Extending DD-$\alpha$AMG on heterogeneous machines
<|reference_start|>Extending DD-$\alpha$AMG on heterogeneous machines: Multigrid solvers are the standard in modern scientific computing simulations. Domain Decomposition Aggregation-Based Algebraic Multigrid, also known as the DD-$\alpha$AMG solver, is a successful realization of an algebraic multigrid solver for lattice quantum chromodynamics. Its CPU implementation has made it possible to construct, for some particular discretizations, simulations otherwise computationally unfeasible, and furthermore it has motivated the development and improvement of other algebraic multigrid solvers in the area. From an existing version of DD-$\alpha$AMG already partially ported via CUDA to run some finest-level operations of the multigrid solver on Nvidia GPUs, we translate the CUDA code here by using HIP to run on the ORISE supercomputer. We moreover extend the smoothers available in DD-$\alpha$AMG, paying particular attention to Richardson smoothing, which in our numerical experiments has led to a multigrid solver faster than smoothing with GCR and only 10% slower compared to SAP smoothing. Then we port the odd-even-preconditioned versions of GMRES and Richardson via CUDA. Finally, we extend some computationally intensive coarse-grid operations via advanced vectorization.<|reference_end|>
@article{he2024extending,
    title={Extending DD-$\alpha$AMG on heterogeneous machines},
    author={Lianhua He, Gustavo Ramirez-Hidalgo and Ke-Long Zhang},
    journal={arXiv preprint arXiv:2407.08092},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.08092},
    primaryClass={hep-lat cs.DC cs.NA math.NA}
}