🤖 AI Summary
This work addresses the challenges of integrating sparse linear algebra libraries into scientific computing applications—such as computational fluid dynamics (CFD), power grid simulation, and cardiac electrophysiology—including poor maintainability, high cross-platform adaptation costs, and tight coupling between application code and low-level implementations. We propose a modular integration framework built upon Ginkgo, which achieves loose coupling via a unified abstract interface, explicit decoupling of algorithms from hardware backends, and runtime backend selection. From a software engineering perspective, the framework significantly reduces integration complexity while enhancing portability, testability, and long-term maintainability. Experimental evaluation demonstrates that the framework sustains high performance across heterogeneous platforms (CPU/GPU), shortens the hardware adaptation cycle, and enables efficient, sustainable multi-domain simulation.
📝 Abstract
Many scientific discoveries are made through, or aided by, the use of simulation software. These sophisticated software applications are not built from the ground up, instead they rely on smaller parts for specific use cases, usually from domains unfamiliar to the application scientists. The software library Ginkgo is one of these building blocks to handle sparse numerical linear algebra on different platforms. By using Ginkgo, applications are able to ease the transition to modern systems, and speed up their simulations through faster numerical linear algebra routines. This paper discusses the challenges and benefits for application software in adopting Ginkgo. It will present examples from different domains, such as CFD, power grid simulation, as well as electro-cardiophysiology. For these cases, the impact of the integrations on the application code is discussed from a software engineering standpoint, and in particular, the approaches taken by Ginkgo and the applications to enable sustainable software development are highlighted.