Integrating Machine Learning Into Hpc Based Simulations And Analytics
Download Integrating Machine Learning Into Hpc Based Simulations And Analytics PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Integrating Machine Learning Into Hpc Based Simulations And Analytics book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Integrating Machine Learning Into HPC-Based Simulations and Analytics
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.
AI Methods for Environmental Protection and Resource Conservation
AI methods are harnessed for environmental protection and resource conservation, offering innovative solutions to some of the world’s most pressing ecological challenges. From monitoring biodiversity and predicting climate change impacts to optimizing energy consumption and managing waste, AI enables more efficient, data-driven decision-making. Machine learning algorithms analyze large datasets from satellite imagery, sensors, and environmental models to detect deforestation, pollution levels, and the health of ecosystems. AI-powered systems can improve resource management by forecasting demand and consumption patterns, enhancing the sustainability of water, energy, and agricultural systems. By automating processes, identifying trends, and proposing actionable insights, AI may empower governments, organizations, and individuals to implement more effective conservation strategies. AI Methods for Environmental Protection and Resource Conservation explores the role of intelligent technology in environmental science. It examines how artificial intelligence assists in conservation, deforestation monitoring, weather forecasting, CO2 removal, and sustainable transportation. This book covers topics such as big data, energy engineering, and sustainable development, and is a useful resource for engineers, business owners, academicians, researchers, and environmental scientists.
Handbook of Research on Integrating Machine Learning Into HPC-Based Simulations and Analytics
Author: Belgacem Ben Youssef
language: en
Publisher: Engineering Science Reference
Release Date: 2024-07-31
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.