Parallel Implementation of the Conjugate Gradient Method for Solving the Inverse Gravimetry Problem on GPU

2019

Closed access Metadata (CSL-JSON)
DOI 10.3997/2214-4609.201902037
Data confidence 70 medium
How this score is calculated

It is built from checkable properties of the record itself: whether the article has a DOI, how many independent sources confirmed its data, whether the required fields are filled in, and how recent the latest confirmation is. Disagreement between sources and a missing identifier lower it. A value of 80 or above means the record was published without further checking; 60 to 79 means it is published at reduced confidence, with a manual check advisable. A record verified by an editor by hand is never pushed below 90 by a recalculation. This scores our data about the article, not the article itself.

Abstract

Summary The paper is devoted to development and implementation of efficient parallel algorithm based on the conjugate gradient method for solving the nonlinear inverse potential problem of finding a boundary surface in two-layer medium. The algorithm uses the approximation of the Jacobian matrix of the integral operator based on dropping out the lesser elements and utilizing the Toeplitz-block-Toeplitz structure of the matrix. The parallel algorithm was implemented for the graphics processor using CUDA technology. The numerical experiments was carried out. Investigation of efficiency and speedup of the parallel algorithm was performed.