The Smoky Mountains Computational Sciences and Engineering Conference (SMC2023) 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 20th installment of the conference is virtual and in person at the Crowne Plaza Hotel, Knoxville, Tennessee, USA. The conference theme is “Peering into the future: Accelerating science and engineering discoveries through advanced technologies, integrated research infrastructures for experiments, machine learning/AI, and modeling and simulation”. This year, the program committee will invite 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 shankarm@ornl.gov.


Session 1. Science and Engineering Applications Requiring and Motivating an Integrated Ecosystem

Session chairs – Prasanna Balaprakash and Helia Zandi

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 2. Foundational Methods Enabling Science in an Integrated Ecosystem

Session chairs – Dan Lu and Sudip Seal

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 3. Deploying Advanced Technologies for an Integrated Science and Engineering Ecosystem in the Exascale Era

Session chairs – Matt Sieger and Veronica Melesse Vergara

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 4. Systems and Software Advances Enabling an Integrated Science and Engineering Ecosystem

Session chairs – Olga Kuchar and Ben Mintz

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 5. Data Challenge

Session Chairs – Suzanne Parete-Koon and Hong Liu

SMC2023 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: https://smc-datachallenge.ornl.gov