Explicit Integration with GPU Acceleration for Large Kinetic Networks

TL;DR

This paper presents a GPU-accelerated explicit integration method for large, stiff kinetic networks, enabling significant speedups over traditional CPU-based implicit methods, thus broadening the scope of feasible scientific simulations.

Contribution

It introduces the first GPU implementation of fast explicit kinetic integration algorithms for large networks, demonstrating substantial computational efficiency improvements.

Findings

Achieved order-of-magnitude faster solutions for large kinetic networks on GPU

Enabled parallel solution of multiple realistic networks simultaneously

Demonstrated feasibility of complex multiphysics simulations previously intractable

Abstract

We demonstrate the first implementation of recently-developed fast explicit kinetic integration algorithms on modern graphics processing unit (GPU) accelerators. Taking as a generic test case a Type Ia supernova explosion with an extremely stiff thermonuclear network having 150 isotopic species and 1604 reactions coupled to hydrodynamics using operator splitting, we demonstrate the capability to solve of order 100 realistic kinetic networks in parallel in the same time that standard implicit methods can solve a single such network on a CPU. This orders-of-magnitude decrease in compute time for solving systems of realistic kinetic networks implies that important coupled, multiphysics problems in various scientific and technical fields that were intractible, or could be simulated only with highly schematic kinetic networks, are now computationally feasible.