Smokey Mountain Conference

Computational Sciences and Engineering Conference

WHEN Aug 25-27, 2020
WHERE Kingsport, TN MeadowView Resort

Smoky Mountains Computational Sciences & Engineering Conference

Driving future science & engineering discoveries through the integration of experiment, big data, and modeling and simulation

MeadowView Conference Resort & Convention Center

1901 Meadowview Parkway
Kingsport, TN 37660

Call for Papers

Important Dates:

  • Abstract submission and paper registration due date: March 27, 2020
  • Author notification for abstract acceptance: April 27, 2020
  • Paper submission for review: May 29, 2020
  • Author notification for paper acceptance: June 14, 2020
  • Conference ready paper submission: July 17, 2020
  • Conference paper presentation: August 25-27, 2020
  • Camera ready paper submission: September 15, 2020


The Smoky Mountains Computational Sciences and Engineering Conference (SMC2020) 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 18th installment of the conference will be held in Kingsport, TN. The conference focuses on four major areas—theory, experiment, modeling and simulation, and data—that focus on accelerated node computing and integrated instruments for science. This year, the program committee will accept vision papers that include the author’s perspective on the most important directions for research, development, production and experiences, and needs for investment in the specific areas identified in the following five sessions:


Session 1. Computational Applications: Converged HPC and Artificial Intelligence (AI)

Session chairs – Bronson Messer and Steven Hamilton, ORNL

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 that will play an important role in this area. 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. System Software: Data Infrastructure and Life Cycle

Session chairs – Sudharshan Vazhkudai and Amy Rose, ORNL

In this session, participants will consider the scientific data life cycle from collection to archive, including all the aspects in between and the infrastructure needed to support it. The group will cover techniques and system designs needed to securely publish, curate, stage, store, reduce, and compress data. Also relevant are techniques to annotate the data with metadata and automatically extract information from datasets that will aid with the scalable search and discovery of mountains of data.


Session 3. Experimental/Observational Applications: Use Cases That Drive Requirements for AI and HPC Convergence

Session chairs – Kate Evans and Vincent Paquit, ORNL

Participants will discuss ways to use multiple 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 use cases that require multiple scientific instruments, emphasizing use cases that combine AI and HPC with edge computing.


Session 4. Deploying Computation: On the Road to a Converged Ecosystem

Session chairs – Gina Tourassi and Arjun Shankar, ORNL

Topics will include industry experience and plans for deploying the hardware and software infrastructure needed to support applications used for AI methodologies and simulation to deploy next-generation HPC and data science systems. This session will focus on how emerging technologies can be co-designed to support compute and data workflows at scale.


Session 5. Scientific Data Challenges: Data Sponsors

Session chair – Suzanne Parete-Koon, ORNL

SMC2020 provides scientists with an opportunity to become scientific data sponsors and describe challenges for eminent data sets at ORNL. These data sets will be used for the SMC Data Challenge (SMCDC2020) competition ( These data sets come from scientific simulations and instruments in physical and chemical sciences, electron microscopy, bioinformatics, neutron sources, urban development, and other areas. The goal of this session is to provide and describe a significant data set, then formulate three to five challenge questions associated with the data set in a paper. The challenge questions for each data set will cover multiple difficulty levels. The first question in each challenge should be suitable for a novice, with each subsequent question increasing in difficulty and the series of questions ending with an advanced/expert level challenge question. These challenges are intended to draw scientists and researchers at the beginning stages of incorporating data analytics into their workflow, as well as data analytics experts interested in applying novel data analytics techniques to data sets of national importance.


Contact for More Information About the Sessions


Jeff Nichols
General Chair:
Jeff Nichols
Becky Verastegui
Conference Organizer:
Becky Verastegui


Committee Members

Steering Committee:

  • Jeff Nichols, ORNL
  • Gina Tourassi, ORNL
  • Barney Maccabe, ORNL
  • Kate Evans, ORNL
  • Becky Verastegui, ORNL
  • David Womble, ORNL
  • Suzanne Parete-Koon, ORNL
  • Jim Hack, ORNL
  • Oscar Hernandez, ORNL
  • Matt Baker, ORNL

Media & Communications:

  • Scott Jones, ORNL
  • Elizabeth Rosenthal, ORNL

Program Committee:

  • Barney Maccabe, ORNL (Program Committee Chair)
  • Gina Tourassi, ORNL
  • Sadaf Alam, Swiss National Supercomputing Centre
  • Pete Beckman, Argonne National Laboratory
  • Greg Peterson, University of Tennessee
  • Victor Hazlewood, University of Tennessee
  • Michela Taufer, University of Tennessee
  • Rio Yokota, Tokyo Institute of Technology
  • Vivek Sarkar, Georgia Institute of Technology
  • Jonathan Beard, ARM Research
  • Paul Cook, Cray
  • Robert Harrison, Stonybrook University
  • Michael Schulte, AMD
  • Gary Grider, Los Alamos National Laboratory
  • Susan Coghlan, Argonne National Laboratory
  • Oscar Hernandez, ORNL
  • Matt Baker, ORNL
  • Bronson Messer, ORNL
  • Steven Hamilton, ORNL
  • Sudharshan Vazhkudai, ORNL
  • Amy Rose, ORNL
  • Marie Urban, ORNL
  • Arjun Shankar, ORNL
  • Suzanne Parete-Koon, ORNL
  • Ketan Maheshwari, ORNL
  • Pravallika Devineni, ORNL
  • Olivera Kotevska, ORNL
  • Dasha Herrmannova, ORNL


Abstract and paper submission instructions:

All contributions are planned to be published in SMC2020 proceedings in a CCIS Springer volume and will be peer-reviewed by the program committee. All authors must first submit a 250-word abstract to register their papers. Once the abstract is accepted, we will encourage the authors to submit full or short papers. We will accept full papers of 12 pages and short papers of 6-8 pages. Papers need to be formatted according to Springer's single column style. Please use the paper templates available for LaTeX and Word ( The copyright will need to be transferred to Springer. A copyright form will be provided, which allows users to self-archive.

Upload Abstracts and Papers

Special instructions for data sponsors (session 5):

Data sponsors participating in the SMCDC2020 competition are invited to submit papers describing their challenge data sets and challenge questions. The opening sections should include a full description of the data that explains why this data set is significant in their scientific field and what the broader implications of learning from this data set may be. Include instructions for reading the data and a description of the data format as well. The latter sections of the paper should include three to five challenge questions listed in order of increasing difficulty. The first question should encourage scientists or students who are non-experts in novel data analytics techniques to attempt the challenge, and there should be at least one advanced, expert level question. Give a detailed description of expected answers to the challenge questions; e.g. tools used and algorithms developed or implemented.

* Data sponsor papers are invited papers and don’t need to submit an abstract

More Information About SMCDC2020...