P14: Robust SA-AMG Solver by Extraction of Near-Kernel
Vectors
SessionPoster Reception
Authors
Event Type
ACM Student Research Competition
Poster
Reception
TimeTuesday, November 14th5:15pm -
7pm
LocationFour Seasons Ballroom
DescriptionThe smoothed aggregation algebraic multigrid (SA-AMG)
method is among the fastest solvers for large-scale
linear equations, Ax=b. The SA-AMG method achieves good
convergence and scalability by damping various
wavelength components efficiently. To achieve this
damping, this method creates multi-level matrices which
are hierarchically smaller in dimension than the
original matrix. Moreover, the convergence can be
further improved by setting near-kernel vectors p, which
satisfy Ap≈0 and p≠0. Generally, the same number of
near-kernel vectors are used at each level. In the
present work, we propose a method that extracts and adds
near-kernel vectors at each level. We evaluate the
performance of the solver that extracts the near-kernel
vectors and adds them at each level. We use the
three-dimensional elastic problem and employ up to 512
processes on the FX10 supercomputer system. By using
this method, the performance is improved compared with
previous work.
