论文标题
混合精度Jacobi SVD算法
A mixed precision Jacobi SVD algorithm
论文作者
论文摘要
我们提出了一种用于计算密集矩阵的奇异值分解（SVD）的混合精度Jacobi算法。经过适当的预处理后，提出的算法将SVD以较低的精度计算为初始猜测，然后在工作精度中以迭代精度执行单方面的Jacobi旋转。通过将较低的精度溶液仔细转换为较高的精度，我们的算法在X86-64架构上的速度约为拉帕克（Lapack）的常见单侧Jacobi SVD算法的速度约为2倍，而无需牺牲准确性。
We propose a mixed precision Jacobi algorithm for computing the singular value decomposition (SVD) of a dense matrix. After appropriate preconditioning, the proposed algorithm computes the SVD in a lower precision as an initial guess, and then performs one-sided Jacobi rotations in the working precision as iterative refinement. By carefully transforming a lower precision solution to a higher precision one, our algorithm achieves about 2 times speedup on the x86-64 architecture compared to the usual one-sided Jacobi SVD algorithm in LAPACK, without sacrificing the accuracy.
