Sustainability is a core overarching principle of STRUCTURES, shaping both our research projects and our daily operations. Recognizing the high energy demands of large-scale computational and machine learning (ML) projects, we are dedicated to addressing these challenges with forward-thinking approaches that contribute to a more sustainable future in science and beyond. More than that, our approach to sustainability spans all facets of our work – with concrete actions outlined in the following areas, to name just four examples:
Fundamental research within our cluster emphasizes the development of efficient algorithms, which are key to reducing energy use in large computations. This foundational work, which is achieved in STRUCTURES at the interface of physics, mathematics, and computer science, significantly contributes to sustainable scientific computing.
STRUCTURES takes a comprehensive approach to advancing the performance and efficiency of computational tasks through global optimization across numerical methods, algorithm design, software implementation, and hardware acceleration. The potential is vast, as typical scientific applications use only a fraction of the peak performance of today’s computers. This approach ensures that available resources are utilized with maximum efficiency while minimizing waste.
Photonic computing, the use of light instead of electrons for data processing, is one of our research foci, also in combination with neuromorphic hardware architectures inspired by the energy efficiency of biological systems. We develop new computational strategies and design corresponding integrated photonic circuits. STRUCTURES research on these systems has already provided proofs-of-concept for future resource-efficient high-performance computing and data processing.
In addition to its research, STRUCTURES incorporates sustainable practices into its daily operations. For example, the cluster encourages train travel over air travel whenever possible and offers hybrid options for conferences. Furthermore, we prioritize the efficient use of computing facilities, focusing on optimizing the use of available computational resources to minimize energy waste.