[hpc-announce] Extended Deadline: May 17. Workshop on Modeling and Simulation of Systems and Applications ModSim 2020

[Hoisie, Adolfy]

Call for Abstracts

ModSim 2020
Workshop on Modeling & Simulation of Systems and Applications
August 12-14, 2020, University of Washington Botanic Gardens
Center for Urban Horticulture, Seattle

Workshop URL: https://www.bnl.gov/modsim2020/
Submission URL: https://easychair.org/conferences/?conf=modsim20200
EasyChair Submission Deadline: Sunday, May 17, 2020 (11:59 PM, Anywhere on Earth [AOE])
Notification of Acceptance: Monday, June 01, 2020

To promote advancements in modeling and simulation (ModSim) research, we are soliciting community input in the form of abstracts. If accepted, author(s) will be invited to offer a poster and short presentation at the annual gathering of our community, the ModSim 2020 Workshop.

The overarching theme for this year's workshop is "Modeling and Simulation in the Artificial Intelligence Era." The emphasis will be on AI-driven methodologies, tools, best practices, projects, and initiatives that aim to address the challenges and achieve the goal of modeling performance, power, and reliability of high-performance systems under a realistic application workload.

Abstract Submission Guidelines
There is no set word limit for abstract submissions. However, please limit your submission to one page. The abstract should provide an overview that adequately summarizes the topic(s) presented and any proposed impact on ModSim research or techniques, especially related to modeling and simulation in the era of artificial intelligence. The following details a proposed abstract layout and points to consider:

Abstract Title
Primary research area:
--Artificial Intelligence and Machine Learning Workloads and Systems
--Modeling and Simulation of Subsystems via Artificial Intelligence and Machine Learning
--Advances in ModSim Implementation
What is being modeled? (e.g., performance, reliability, power, other)
What is the target application?
What modeling techniques are being used?
What is novel about the approach versus current state of the art?
Are preliminary results available?

All abstracts must be submitted through EasyChair [https://easychair.org/conferences/?conf=modsim20200] no later than Sunday, May 17, 2020 (11:59 PM, AOE). Those with accepted abstracts will be notified via e-mail on Monday, June 01, 2020.

Dr. Sudhakar Yalamanchili Award
This year, submissions may be eligible for the inaugural Dr. Sudhakar Yalamanchili Award, which is intended to recognize researchers for their outstanding contribution to the field of computer modeling and simulation. Presenters will be evaluated during the Contributed Presentation/Poster Session at the ModSim 2020 Workshop. Learn more at the Sudha Award link: https://www.bnl.gov/modsim2020/sudhaAward.php.

Topic Areas

Abstract contributions should focus on the following topical areas of interest:

Artificial Intelligence and Machine Learning Workloads and Systems. AI, in general, and Machine Learning (ML), in particular, are important drivers to all forms of computing, including large-scale data- and numerically-intensive high-performance computing (HPC). Consequently, systems designed for AI/ML workloads are critically important. Abstracts in this category should offer novel approaches for AI and ML workloads, ModSim for AI/ML architectures, and other approaches (e.g., intelligent computational steering driven by dynamic and offline learning).
Methodologies and Tools. AI and ML are not only revolutionizing applications, but these techniques also have the potential to revolutionize the way that HPC systems are designed. This abstract category solicits submissions that adopt AI/ML techniques in system design, such as predictive models of performance, power, or cost; approaches that intelligently explore and recommend designs; and techniques that optimize individual subsystems, across system layers, or the whole system with AI/ML. Abstracts should highlight how to advance the state of the art, as well as expectations for impacting future directions in this area.

Recent Advances in ModSim Implementation. The rapidly increasing complexity of systems and application workloads—along with the blending of compute, memory devices, storage, and interconnect then further combined with application software—translates into unprecedented challenges within the ModSim field. Submissions in this category, not necessarily related to AI/ML, are expected to highlight recent developments that can help overcome these significant challenges. Possible topics include, but are not limited to, novel ModSim methodologies, emerging areas of R&D, new projects or advances in existing projects, and new applications of ModSim tools to real-life problems.