2021 Ieee Acm Workshop On Machine Learning In High Performance Computing Environments Mlhpc
Download 2021 Ieee Acm Workshop On Machine Learning In High Performance Computing Environments Mlhpc PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get 2021 Ieee Acm Workshop On Machine Learning In High Performance Computing Environments Mlhpc 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.
2021 IEEE ACM Workshop on Machine Learning in High Performance Computing Environments (MLHPC)
The intent of this workshop is to bring together researchers, practitioners, and scientific communities to discuss methods that utilize extreme scale systems for machine learning This workshop will focus on the greatest challenges in utilizing HPC for machine learning and methods for exploiting data parallelism, model parallelism, ensembles, and parameter search We invite researchers and practitioners to participate in this workshop to discuss the challenges in using HPC for machine learning and to share the wide range of applications that would benefit from HPC powered machine learning
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.