Title :
Enhancing the Performance of Conjugate Gradient Solvers on Graphic Processing Units
Author :
Dehnavi, Maryam Mehri ; Fernández, David M. ; Giannacopoulos, Dennis
Author_Institution :
Electr. & Comput. Eng. Dept., McGill Univ. Montreal, Montreal, QC, Canada
fDate :
5/1/2011 12:00:00 AM
Abstract :
A study of the fundamental obstacles to accelerate the preconditioned conjugate gradient (PCG) method on modern graphic processing units (GPUs) is presented and several techniques are proposed to enhance its performance over previous work independent of the GPU generation and the matrix sparsity pattern. The proposed enhancements increase the performance of PCG up to 23 times compared to vector optimized PCG results on modern CPUs and up to 3.4 times compared to previous GPU results.
Keywords :
computer graphic equipment; conjugate gradient methods; coprocessors; parallel processing; sparse matrices; CPU; GPU generation; fundamental obstacle; graphic processing unit; matrix sparsity pattern; preconditioned conjugate gradient method; vector optimized PCG; Acceleration; Computer architecture; Graphics processing unit; Instruction sets; Kernel; Optimization; Sparse matrices; Computer architecture; conjugate gradients (CGs); graphic processing units (GPUs); parallel processing;
Journal_Title :
Magnetics, IEEE Transactions on
DOI :
10.1109/TMAG.2010.2081662
