[hpc-announce] Smoky Mountains Computational Sciences and Engineering Conference (SMC2021)

Ahearn, Theresa ahearntm at ornl.gov
Mon Feb 15 07:06:46 CST 2021

Dear HPC Announce,
We would like to request that our call for papers be posted for The Smoky Mountains Computational Sciences and Engineering Conference (SMC2021).
I have copied the CFP notice below.
Thank you for your time and consideration. 

Call for Papers for SMC2021

*** 250-word abstract submission due March 2, 2021 ***

The Smoky Mountains Computational Sciences and Engineering Conference (SMC2021) 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 “Driving future science and engineering discoveries through the integration of experiment, big data, and modeling and simulation—that focus on accelerated node high-performance computing and integrated instruments for science (edge computing). 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.

Important Dates:

Abstract submission and paper registration due date: March 2, 2021 Author notification for abstract acceptance: April 2, 2021 Paper submission for review: June 1, 2021 Author notification for paper acceptance: July 16, 2021 Conference ready paper submission: August 3, 2021 Conference presentation: August 24-26th, 2021 Camera-ready paper submission: September 14th, 2021

Session 1. Advanced Computing Methods: Instruments from Edge to Supercomputers  

Session chairs –  Olga Ovchinnikova, 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 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. Advanced Computing Applications: Use Cases that Combine Multiple Aspects of Data 
and Modeling

Session chairs – Teja Kuruganti and Olga Kuchar, ORNL

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. Advanced Computing Systems and Software: Connecting Instruments from Edge to 

Session chairs – Arjun Shankar and Neena Imam, ORNL 

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 Computing Platforms: On the Road to a Converged Ecosystem 

Session chairs – Scott Atchley and David Bernholdt, ORNL 

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 

For more information about the conference, sessions and program committee members visit
https://smc2021.ornl.gov or contact smc21 at easychair.org

Abstract and paper submission instructions:

All contributions are planned to be published in SMC2021 proceedings with Springer 
(pending approval) and will be peer-reviewed by the program committee. Authors should clearly 
identify which of the four sessions described above their paper is targeting.  Papers that do not fit 
into a session (either by topic or due to the number of papers accepted for a session) will be 
considered for short presentations in a poster session.  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 papers. We will accept full papers of 12-18 pages. Papers need to be formatted according 
to  Springer’s single-column style. Please use the paper templates available for LaTeX and Word 

Abstracts and papers need to be uploaded here

Learn More About:

SMC2021 Scientific Data Challenges

Data Challenge chair – Pravallika Devineni, ORNL

SMC2021 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. These data sets will be used for the SMC Data Challenge (SMCDC2021) competition. 
For more information please visit:  https://smc-datachallenge.ornl.gov

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