High Performance Computing for Computational Science - Vecpar 2016: 12th International Conference, Porto, Portugal, June 28-30, 2016, Revised Selected

Książki

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Data Science and Predictive Analytics Springer, Berlin

Książki / Literatura obcojęzyczna

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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Intelligent System Design Springer, Berlin

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This book presents a collection of high-quality, peer-reviewed research papers from the 7th International Conference on Information System Design and Intelligent Applications (India 2022), held at BVRIT Hyderabad College of Engineering for Women, Hyderabad, Telangana, India, from February 25 to 26, 2022. It covers a wide range of topics in computer science and information technology, including data mining and data warehousing, high-performance computing, parallel and distributed computing, computational intelligence, soft computing, big data, cloud computing, grid computing and cognitive computing.

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Programming Massively Parallel Processors Elsevier Science & Technology

Książki / Literatura obcojęzyczna

Programming Massively Parallel Processors: A Hands-on Approach shows both student and professional alike the basic concepts of parallel programming and GPU architecture. Various techniques for constructing parallel programs are explored in detail. Case studies demonstrate the development process, which begins with computational thinking and ends with effective and efficient parallel programs. Topics of performance, floating-point format, parallel patterns, and dynamic parallelism are covered in depth.  For this new edition, the authors are updating their coverage of CUDA, including the concept of unified memory, and expanding content in areas such as threads, while still retaining its concise, intuitive, practical approach based on years of road-testing in the authors' own parallel computing courses. Teaches computational thinking and problem-solving techniques that facilitate high-performance parallel computing Updated to utilize CUDA version 10.0, NVIDIA's software development tool created specifically for massively parallel environments Features new content on unified memory, as well as expanded content on threads, streams, warp divergence, and OpenMP Includes updated and new case studies

Sklep: Libristo.pl

Programming Massively Parallel Processors Elsevier Science & Technology

Książki / Literatura obcojęzyczna

Programming Massively Parallel Processors: A Hands-on Approach shows both student and professional alike the basic concepts of parallel programming and GPU architecture. Various techniques for constructing parallel programs are explored in detail. Case studies demonstrate the development process, which begins with computational thinking and ends with effective and efficient parallel programs. Topics of performance, floating-point format, parallel patterns, and dynamic parallelism are covered in depth. For this new edition, the authors are updating their coverage of CUDA, including coverage of newer libraries such as CuDNN, moving content that has become less important to appendices, adding two new chapters on parallel patterns, and updating case studies to reflect current industry practices, while still retaining its concise, intuitive, practical approach based on years of road-testing in the authors' own parallel computing courses. Teaches computational thinking and problem-solving techniques that facilitate high-performance parallel computingUtilizes CUDA version 7.5, NVIDIA's software development tool created specifically for massively parallel environmentsContains new and updated case studiesIncludes coverage of newer libraries such as CuDNN for Deep Learning

Sklep: Libristo.pl