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Call for Chapters
Proposals Submission Deadline: August 20, 2019
Full Chapters Due: August 20, 2019
Submission Date: October 4, 2019
Introduction
High performance computing has come a long way since 1972. The democratization of the computing power in the past two decades have ensured that high performance computing is not a niche forte but a area where all can contribute. Developments in High performance computing research, tools, and improved software development models over the past decade has resulted in HPC being used in various scientific and engineering domains. The emergence of the Massively parallel architectures like accelerators and GPU’s have significantly created renewed interest in the area of Deep learning, machine learning and Artificial Intelligence. We are at the cross roads where the intersection of HPC and AI are going to create applications and solutions for the real-life problems in various domains. This interesting combination demands newer skill sets, research approaches and thinking. This edited book tries to consolidate the experience of researcher’s, practitioners and industry experts about this exciting convergence.
Objective
In the past few decades, high performance computing has evolved from distributed computing, multi core computing to massively parallel computing using GPU’s, FPGA’s etc. Recently AI concepts like machine learning and Deep learning have gained practical importance due availability of accelerators and huge data. AI involves huge computations and large datasets in its applications of ML/DL whose fundamental are linear regression and neural networks. Hardware’s like accelerators are capable of performing huge computation. Also, huge dataset of images and other data points have been collected with growth of internet. But still there is long way to go since many of AI concepts like reinforcement learning to be brought into practical applications and HPC has bigger role to make it happen.
ML/DL Practical applications can be built using programming frameworks like tensor flow, MxNet and others. These frameworks rely on parallel programming at low level and are capable of utilizing CPU/GPU hardware architecture available. But, just utilizing parallel programming is not sufficient since HPC has to evolve to meet the demands of AI applications in terms of algorithms, network communications, processors architecture high-performance and storage. HPC provides numerous opportunities to develop newer hardware’s like processors and storage suitable for AI applications.
Target Audience
The target audience of this book will be composed of professionals and researchers working in the field of High performance computing, Artificial Intelligence, Machine Learning, GPU Programming, Parallel Programming, Scientific Applications and Exascale computing applications. Moreover, the book will provide insights and support from practitioners and academia in order to highlight the most debated aspects in the field.
Recommended Topics
High-Performance computing and Machine learning
Big-capacity scalable storage for Ai applications
Scalable Deep Learning
Integrating AI into infrastructure and applications
Scalable representations/languages for AI/DL/ML applications Digital Twins: Impact in manufacturing, healthcare, weather etc....
Distributed deep learning environment
High-performance computing and Industry 4.0
Convergence of HPC, AI and Cloud
DL/ML applications in various Scientific/ Engineering applications DL/ML applications in sectors like Energy, Oil, Gas etc.
Submission Procedure
Researchers and practitioners are invited to submit their full chapters on or before August 20, 2019, a chapter proposal of 1,000 to 2,000 words clearly explaining the mission and concerns of his or her proposed chapter. Authors will be notified by August 30, 2019, about the status of their proposals and sent chapter guidelines. All interested authors must consult the guidelines for manuscript submissions at http://www.igi-global.com/publish/contributor-resources/before-you-write/ prior to submission. All submitted chapters will High-performance on a double-blind review basis. Contributors may also be requested to serve as reviewers for this project.
Note: There are no submission or acceptance fees for manuscripts submitted to this book publication, Trust in Knowledge Management and Systems in Organizations. All manuscripts are accepted based on a double-blind peer review editorial process.
All proposals should be submitted through the eEditorial Discovery®TM online submission manager.
Publisher
This book is scheduled to be published by IGI Global (formerly Idea Group Inc.), publisher of the "Information Science Reference" (formerly Idea Group Reference), "Medical Information Science Reference," "Business Science Reference," and "Engineering Science Reference" imprints. For additional information regarding the publisher, please visit www.igi-global.com. This publication is anticipated to be released in 2020.
Important Dates
• Full chapter submission: August 20, 2019
• Review results to editor: September 3, 2019
• Review results to authors: September 8, 2019
• Progress report 2: September 8, 2019
• Revisions due from authors: September 28, 2019
• Final acceptance/rejection notification due to authors: October 2, 2019
• All final materials from authors: October 4, 2019
• Final preface and table of contents: October 5, 2019
• Final deadline: October 7, 2019
Inquiries
Satyadhyan Chickerur Ph.D., MBA(Edu. Mgmt.)
Professor, School of Computer Science and Engineering,
Head, Centre for High Performance Computing,
KLE Technological University(formerly B V B college of Engg and Technology) Hubballi - 580031, Karnataka, India
Tel: +91(836)237-8415
[email protected] [email protected] http://tinyurl.com/chickerursr
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