Title :
Finite-Element Sparse Matrix Vector Multiplication on Graphic Processing Units
Author :
Dehnavi, Maryam Mehri ; Fernández, David M. ; Giannacopoulos, Dennis
Author_Institution :
Dept. of Electr. & Comput. Eng., McGill Univ., Montreal, QC, Canada
Abstract :
A wide class of finite-element (FE) electromagnetic applications requires computing very large sparse matrix vector multiplications (SMVM). Due to the sparsity pattern and size of the matrices, solvers can run relatively slowly. The rapid evolution of graphic processing units (GPUs) in performance, architecture, and programmability make them very attractive platforms for accelerating computationally intensive kernels such as SMVM. This work presents a new algorithm to accelerate the performance of the SMVM kernel on graphic processing units.
Keywords :
computer graphic equipment; coprocessors; finite element analysis; matrix multiplication; sparse matrices; finite-element electromagnetic applications; finite-element sparse matrix vector multiplication; graphic processing units; sparsity pattern; Acceleration; Computer architecture; Finite element methods; Graphics; Iron; Iterative methods; Kernel; Parallel processing; Sparse matrices; Yarn; Computer architecture; graphic processing units (GPUs); parallel processing; sparse matrix vector multiplication (SMVM);
Journal_Title :
Magnetics, IEEE Transactions on
DOI :
10.1109/TMAG.2010.2043511
