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Robust parallel eigenvector computation for the non-symmetric eigenvalue problem

机译:非对称特征值问题的强大的并行特征向量计算

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A standard approach for computing eigenvectors of a non-symmetric matrix reduced to real Schur form relies on a variant of backward substitution. Backward substitution is prone to overflow. To avoid overflow, the LAPACK eigenvector routine DTREVC3 associates every eigenvector with a scaling factor and dynamically rescales an entire eigenvector during the backward substitution such that overflow cannot occur. When many eigenvectors are computed, DTREVC3 applies backward substitution successively for every eigenvector. This corresponds to level-2 BLAS operations and constitutes a bottleneck. This paper redesigns the backward substitution such that the entire computation is cast as tile operations (level-3 BLAS). By replacing LAPACK's scaling factor with tile-local scaling factors, our solver decouples the tiles and sustains parallel scalability even when a lot of numerical scaling is necessary.
机译:用于计算非对称矩阵的特征向量的标准方法减少到真实梭形形式依赖于反替代的变型。向后替代易于溢出。为避免溢出,Lapack特征向量例程DTREvc3将每个特征向量与缩放因子相关联,并在后向替换期间动态重新加强整个特征向量,使得不能发生溢出。当计算许多特征向量时,DTREVC3依次向后替换为每个特征向量。这对应于等级-2BLAS操作,并构成瓶颈。本文重新设计了后向替换,使整个计算投用为瓷砖操作(Level-3 Blas)。通过用瓷砖局部缩放因子替换Lapack的缩放系数,我们的求解器即使在需要大量数值缩放时,我们的求解器也使瓷砖拆分并维持并行可扩展性。

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