Evaluating polynomials in several variables and their derivatives
on a GPU computing processor
Jan Verschelde and
Genady Yoffe
Abstract:
In order to obtain more accurate solutions of polynomial systems
with numerical continuation methods we use multiprecision arithmetic.
Our goal is to offset the overhead of double double arithmetic
accelerating the path trackers and in particular Newton's method
with a general purpose graphics processing unit.
In this paper we describe algorithms for the massively parallel evaluation
and differentiation of sparse polynomials in several variables.
We report on our implementation of the algorithmic differentiation
of products of variables on the NVIDIA Tesla C2050 Computing Processor
using the NVIDIA CUDA compiler tools.
The 26th Parallel and Distributed Processing Symposium (IPDPS12).
The 13th IEEE International Workshop on Parallel and Distributed Scientific
and Engineering Computing (PDSEC-12), 21-25 May 2012, Shanghai, China.
slides of the talk