Call for Papers - SMC2020
SMC2020: Smoky Mountains Computational Sciences and
Engineering Conference
Kingsport, Tennessee, USA.
Date: August 25-27, 2020
Website: http://smc.ornl.gov
General chair: Jeff Nichols, Oak Ridge National Laboratory (ORNL)
Conference organizer: Becky Verastegui, ORNL
Communications: Scott Jones and Elizabeth Rosenthal, ORNL

Important dates:
Abstract submission and paper registration due date: April 3, 2020
Author notification for abstract acceptance: April 17, 2020
Paper submission for review: June 8, 2020
Author notification for paper acceptance: June 22, 2020
Conference ready paper submission: July 24, 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, Tennessee. 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 e xperiences, 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. Priority areas of interest include, but are not
limited to:

* Use cases that require multiple scientific instruments,
* Use cases that combine AI and HPC with edge computing,
* Examples that show how to interface with the users (of data, output..)
* Examples that demonstrate a diversity of connectivity across
instruments, HPC, and data store
* Novel methods within a larger use case that show improvements one
facet, say edge compute, storage, transfer etc. that enhance current
practice
* An overview of the state of convergence of data, compute, and
instruments in an application area
* Provocative ideas on how to revolutionize the convergence of
instruments, data, and compute at the edge and HPC

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 (https://smc-datachallenge.ornl.gov). 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.
For more information about the sessions, contact smc2020@easychair.org.

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
Matthew Baker, ORNL
Program Committee:
Barney Maccabe, ORNL (Program Committee Chair)
Sadaf Alam, Swiss National Supercomputing Centre
Vassil Alexandrov, Hartree
Jim Ang, Pacific Northwest National Laboratory
Manuel Arenaz, Universidade da Coruña /Appentra
Scott Atchley, ORNL
Matt Baker, ORNL
Jonathan Beard, ARM
Anne Berres, ORNL
Patrick Bridges, University of New Mexico
David Brown, Lawrence Berkeley National Laboratory
Barbara Chapman, Stonybrook University
Norbert Eicker, Jülich Supercomputing Centre
Kate Evans, ORNL
Marta Garcia, Barcelona Supercomputing Center
Aric Hagberg, Los Alamos National Laboratory
Stephen Hamilton, ORNL
Victor Hazlewood, University of Tennessee
Oscar Hernandez, ORNL (Program Committee Co-chair)
Andreas Herten, Jülich Supercomputing Centre
Jeff Hittinger, Lawrence Livermore National Laboratory
Shantenu Jha, Brookhaven National Laboratory
Travis Johnston, ORNL
Guido Juckeland, Helmholtz-Zentrum Dresden Rossendorf
Olivera Kotevska, ORNL
Kody Law, University of Manchester
Piotr Luszczek, University of Tennessee
John Levesque, HPE
Barney Maccabe, ORNL
Esteban Meneses, Costa Rica Institute of Technology,
Bronson Messer, ORNL
Mathias Mueller, RWTH Aachen University
Bernd Mohr, Jülich Supercomputing Centre
CJ Newburn, NVIDIA
Vincent Paquit, ORNL
Suzanne Parete-Koon, ORNL
Greg Peterson, University of Tennessee
Dirk Pleiter, Jülich Supercomputing Centre
Laura Pullum, ORNL
Roxana Rositoru, ARM, UK
Amy Rose, ORNL
Jibo Sanyal, ORNL
Mitsuhisa Sato, RIKEN
Thomas Schulthess, ETH Zurich / CSCS
Jim Sexton, IBM
Stuart Slattery, ORNL
Jim Stewart, Sandia
Arjun Shankar, ORNL
Tjerk Straatsma, ORNL
Valerie Taylor, Argonne National Laboratory
Christian Terboven, RWTH Aachen University
Stan Tomov, University of Tennessee
Gina Tourassi, ORNL
Sudharshan Vazhkudai, ORNL
Rio Yokota, Tokyo Institute of Technology

Abstract and paper submission instructions:

All contributions are planned to be published with Springer in their
Communications in Computer and Information Science series (CCIS) (final
approval pending). Submissions 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 6-11 pages, with preference for full papers.
Papers need to be formatted according to Springer's single column
style. Please use the paper templates available for LaTeX and Word
(https://www.springer.com/gp/authors-editors/conference-proceedings/conference-proceedings-guidelines).
The copyright will need to be transferred to Springer. A copyright form
will be provided, which allows users to self-archive.
Abstracts and papers need to be uploaded here:
https://easychair.org/conferences/?conf=smc2020.

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 do not need to submit an abstract.
For more information about SMCDC2020, visit https://smc-datachallenge.ornl.gov.