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GPU acceleration of Newton's method for large systems of polynomial equations in double double and quad double arithmetic

机译:用于大型多项式系统的牛顿方法的GpU加速   双重双四次算术中的方程

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摘要

In order to compensate for the higher cost of double double and quad doublearithmetic when solving large polynomial systems, we investigate theapplication of NVIDIA Tesla K20C general purpose graphics processing unit. Thefocus on this paper is on Newton's method, which requires the evaluation of thepolynomials, their derivatives, and the solution of a linear system to computethe update to the current approximation for the solution. The reverse mode ofalgorithmic differentiation for a product of variables is rewritten in a binarytree fashion so all threads in a block can collaborate in the computation. Fordouble arithmetic, the evaluation and differentiation problem is memory bound,whereas for complex quad double arithmetic the problem is compute bound. Withacceleration we can double the dimension and get results that are twice asaccurate in about the same time.
机译:为了弥补求解大型多项式系统时双精度和双精度双重算法的较高成本,我们研究了NVIDIA Tesla K20C通用图形处理单元的应用。本文的重点是牛顿法,该方法需要对多项式,它们的导数以及线性系统的解进行求值,以计算对该解的当前近似值的更新。变量乘积的算法微分的反向模式以二叉树方式重写,因此块中的所有线程都可以在计算中进行协作。对于双精度算术,评估和微分问题是内存限制,而对于复杂四重双精度算术,问题是计算范围。有了加速度,我们可以将尺寸加倍,并在大约同一时间获得两倍精度的结果。

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