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arXiv 2402.15033math.NAcs.DCcs.NA
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两阶段块正交化以提升s步GMRES的性能

Two-Stage Block Orthogonalization to Improve Performance of $s$-step GMRES

Ichitaro Yamazaki, Andrew J. Higgins, Erik G. Boman, Daniel B. Szyld

AI总结:

本文提出两阶段块正交化方案,通过延迟正交化至第二阶段以降低通信成本,在保持稳定性的同时将s步GMRES的正交化时间和总求解时间分别加速最高2.6倍和1.6倍。

摘要:

在当前计算机架构上,GMRES的性能可能受限于其生成Krylov子空间标准正交基向量的通信成本。为了解决这一性能瓶颈,其s步变体一次对包含s个基向量的块进行正交化,有可能将通信成本降低s倍。不幸的是,对于较大的步长s,求解器可能生成极度病态的基向量,而为了在实际中保持稳定性,通常采用保守的小步长,这限制了s步求解器的性能。为了在小步长下提升性能,本文引入了一种两阶段块正交化方案。与原始方案类似,所提方法的第一阶段一次处理包含s个基向量的块,但其目标是以更低的成本维持所生成基向量的良好条件性。基向量的正交化被推迟到第二阶段,直到生成了足够多的基向量以获得更高的性能。我们的分析表明所提出的两阶段方案具有稳定性。性能得到提升的原因在于,虽然所需的计算量与原始方案相同,但大部分通信在所提方案的第二阶段完成,从而降低了总体通信需求。我们在Summit超级计算机上使用多达192块NVIDIA V100 GPU的性能结果表明,在求解二维Laplace问题时,与原始s步GMRES相比,两阶段方法可以将正交化时间和总求解时间分别降低高达2.6倍和1.6倍,而原始s步GMRES相对于标准GMRES已经分别获得了2.1倍和1.8倍的加速。对于三维问题以及来自SuiteSparse Matrix Collection的矩阵,也获得了类似的加速效果。

英文摘要:

On current computer architectures, GMRES' performance can be limited by its communication cost to generate orthonormal basis vectors of the Krylov subspace. To address this performance bottleneck, its $s$-step variant orthogonalizes a block of $s$ basis vectors at a time, potentially reducing the communication cost by a factor of $s$. Unfortunately, for a large step size $s$, the solver can generate extremely ill-conditioned basis vectors, and to maintain stability in practice, a conservatively small step size is used, which limits the performance of the $s$-step solver. To enhance the performance using a small step size, in this paper, we introduce a two-stage block orthogonalization scheme. Similar to the original scheme, the first stage of the proposed method operates on a block of $s$ basis vectors at a time, but its objective is to maintain the well-conditioning of the generated basis vectors with a lower cost. The orthogonalization of the basis vectors is delayed until the second stage when enough basis vectors are generated to obtain higher performance. Our analysis shows the stability of the proposed two-stage scheme. The performance is improved because while the same amount of computation as the original scheme is required, most of the communication is done at the second stage of the proposed scheme, reducing the overall communication requirements. Our performance results with up to 192 NVIDIA V100 GPUs on the Summit supercomputer demonstrate that when solving a 2D Laplace problem, the two-stage approach can reduce the orthogonalization time and the total time-to-solution by the respective factors of up to $2.6\times$ and $1.6\times$ over the original $s$-step GMRES, which had already obtained the respective speedups of $2.1\times$ and $1.8\times$ over the standard GMRES. Similar speedups were obtained for 3D problems and for matrices from the SuiteSparse Matrix Collection.

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