Data Parallel Algorithms for General Purpose Graphics Processing Units

Accelerated Polynomial Evaluation and Differentiation at Power Series in Multiple Double Precision

To compensate for the cost overhead of multiple doubleprecision and power series arithmetic, data parallel algorithms for general purpose graphics processing units are presented.The problem is to evaluate a polynomial in several variables and its gradientat a power series truncated to some finite degree with multiple doubleprecision arithmetic.To compensate for the cost overhead of multiple doubleprecision and power series arithmetic, data parallel algorithms for general purpose graphics processing units are presented.The reverse mode of algorithmic differentiation is organized into a massively parallel computationof many convolutions and additions of truncated power series.The problem is to evaluate a polynomial in several variables and its gradientat a power series truncated to some finite degree with multiple doubleprecision arithmetic.The reverse mode of algorithmic differentiation is organized into a massively parallel computationof many convolutions and additions of truncated power series.Experimental results demonstrate that teraflop performance is obtained in deca doubleprecision with power series truncated at degree 152.