An Introduction To High - Performance Scientific Computing

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High-Performance Scientific Computing: First Jara-HPC Symposium, Jhpcs 2016, Aachen, Germany, October 4-5, 2016, Revised Selected Papers

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Mathematical Modelling and Computing in Physics, Chemistry and Biology Springer, Berlin

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This book  keeps an eye in the direction of applications of advanced and high performance scientific computing in  describing the behavior of natural and constructed systems, e.g. chaos, bifurcation, fractal, Lyapunov exponent, period doubling, Poincaré map, strange attractor etc. With the aid of powerful computers the modem theory of chaos and its geometry, the fractals, and attractors are developed. The concepts of object oriented computing are introduced early in the text and steadily expanded as one progresses through the chapters. The beginning of each chapter is of an introductory nature, followed by practical applications, the discussion of numerical results, theoretical investigations on nonlinear stability and convergence.This is the first complete introduction to process modeling and computing that fully integrates software tools - enabling professionals and students to master critical techniques hands on through computer simulations based on the popular MATLAB environment. The book offers a simple tool for all those oscillations that are travelling through the world, helping them discover its hidden beauty.Many applications as well as results of computer simulations are presented. The center of concern is set on existing as well as emerging continuous methods of investigations useful for researchers, engineers and practitioners active in many and often interdisciplinary fields, where physics, electrochemistry, biology and medicine play a key role. Coverage includes:- Dynamic behavior of nonlinear systems,- Fundamental descriptions of processes exhibiting nonlinear oscillations,- Mechanism and function of structures of nonlinear oscillations' patterns,- Analysis of dynamical oscillations in electric circuits and systems,- Artificial intelligence models of natural systems- Nonlinear oscillations in chemistry, biology and medicine,- Oscillations in mechanics and transport systems,- Oscillations in fractional-order systems,- Energy harvesting systems from the surrounding environment.With an insatiable appetite for exploring the surrounding world and doing research, this book can help readers quickly find ways to use new computers and facilitate the quest for greater knowledge and understanding of reality. The reach of novelty of the book ranges from new mathematical ideas to motivating questions and science issues in many subject areas

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Software Automatic Tuning Springer-Verlag New York Inc.

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Automatic Performance Tuning is a new software paradigm which enables software to be high performance in any computing environment. Its methodologies have been developed over the past decade, and it is now rapidly growing in terms of its scope and applicability, as well as in its scientific knowledge and technological methods. Software developers and researchers in the area of scientific and technical computing, high performance database systems, optimized compilers, high performance systems software, and low-power computing will find this book to be an invaluable reference to this powerful new paradigm.

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Numerical Simulation in Science and Engineering Friedrich Vieweg & Sohn Verlagsgesellschaft mbH

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Founded in April, 1992 and financed by the State of Bavaria and the Bavarian Re search Foundation, the Bavarian Consortium for High Performance Scientific Comput ing (FORTWIHR) consists of more than 40 scientists working in the fields of engineer ing sciences, applied mathematics, and computer science at the Technische Universitat Munchen and at the Friedrich-Alexander-Universitat Erlangen-Nurnberg. Its inter disciplinary concept is based on the recognition that the increasing significance of the yet young discipline High Performance Scientific Computing (HPSC) can only be given due consideration if the technical knowledge of the engineer, the numerical methods of the mathematician, and the computers and up to date methods of computer science are all applied equally. Besides the aim to introduce HPSC into the graduate degree program at the universi ties, there is a strong emphasis on cooperation with industry in all areas of research. Direct cooperation and a transfer of knowledge through training courses and confer ences take place in order to ensure the rapid utilization of all results of research. In this spirit, FORTWIHR annually organizes symposiums on High Performance Scientific Computing and Numerical Simulation in Science and Engineering.

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CUDA Fortran for Scientists and Engineers Elsevier Science & Technology

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CUDA Fortran for Scientists and Engineers shows how high-performance application developers can leverage the power of GPUs using Fortran, the familiar language of scientific computing and supercomputer performance benchmarking. The authors presume no prior parallel computing experience, and cover the basics along with best practices for efficient GPU computing using CUDA Fortran. To help you add CUDA Fortran to existing Fortran codes, the book explains how to understand the target GPU architecture, identify computationally intensive parts of the code, and modify the code to manage the data and parallelism and optimize performance. All of this is done in Fortran, without having to rewrite in another language. Each concept is illustrated with actual examples so you can immediately evaluate the performance of your code in comparison. You can leverage the power of GPU computing with PGI's CUDA Fortran compiler. You can gain insights from members of the CUDA Fortran language development team. It includes multi-GPU programming in CUDA Fortran, covering both peer-to-peer and message passing interface (MPI) approaches.It includes full source code for all the examples and several case studies. You can download source code and slides from the book's companion website.

