Call for Papers

Call for Papers

The Smoky Mountains Computational Sciences and Engineering Conference (SMC2022) is a premier event for discussing the latest developments in computational sciences and engineering for high-performance computing (HPC) and integrated instruments for science. The conference has been held since 2003. This year, the 19th installment of the conference is virtual and in person at the MeadowView Marriott Resort & Convention Center, Kingsport, Tennessee, USA. The conference theme is “Accelerating science and engineering discoveries through integrated research infrastructure for experiment, big data, modeling and simulation”. This year, the program committee will accept vision papers that include authors’ perspectives on the most important directions for research, development, production, and experiences, and needs for investment. We specifically encourage authors to emphasize their positions, grounded in evidence, in the specific areas identified in the sessions below. For questions please contact

*The deadline for submission of abstracts has been extended to April 4, 2022.

Instructions on submissions can be found here (or via EasyChair)

*All submissions must be sent via the EasyChair Submission Link: 

  • Call for papers: February 15, 2022 
  • Abstract submission and paper registration due date: March 25,2022  April 4, 2022 (Closed)
  • Author notification for abstract acceptance: April 19, 2022  April 22, 2022 (extended)
  • Final Paper submission date: June 17, 2022, COB anywhere on earth (hard deadline)
  • SMC PC Review Period: June 20 – July 18, 2022
  • Author notification for paper acceptance: July 20 – 22, 2022
  • Conference ready paper submission: August 8, 2022
  • Conference presentation: August 24 – 25, 2022
  • Submission for Proceedings: September 16, 2022

*Please note that the Data-Challenge may run on a different timeline, please visit for more information.

Session 1. Foundational Methods Enabling Science in an Integrated Ecosystem

Session chairs – Jim Nutaro and Pablo Seleson 

This session will address applications that embrace data-driven and first-principle methods, focusing on converging AI methods and approaches with high-performance modeling and simulation applications. Topics will include experiences, algorithms, and numerical methods development and integration with the edge. This session also focuses on mixed-precision, data reduction methods, and scientific libraries and frameworks for converged HPC and AI. Participants will discuss how simulation can be used to train AI models and integrate them to work with simulation applications while quantifying errors.

Session 2. Science and Engineering Applications Requiring and Motivating an Integrated Ecosystem

Session chairs –Helia Zandi and Dayle Smith

Participants will discuss multi-domain applications that use federated scientific instruments with data sets and large-scale compute capabilities, including sensors, actuators, instruments for HPC systems, data stores, and other network-connected devices. Some of the AI and HPC workloads are being pushed to the edge (closer to the instruments) while large-scale simulations are scheduled on HPC systems with large capacities. This session will focus on applications that focus on integration across domains and scientific datasets that combine AI and HPC with edge computing.

Session 3. Systems and Software Advances Enabling an Integrated Science and Engineering Ecosystem

Session chairs –Jack Lange and Addi Thakur Malviya

This session includes programming systems and software technologies for novel computing processors such as neuromorphic, automata, advanced FETs, carbon nanotube processors, and other types of accelerators that meet the SWaP constraints to be deployed at the edge. To connect instruments from the edge to supercomputers, we need to efficiently collect and process data at the edge. Specialized workflows, efficient networks, data transfer toolkits, and communication libraries need to be developed to minimize the latency between edge and supercomputers and close the AI/learning and control loops. This session will present the latest ideas and findings in the programming and software ecosystems for these rapidly changing and emerging fields.

Session 4. Deploying Advanced Technologies for an Integrated Science and Engineering Ecosystem

Session chairs –Sarp Oral and Seth Hitefield

Topics include industry experience and plans for deploying both hardware and software infrastructure needed to support emerging AI and/or classical simulation workloads; for combining on-premises and cloud resources; and for connecting distributed experimental, observational, and data resources and computing facilities using edge technologies. This session will focus on how emerging technologies can be co-designed to support compute and data workflows at scale for next-generation HPC and AI systems.

Session 5. Data Challenge

Session Chairs – Hong Liu and Suzanne Parete-Koon

SMC2022 provides an opportunity to tackle scientific data challenges that come from eminent data sets at ORNL. These data sets come from scientific simulations and instruments in physical and chemical sciences, electron microscopy, bioinformatics, neutron sources, urban development, and other areas. For more information please visit:

Important Dates for Data Sponsors:

  • Challenge descriptions due date: March 7,2022
  • Datasets: April 4, 2022
  • Website goes live: April 11, 2022

Abstract and paper submission instructions:

All paper submissions will be peer-reviewed by the SMC2022 program committee for the purpose of producing a high-quality proceeding.

Accepted papers will be invited to give an in-person talk at the conference.

Please note your original submission track may change if reviewers feel your submission(s) fit best in another session/track.

We will be accepting full papers of 12-18 pages long. Papers need to be formatted according to Springer’s single-column style.

If your abstract was accepted, please use the paper templates available for LaTeX and Word (

The copyright will need to be transferred. A copyright form will be provided, which allows users to self-archive.