integrating machine learning hpc based (12 Ergebnisse)

Zustand: New. In.

Paperback. Zustand: new. Paperback. Researchers are increasingly using machine learning (ML) models to analyze data and simulate complex systems and phenomena. Small-scale computing systems used for training, validation, and testing of these ML models are no longer sufficient for grand-challenge problems characterized by large volumes of data generated at a much higher rate than before, surpassing by far the computing capabilities currently available in many cyberinfrastructure platforms. By associating high-performance computing (HPC) with ML environments, scientists and engineers would be able to enhance not only the scalability but also the performance of their predictive ML models. The Handbook of Research on Integrating Machine Learning Into HPC-Based Simulations and Analytics presents recent research efforts in designing and using ML techniques on HPC systems and discusses some of the results achieved thus far by cutting-edge relevant contributions. Covering topics such as data analytics, deep learning, and networking, this major reference work is ideal for computer scientists, academicians, engineers, researchers, scholars, practitioners, librarians, instructors, and students. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Paperback. Zustand: new. Paperback. Researchers are increasingly using machine learning (ML) models to analyze data and simulate complex systems and phenomena. Small-scale computing systems used for training, validation, and testing of these ML models are no longer sufficient for grand-challenge problems characterized by large volumes of data generated at a much higher rate than before, surpassing by far the computing capabilities currently available in many cyberinfrastructure platforms. By associating high-performance computing (HPC) with ML environments, scientists and engineers would be able to enhance not only the scalability but also the performance of their predictive ML models. The Handbook of Research on Integrating Machine Learning Into HPC-Based Simulations and Analytics presents recent research efforts in designing and using ML techniques on HPC systems and discusses some of the results achieved thus far by cutting-edge relevant contributions. Covering topics such as data analytics, deep learning, and networking, this major reference work is ideal for computer scientists, academicians, engineers, researchers, scholars, practitioners, librarians, instructors, and students. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Zustand: New. In.

Zustand: New.

Paperback. Zustand: new. Paperback. Researchers are increasingly using machine learning (ML) models to analyze data and simulate complex systems and phenomena. Small-scale computing systems used for training, validation, and testing of these ML models are no longer sufficient for grand-challenge problems characterized by large volumes of data generated at a much higher rate than before, surpassing by far the computing capabilities currently available in many cyberinfrastructure platforms. By associating high-performance computing (HPC) with ML environments, scientists and engineers would be able to enhance not only the scalability but also the performance of their predictive ML models. The Handbook of Research on Integrating Machine Learning Into HPC-Based Simulations and Analytics presents recent research efforts in designing and using ML techniques on HPC systems and discusses some of the results achieved thus far by cutting-edge relevant contributions. Covering topics such as data analytics, deep learning, and networking, this major reference work is ideal for computer scientists, academicians, engineers, researchers, scholars, practitioners, librarians, instructors, and students. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Zustand: New.

Hardcover. Zustand: new. Hardcover. Researchers are working together to build intelligent systems that exploit a variety of real datasets using ML techniques to address problems and challenges in diverse research fields. More collaboration between the HPC and ML communities is encouraged for rapid and seamless progress toward an ecosystem that effectively serves both of these communities. As the performance improvements provided by semiconductor scaling diminish, future HPC systems are expected to exhibit an increased level of heterogeneity. These systems need to be flexible and provide low latency at all levels to effectively support new use cases and paradigms. Further, new tools and benchmarks are required to overcome the common challenges across HPC and ML applications. New programming tools, languages, compilers, and operating and runtime systems may also be needed to provide new abstractions, capabilities, and services. This book presents to the reader recent research efforts in designing and using ML techniques on HPC systems, discusses some of the results achieved thus far by cutting-edge contributions, as well as highlights some of the ongoing research works in these two fields. Another objective is to identify research challenges and opportunities in the area spanning the intersection of HPC and ML. It is ideal for students, academics, researchers, computer scientists, computer and electrical engineers, as well as experts in the field of machine learning and high-performance computing. Presents recent research efforts in designing and using ML techniques on HPC systems, discusses some of the results achieved thus far by cutting-edge contributions, and highlights some of the ongoing research works in these two fields. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Zustand: As New. Unread book in perfect condition.

Zustand: As New. Unread book in perfect condition.

Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Presents recent research efforts in designing and using ML techniques on HPC systems, discusses some of the results achieved thus far by cutting-edge contributions, and highlights some of the ongoing research works in these two fields.

Buch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 'This edited book aims to present to the reader recent research efforts in designing and using ML techniques on HPC systems, discuss some of the results achieved thus far by cutting-edge relevant contributions. Another objective is to identify research challenges and opportunities in the area spanning the intersection of HPC and ML. In fact, further collaboration between the HPC and ML communities is encouraged for rapid and seamless progress toward an ecosystem that effectively serves both of these communities. Furthermore, new tools and benchmarks are required to overcome the common challenges across HPC and ML applications. The goals of this form of convergence are fourfold: 1. Obtain optimized solutions that show discernible reduction in compute requirements. 2. Facilitate having a more dynamic view of domain sciences. 3. Develop integrated knowledge through interdisciplinary collaboration. 4. Stimulate innovations with deep societal impact through the provisioning of new advances in scientific research spanning many application areas'.