Search for: All records

Abstract Artificial intelligence (AI) has the potential for vast societal and economic gain; yet applications are developed in a largely ad hoc manner, lacking coherent, standardized, modular, and reusable infrastructures. The NSF‐funded Intelligent CyberInfrastructure with Computational Learning in the Environment AI Institute (“ICICLE”) aims to fundamentally advanceedge‐to‐center, AI‐as‐a‐Service, achieved through intelligent cyberinfrastructure (CI) that spans the edge‐cloud‐HPCcomputing continuum,plug‐and‐playnext‐generation AI and intelligent CI services, and a commitment to design for broad accessibility and widespread benefit. This design is foundational to the institute's commitment to democratizing AI. The institute's CI activities are informed by three high‐impact domains:animal ecology,digital agriculture, andsmart foodsheds. The institute's workforce development and broadening participation in computing efforts reinforce the institute's commitment todemocratizing AI. ICICLE seeks to serve asthe national nexus for AI and intelligent CI, and welcomes engagement across its wide set of programs. 

Finch, a domain specific language and code generation framework for partial differential equations (PDEs), is demonstrated here to solve two classical problems: steady-state advection diffusion equation (single PDE) and the phonon Boltzmann transport equation (coupled PDEs). Both finite volume and finite element methods are explored. In addition to work presented at the 2022 International Conference on Computational Science (Heisler et al., 2022), we include recent developments for solving nonlinear equations using both automatic and symbolic differentiation, and demonstrate the capability for the Bratu (nonlinear Poisson) equation. 

The phonon Boltzmann transport equation is a good model for heat transfer in nanometer scale structures such as semiconductor devices. Computational complexity is one of the main challenges in numerically solving this set of potentially thousands of nonlinearly coupled equations. Writing efficient code will involve careful optimization and choosing an effective parallelization strategy, requiring expertise in high performance computing, mathematical methods, and thermal physics. To address this challenge, we present the domain specific language and code generation software Finch. This language allows a domain scientist to enter the equations in a simple format, provide only basic mathematical functions used in the model, and generate efficient parallel code. Even very complex systems of equations such as phonon Boltzmann transport can be entered in a very simple, intuitive way. A feature of the framework is flexibility in numerical methods, computing environments, parallel strategies, and other aspects of the generated code. We demonstrate Finch on this problem using a variety of parallel strategies and model configurations to demonstrate the flexibility and ease of use.