Scientific computing applications generate enormous datasets that are continuously increasing exponentially in both complexity and volume, making their analysis, archival, and sharing one of the grand challenges of modern big data analytics. Supported by the rise of artificial intelligence and deep learning, such enormous datasets are becoming valuable resources even beyond their original scope, opening new opportunities to learn patterns and extract new knowledge at large scale, potentially without human intervention. However, this leads to an increasing complexity of the workflows that combine traditional HPC simulations with big data analytics and AI workloads. An initial wave that opened this direction was the shift from compute-intensive to data-intensive, which saw several ideas from big data analytics (in-situ processing, shipping computations close to data, complex and dynamic workflows) fused with the tightly coupled patterns needed by the HPC ecosystem. AI workloads have brought additional challenges to tightly coupled patterns: irregular I/O (small, scattered reads/writes), relaxed collectives (e.g. tolerant to stragglers), accelerator-centric optimizations, parallelization strategies, agentic workflows, etc. In a quest to keep up with these new challenges, the design and operation of the infrastructures capable of running them efficiently at scale has evolved accordingly. Extreme heterogeneity at all levels (combinations of CPUs and accelerators, various types of memories and local storage and network links, parallel file systems and object stores, etc.) is now the norm. Ideas pioneered by cloud and edge computing (aspects related to elasticity, multi-tenancy, geo-distributed processing, stream computing) are also beginning to be adopted in the HPC ecosystem (containerized workflows, on-demand jobs to complement batch jobs, streaming of experimental data from instruments directly to supercomputers, etc.). Thus, modern scientific applications need to be integrated into an entire AI-Enabled Compute Continuum from the edge all the way to supercomputers and large data-centers using flexible infrastructures and middlewares.

The 16th workshop on AI and Scientific Computing at Scale using Flexible Computing Infrastructures (FlexScience) will provide the scientific community a dedicated forum for discussing new research, development, and deployment efforts in running scientific computing workloads in such flexible ecosystems, across the Computing Continuum, focusing on emerging technologies and new convergence challenges that are not sufficiently addressed by the current generation of supercomputers and dedicated data centers. The workshop aims to address questions such as: what architectural changes to existing frameworks (hardware, operating systems, networking and/or programming models) are needed to support flexible computing? Dynamic information derived from remote instruments, coupled simulations, and sensor ensembles that stream data for real-time analysis and machine learning are important emerging trends. How can we leverage and adapt to these patterns? What scientific workloads are suitable candidates to take advantage of heterogeneity, elasticity and/or on-demand resources? What factors are limiting the adoption of a flexible design? The workshop encourages interaction and cross-pollination between participants that are developing applications, algorithms, middleware and infrastructure and that are facing new challenges and opportunities to take advantage of flexible computing. The workshop will be an excellent place to help the community define the current state, determine future goals, and discuss promising technologies and techniques