To enhance the performance of the thermochemistry solver in nekCRF, particularly for more complex chemistry models, several strategies can be employed. First, optimizing the Jacobian-vector product evaluation is crucial, as it significantly impacts the efficiency of the Newton iterations. Implementing advanced techniques such as automatic differentiation or using analytical Jacobians can reduce computational overhead. Additionally, leveraging mixed-precision techniques can accelerate computations by allowing less critical calculations to be performed in lower precision, thus improving overall throughput without sacrificing accuracy.
Another approach is to enhance the parallelization of the solver. This can be achieved by optimizing the communication patterns among processing units, particularly during global synchronization steps, which are often bottlenecks in performance. Techniques such as overlapping communication with computation can mitigate these delays. Furthermore, employing more sophisticated preconditioning strategies tailored to the specific characteristics of the chemistry model can improve the convergence rates of the linear solvers.
Lastly, integrating adaptive mesh refinement (AMR) can provide significant performance benefits by concentrating computational resources on regions of interest, such as reaction zones, while reducing the resolution in less critical areas. This targeted approach can lead to more efficient use of computational resources, especially in simulations involving complex chemical kinetics.