Call for Abstracts
ModSim 2021
Workshop on Modeling & Simulation of Systems and Applications
August 11-13, 2021, University of Washington Botanic Gardens
Center for Urban Horticulture, Seattle
Workshop URL: https://www.bnl.gov/modsim2021/
Submission URL: https://easychair.org/conferences/?conf=modsim2021
EasyChair Submission Deadline: Sunday, May 02, 2021 (11:59 PM, Pacific Daylight Time [PDT])
Notification of Acceptance: Friday, June 04, 2021 (via e-mail)
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 2021 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=modsim2021) no later than Sunday May 02, 2021 (11:59 PM, PDT). Those with accepted abstracts will be notified via e-mail on Friday, June 04, 2021
Dr. Sudhakar Yalamanchili Award
Submissions will be eligible for the Dr. Sudhakar Yalamanchili Award, which is intended to recognize young researchers for their outstanding contribution to the field of performance modeling and simulation. Presenters, who must be a graduate student or postdoctoral researcher (within six years of highest awarded degree), will be evaluated during the Contributed Presentation/Poster Session at the ModSim 2021 Workshop. Learn more at https://www.bnl.gov/modsim2021/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 research and development, new projects or advances in existing efforts, and new applications of ModSim tools to real-life problems.