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Data Science and Predictive Analytics Springer, Berlin

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This textbook integrates important mathematical foundations, efficient computational algorithms, applied statistical inference techniques, and cutting-edge machine learning approaches to address a wide range of crucial biomedical informatics, health analytics applications, and decision science challenges. Each concept in the book includes a rigorous symbolic formulation coupled with computational algorithms and complete end-to-end pipeline protocols implemented as functional R electronic markdown notebooks. These workflows support active learning and demonstrate comprehensive data manipulations, interactive visualizations, and sophisticated analytics. The content includes open problems, state-of-the-art scientific knowledge, ethical integration of heterogeneous scientific tools, and procedures for systematic validation and dissemination of reproducible research findings.Complementary to the enormous challenges related to handling, interrogating, and understanding massive amounts of complex structured and unstructured data, there are unique opportunities that come with access to a wealth of feature-rich, high-dimensional, and time-varying information. The topics covered in Data Science and Predictive Analytics address specific knowledge gaps, resolve educational barriers, and mitigate workforce information-readiness and data science deficiencies. Specifically, it provides a transdisciplinary curriculum integrating core mathematical principles, modern computational methods, advanced data science techniques, model-based machine learning, model-free artificial intelligence, and innovative biomedical applications. The book's fourteen chapters start with an introduction and progressively build foundational skills from visualization to linear modeling, dimensionality reduction, supervised classification, black-box machine learning techniques, qualitative learning methods, unsupervised clustering, model performance assessment, feature selection strategies, longitudinal data analytics, optimization, neural networks, and deep learning. The second edition of the book includes additional learning-based strategies utilizing generative adversarial networks, transfer learning, and synthetic data generation, as well as eight complementary electronic appendices. This textbook is suitable for formal didactic instructor-guided course education, as well as for individual or team-supported self-learning. The material is presented at the upper-division and graduate-level college courses and covers applied and interdisciplinary mathematics, contemporary learning-based data science techniques, computational algorithm development, optimization theory, statistical computing, and biomedical sciences. The analytical techniques and predictive scientific methods described in the book may be useful to a wide range of readers, formal and informal learners, college instructors, researchers, and engineers throughout the academy, industry, government, regulatory, funding, and policy agencies. The supporting book website provides many examples, datasets, functional scripts, complete electronic notebooks, extensive appendices, and additional materials.

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Python Data Analytics Springer, Berlin

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Explore the latest Python tools and techniques to help you tackle the world of data acquisition and analysis. You'll review scientific computing with NumPy, visualization with matplotlib, and machine learning with scikit-learn. This third edition is fully updated for the latest version of Python and its related libraries, and includes coverage of social media data analysis, image analysis with OpenCV, and deep learning libraries. Each chapter includes multiple examples demonstrating how to work with each library. At its heart lies the coverage of pandas, for high-performance, easy-to-use data structures and tools for data manipulationAuthor Fabio Nelli expertly demonstrates using Python for data processing, management, and information retrieval. Later chapters apply what you've learned to handwriting recognition and extending graphical capabilities with the JavaScript D3 library. Whether you are dealing with sales data, investment data, medical data, web page usage, or other data sets, Python Data Analytics, Third Edition is an invaluable reference with its examples of storing, accessing, and analyzing data.What You'll LearnUnderstand the core concepts of data analysis and the Python ecosystemGo in depth with pandas for reading, writing, and processing dataUse tools and techniques for data visualization and image analysisExamine popular deep learning libraries Keras, Theano,TensorFlow, and PyTorchWho This Book Is ForExperienced Python developers who need to learn about Pythonic tools for data analysis

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Parallel Programming in OpenMP Elsevier Science & Technology

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The rapid and widespread acceptance of shared-memory multiprocessor architectures has created a pressing demand for an efficient way to program these systems. At the same time, developers of technical and scientific applications in industry and in government laboratories find they need to parallelize huge volumes of code in a portable fashion. OpenMP, developed jointly by several parallel computing vendors to address these issues, is an industry-wide standard for programming shared-memory and distributed shared-memory multiprocessors. It consists of a set of compiler directives and library routines that extend FORTRAN, C, and C++ codes to express shared-memory parallelism. "Parallel Programming in OpenMP" is the first book to teach both the novice and expert parallel programmers how to program using this new standard. The authors, who helped design and implement OpenMP while at SGI, bring a depth and breadth to the book as compiler writers, application developers, and performance engineers. It is designed so that expert parallel programmers can skip the opening chapters, which introduce parallel programming to novices, and jump right into the essentials of OpenMP.It presents all the basic OpenMP constructs in FORTRAN, C, and C++. It emphasizes practical concepts to address the concerns of real application developers. It includes high quality example programs that illustrate concepts of parallel programming as well as all the constructs of OpenMP. It serves as both an effective teaching text and a compact reference. It includes end-of-chapter programming exercises.

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Application of MATLAB and SIMULINK Modeling for Beginners LAP Lambert Academic Publishing

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Design engineers, inventors, developers, innovators, engineering students, science students and all those interested in design, simulation and modeling require comprehensive tutorial information on writing and efficient use of MATLAB/SIMULINK software to solve various technical computing problems encountered daily. This book is intended to meet this need in the areas of programming, simulation and modeling. MATLAB is a high performance language for visualizations and programming in an easy to use environment where problems and solutions are expressed in familiar mathematical notations. MATLAB is used in: Mathematics and Computation; Algorithmic Development; Data Acquisition; Modeling, Simulation and Prototyping; Data analysis, exploration and visualization; Scientific and Engineering Graphics application development & GUI building. This is an introductory course suitable for those with no experience at all with MATLAB/SIMULINK. It is written in a self-contained way; if you go through the notes, all the new commands and ideas are explained as they are introduced.

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