On the Efficiency of Supernodal Factorization in Interior-Point Method Using CPU-GPU Collaboration

On the Efficiency of Supernodal Factorization in Interior-Point Method Using CPU-GPU Collaboration

Primal-dual interior-point method (PDIPM) is the most efficient technique for solving sparse linear programming (LP) problems. Despite its efficiency, PDIPM remains a compute-intensive algorithm. Fortunately, graphics processing units (GPUs) have the potential to meet this requirement. However, thei...

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Bibliographic Details
Main Authors: Usman Ali Shah, Suhail Yousaf, Iftikhar Ahmad, Muhammad Ovais Ahmad
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Sparse matrices
GPU
GPGPU
linear programming
primal-dual interior-point method
Online Access:https://ieeexplore.ieee.org/document/9130677/

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