700 research outputs found
Parallel Unsmoothed Aggregation Algebraic Multigrid Algorithms on GPUs
We design and implement a parallel algebraic multigrid method for isotropic
graph Laplacian problems on multicore Graphical Processing Units (GPUs). The
proposed AMG method is based on the aggregation framework. The setup phase of
the algorithm uses a parallel maximal independent set algorithm in forming
aggregates and the resulting coarse level hierarchy is then used in a K-cycle
iteration solve phase with a -Jacobi smoother. Numerical tests of a
parallel implementation of the method for graphics processors are presented to
demonstrate its effectiveness.Comment: 18 pages, 3 figure
A Parallel Solver for Graph Laplacians
Problems from graph drawing, spectral clustering, network flow and graph
partitioning can all be expressed in terms of graph Laplacian matrices. There
are a variety of practical approaches to solving these problems in serial.
However, as problem sizes increase and single core speeds stagnate, parallelism
is essential to solve such problems quickly. We present an unsmoothed
aggregation multigrid method for solving graph Laplacians in a distributed
memory setting. We introduce new parallel aggregation and low degree
elimination algorithms targeted specifically at irregular degree graphs. These
algorithms are expressed in terms of sparse matrix-vector products using
generalized sum and product operations. This formulation is amenable to linear
algebra using arbitrary distributions and allows us to operate on a 2D sparse
matrix distribution, which is necessary for parallel scalability. Our solver
outperforms the natural parallel extension of the current state of the art in
an algorithmic comparison. We demonstrate scalability to 576 processes and
graphs with up to 1.7 billion edges.Comment: PASC '18, Code: https://github.com/ligmg/ligm
Adaptive Aggregation Based Domain Decomposition Multigrid for the Lattice Wilson Dirac Operator
In lattice QCD computations a substantial amount of work is spent in solving
discretized versions of the Dirac equation. Conventional Krylov solvers show
critical slowing down for large system sizes and physically interesting
parameter regions. We present a domain decomposition adaptive algebraic
multigrid method used as a precondtioner to solve the "clover improved" Wilson
discretization of the Dirac equation. This approach combines and improves two
approaches, namely domain decomposition and adaptive algebraic multigrid, that
have been used seperately in lattice QCD before. We show in extensive numerical
test conducted with a parallel production code implementation that considerable
speed-up over conventional Krylov subspace methods, domain decomposition
methods and other hierarchical approaches for realistic system sizes can be
achieved.Comment: Additional comparison to method of arXiv:1011.2775 and to
mixed-precision odd-even preconditioned BiCGStab. Results of numerical
experiments changed slightly due to more systematic use of odd-even
preconditionin
Aggregation-based aggressive coarsening with polynomial smoothing
This paper develops an algebraic multigrid preconditioner for the graph
Laplacian. The proposed approach uses aggressive coarsening based on the
aggregation framework in the setup phase and a polynomial smoother with
sufficiently large degree within a (nonlinear) Algebraic Multilevel Iteration
as a preconditioner to the flexible Conjugate Gradient iteration in the solve
phase. We show that by combining these techniques it is possible to design a
simple and scalable algorithm. Results of the algorithm applied to graph
Laplacian systems arising from the standard linear finite element
discretization of the scalar Poisson problem are reported
- …