Minisymposium

In recent years, high-performance computing (HPC) systems have experienced exponential growth in performance that has outpaced the improvements in energy efficiency. While this progress has significantly advanced many scientific fields, like drug discovery, fusion reactor modeling, and climate research, it has also led to substantial energy consumption, contributing to significant carbon dioxide emissions. Addressing this environmental impact is essential to align HPC development with Sustainable Development Goals (SDGs), particularly those focusing on responsible consumption . This study explores energy consumption in particle simulations, with examples from applications in Molecular Dynamics (MD). The analysis highlights challenges in optimizing energy efficiency across hardware and for many different simulation scenarios. A solution to tackle these challenges is presented in the form of AutoPas, a particle simulation library that dynamically selects the optimum algorithm to minimize runtime or energy use. Some strategies to improve energy efficiency include reducing memory-intensive algorithms, leveraging low-level parallelism, etc. are discussed, and examples from MD simulations are presented. This talk advocates for the importance of sustainable approaches in HPC development, that is to mitigate environmental impacts of computing while still ensuring the much needed progress in science and in turn, to improving human lives.

This presentation will explore the transformative role of high-performance computing (HPC) in advancing the United Nations’ 17 Sustainable Development Goals (SDGs). HPC provides a critical capability for solving global challenges, such as health disparities, and energy access. By enabling large-scale data processing and simulations, HPC helps to unearth emerging trends and accelerate innovations in renewable energy, healthcare, education, and agriculture—key areas in the SDGs. The talk will cover practical examples of HPC applications, including precision medicine (SDG 3), and renewable energy optimization and smart grid (SDG 7). Furthermore, the discussion will highlight the need for equitable access to HPC resources in low-resource settings, fostering global collaboration in solving the world’s most pressing challenges. The session aims to inspire the research community to leverage HPC for achieving the SDGs, providing actionable insights for the integration of advanced computational tools in sustainable development efforts.

The increasing computational demand for AI has resulted in a significant increase in the amount of energy consumed by data centers. The International Energy Agency has projected that data center energy consumption could double from 2022 to 2026 and has become a major cost factor in the procurement and operation of data centers and large scale HPC data centers. While AI technologies have put significant pressure on the sustainability of IT systems and data centers, they also have tremendous promise for improving the sustainability of IT and other ecosystems. This presentation will cover research spanning efforts to improve sustainability using digital twins and AI. We’ll discuss the integration of AI in digital twins to optimize data center and system operations, to model climate change, fusion reactor design and to accelerate scientific discovery.

Increasingly complex GeoAI models produce visual results that suggest more clarity and certainty than they often contain. Seductive graphics suggest clarity and continuity and gloss over ambiguities in favor of coherent depictions of consumable and readable graphic representations. This presentation will discuss how the challenge of visually representing uncertainty was addressed in the mapping of agroforestry in the highlands of Bali, Indonesia and the tradeoffs required to address uncertainty. Based on those practical observations, I will discuss how uncertainty inherent in complex Earth models are compromised in seductive game-like visualizations, and how presentations of presumed clarity may impact public perception of climate modelling and the ability to counter climate change.