Call for Chapters: Handbook of Research on Integrating Machine Learning Into HPC-Based Simulations and Analytics


Belgacem Ben Youssef, King Saud University, Saudi Arabia
Mohamed Maher Ben Ismail, King Saud University, Saudi Arabia

Call for Chapters

Proposals Submission Deadline: January 30, 2023
Full Chapters Due: March 20, 2023


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. While ML has achieved remarkable progress in different application domains, many of its models, including deep learning, require large amounts of memory and processing resources. As a result, the adoption of HPC systems in this regard could further enhance the progress realized thus far in various research areas and help connect the dots between the raw data at a much faster speed. The confluence of ML techniques with HPC solutions holds great promise towards overcoming barriers of application complexity and machine cost. In response to advancing technological landscapes and evolving user needs, both research communities are adapting and embracing the opportunities and challenges afforded therein. This realization has motivated the need for an edited collection of original research works to showcase the sustained and broad efforts invested in these two disciplines as well as how they could leverage one another in a concerted and complementary way.


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.

Target Audience

The target audience of this book would include academics, researchers, computer scientists, computer and electrical engineers as well as experts in the field of machine learning, high performance computing and data analytics. Practitioners in computational science and engineering, artificial intelligence, machine learning and data science as well as graduate and undergraduate students interested in different aspects of HPC or ML would also be of interest.

Recommended Topics

• High performance computing systems • Machine learning paradigms • Parallelization and scaling of machine learning techniques/algorithms • ML applications on HPC systems • HPC system design and optimization for ML workloads • Convergence of HPC and deep learning • Data analytics using ML on HPC systems • Networking and storage requirements and solutions of HPC systems for ML • Libraries, tools, and workflows for ML on HPC systems • Emerging and new trends in HPC and ML

Submission Procedure

Researchers and practitioners are invited to submit on or before January 30, 2023, 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 February 27, 2023 about the status of their proposals and sent chapter guidelines. Full chapters are expected to be submitted by March 20, 2023, and all interested authors must consult the guidelines for manuscript submissions at prior to submission. All submitted chapters will be reviewed 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, Handbook of Research on Integrating Machine Learning Into HPC-Based Simulations and Analytics. All manuscripts are accepted based on a double-blind peer review editorial process.

All proposals should be submitted through the eEditorial Discovery® online submission manager.


This book is scheduled to be published by IGI Global (formerly Idea Group Inc.), an international academic publisher of the "Information Science Reference" (formerly Idea Group Reference), "Medical Information Science Reference," "Business Science Reference," and "Engineering Science Reference" imprints. IGI Global specializes in publishing reference books, scholarly journals, and electronic databases featuring academic research on a variety of innovative topic areas including, but not limited to, education, social science, medicine and healthcare, business and management, information science and technology, engineering, public administration, library and information science, media and communication studies, and environmental science. For additional information regarding the publisher, please visit This publication is anticipated to be released in 2022.

Important Dates

January 30, 2023: Proposal Submission Deadline
February 27, 2023: Notification of Acceptance
March 20, 2023: Full Chapter Submission
April 10, 2023: Review Results Returned
May 1, 2023: Final Acceptance Notification
May 29, 2023: Final Chapter Submission


Belgacem Ben Youssef
King Saud University

Mohamed Maher Ben Ismail
King Saud University


Computer Science and Information Technology; Science and Engineering
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