A Multithreaded Algorithm for Sparse Cholesky Factorization on Hybrid Multicore Architectures | Zendy

Meng Tang | Zendy; Mohamed Gadou | Zendy; Sanjay Ranka | Zendy

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A Multithreaded Algorithm for Sparse Cholesky Factorization on Hybrid Multicore Architectures

Author(s) -

Meng Tang,

Mohamed Gadou,

Sanjay Ranka

Publication year - 2017

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2017.05.260

Subject(s) - computer science , cholesky decomposition , multi core processor , parallel computing , multithreading , concurrency , cuda , sparse matrix , algorithm , thread (computing) , distributed computing , operating system , eigenvalues and eigenvectors , physics , quantum mechanics , gaussian

We present a multithreaded method for supernodal sparse Cholesky factorization on a hybrid multicore platform consisting of a multicore CPU and GPU. Our algorithm can utilize concurrency at different levels of the elimination tree by using multiple threads in both the CPU and the GPU. The elimination tree is a tree data structure describing the workflow of the factorization. Our experiments results on a platform consisting of an Intel multicore processor along with an Nvidia GPU indicate a significant improvement in performance and energy over single-threaded supernodal algorithm.

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