Influenced by the advances in data and computing, the scientific practice increasingly involves machine learning and artificial intelligence driven methods which requires specialized capabilities at the system-, science- and service-level in addition to the conventional large-capacity supercomputing approaches. The latest distributed architectures built around the composability of data-centric applications led to the emergence of a new ecosystem for container coordination and integration. However, there is still a divide between the application development pipelines of existing supercomputing environments, and these new dynamic environments that disaggregate fluid resource pools through accessible, portable and re-programmable interfaces. New approaches for dynamic composability of heterogeneous systems are needed to further advance the data-driven scientific practice for the purpose of more efficient computing and usable tools for specific scientific domains. In this paper, we present a novel approach for using composable systems in the intersection between scientific computing, artificial intelligence (AI), and remote sensing domain. We describe the architecture of a first working example of a composable infrastructure that federates Expanse, an NSF-funded supercomputer, with Nautilus, a Kubernetes-based GPU geo-distributed cluster. We also summarize a case study in wildfire modeling, that demonstrates the application of this new infrastructure in scientific workflows: a composed system that bridges the insights from edge sensing, AI and computing capabilities with a physics-driven simulation.

In the coming decade, the visible universe will be surveyed to unprecedented breadth, depth, and spectral coverage to explore its structures, past history, and future fate. Data volumes of many petabytes are expected. Numerical simulations of cosmic structure formation and evolution must keep pace in scale and complexity if they are to assist in the interpretation of such data, as well as to make predictions. We describe the Cosmic Simulator, a software facility under development at UCSD for performing, analyzing, and archiving cosmological simulations of unprecedented scale and physical realism. Its major components are the adaptive mesh refinement hydrodynamic cosmology simulation code ENZO, a data analysis pipeline, and a Storage Resource Broker (SRB)-managed data archive housed at SDSC. We show scientific results from the Cosmic Simulator implemented on a data grid connecting LLNL and SDSC computational resources, and discuss future plans.

As an entry for the 2001 Gordon Bell Award in the special category, we describe our 3-d, hybrid, adaptive mesh refinement (AMR) code Enzo designed for high-resolution, multiphysics, cosmological structure formation simulations. Our parallel implementation places no limit on the depth or complexity of the adaptive grid hierarchy, allowing us to achieve unprecedented spatial and temporal dynamic range. We report on a simulation of primordial star formation which develops over 8000 subgrids at 34 levels of refinement to achieve a local refinement of a factor of 1012 in space and time. This allows us to resolve the properties of the first stars which form in the universe assuming standard physics and a standard cosmological model. Achieving extreme resolution requires the use of 128-bit extended precision arithmetic (EPA) to accurately specify the subgrid positions. We describe our EPA AMR implementation on the IBM SP2 Blue Horizon system at the San Diego Supercomputer Center.