation, and the impact of the binary code are further topics of interest. Some of the theoretical results are also confirmed by performing an experiment in meta-evolution on a parallel computer. The meta-algorithm used in this experiment combines components from evolution strategies and genetic algorithms to yield a hybrid capable of handling mixed integer optimization problems. As a detailed description of the algorithms, with practical guidelines for usage and implementation, this work will interest a wide range of researchers in computer science and engineering disciplines, as well as graduate students in these fields.
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