General Lab Information

Call for Abstracts

The instructions below will guide you through the submission process.

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 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?

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

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.

Submission Information

Need Assistance?

Please contact the workshop coordinator if you have questions or would like further information.

Patricia Foy


(631) 344-5037
(631) 344-5751
pfoy@bnl.gov