在异构机器上扩展DD-$α$AMG
Extending DD-$α$AMG on heterogeneous machines
AI总结：
针对格点量子色动力学的DD-$α$AMG求解器，本文通过HIP转换代码适配ORISE超级计算机，扩展平滑器（Richardson平滑表现优异）、移植预处理求解器版本并向量化粗网格操作，提升其在异构机器上的性能。
摘要：
多重网格求解器是现代科学计算模拟中的标准工具。基于区域分解聚合的代数多重网格（也称为DD-$α$AMG求解器）是格点量子色动力学代数多重网格求解器的成功实现。其CPU实现使得对某些特定离散化方法构建原本计算上不可行的模拟成为可能，并且推动了该领域其他代数多重网格求解器的开发和改进。基于已通过CUDA部分移植、可在Nvidia GPU上运行多重网格求解器某些最细层操作的DD-$α$AMG现有版本，我们在此使用HIP转换CUDA代码以在ORISE超级计算机上运行。此外，我们扩展了DD-$α$AMG中可用的平滑器，特别关注Richardson平滑，在我们的数值实验中，这使得多重网格求解器比GCR平滑更快，仅比SAP平滑慢10%。然后我们通过CUDA移植了GMRES和Richardson的奇偶预处理版本。最后，我们通过高级向量化扩展了一些计算密集型的粗网格操作。
英文摘要：
Multigrid solvers are the standard in modern scientific computing simulations. Domain Decomposition Aggregation-Based Algebraic Multigrid, also known as the DD-$α$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-$α$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-$α$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.
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