Session

DescriptionAdvances in genomics, proteomics, and molecular modeling have made computational biology one of the most data- and compute-intensive fields of modern science. New discoveries in life science increasingly rely on a combination of AI-driven analysis, large-scale numerical simulation, and heterogeneous high-performance computing (HPC) systems to transform massive datasets into biological insight. At the same time, the HPC ecosystem is undergoing a fundamental shift: hardware accelerators are increasingly optimized for low-precision arithmetic to satisfy dominant AI workloads, while many traditional life science applications, such as molecular dynamics, biomolecular simulation, and population genetics, continue to require high numerical precision, stability, and rigorous validation. Reconciling this growing dichotomy is one of the most urgent challenges facing HPC today. This minisymposium explores how algorithm-software-hardware co‑design can build reliability and transparency directly into accelerated biological computing. The session brings together four prominent speakers from academia, national labs, and leading HPC centers, representing diverse perspectives and covering topics ranging from large-scale pangenomics to molecular dynamics and multi-omics. Together, these talks illustrate how co-design approaches are being applied in practice to reconcile AI acceleration with high-precision scientific computing, and how these insights are shaping the future of high-performance computing beyond the life sciences.