论文标题
GPU加速不可压缩的流量求解器中线性系统序列的初步猜测
Initial Guesses for Sequences of Linear Systems in a GPU-Accelerated Incompressible Flow Solver
论文作者
论文摘要
我们考虑了几种在迭代求解线性系统的序列时生成初始猜测的方法，这表明它们可以在GPU加速的PDE求解器中有效实现，特别是用于不可压缩流的求解器。我们提出了基于稳定的多项式外推的新的初始猜测方法，并将其与Fischer的投影方法进行比较[15]，表明尽管仅需要存储一半并执行大量的数据移动和通信，但它们通常与投影方案具有竞争力。我们对这些算法的实现是作为GPU加速流求解器的Libaranumal集合集合的一部分免费获得的。
We consider several methods for generating initial guesses when iteratively solving sequences of linear systems, showing that they can be implemented efficiently in GPU-accelerated PDE solvers, specifically solvers for incompressible flow. We propose new initial guess methods based on stabilized polynomial extrapolation and compare them to the projection method of Fischer [15], showing that they are generally competitive with projection schemes despite requiring only half the storage and performing considerably less data movement and communication. Our implementations of these algorithms are freely available as part of the libParanumal collection of GPU-accelerated flow solvers.
