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Julia interface to HYPRE ("high performance preconditioners and solvers featuring multigrid methods for the solution of large, sparse linear systems of equations on massively parallel computers").
While the main purpose of HYPRE is to solve problems on multiple cores, it can also be used for single core problems. HYPRE.jl aims to make it easy to use both modes of operation, with an interface that should be familiar to Julia programmers. This README includes some basic examples -- refer to the documentation for more details, and for information about the included solvers and preconditioners and how to configure them.
HYPRE.jl can be installed from the Pkg REPL (press ] in the Julia REPL to enter):
(@v1) pkg> add HYPRE
To configure MPI, see the documentation for MPI.jl.
All notable changes are documented in CHANGELOG.md.
Some basic usage examples are shown below. See the documentation for details.
It is possible to use Julia's standard sparse arrays (SparseMatrixCSC from the
SparseArrays.jl standard library, and SparseMatrixCSR from the
SparseMatricesCSR.jl package) directly in HYPRE.jl. For example, to solve
Ax = b with conjugate gradients:
# Initialize linear system
A = SparseMatrixCSC(...)
b = Vector(...)
# Create a conjugate gradients solver
cg = HYPRE.PCG()
# Compute the solution
x = HYPRE.solve(cg, A, b)For multi-core problems it is possible to use PartitionedArrays.jl directly
with HYPRE.jl. Once the linear system is setup the solver interface is identical. For
example, to solve Ax = b with bi-conjugate gradients and an algebraic multigrid
preconditioner:
# Initialize linear system
A = PSparseMatrix(...)
b = PVector(...)
# Create preconditioner
precond = BoomerAMG()
# Create a bi-conjugate gradients solver
bicg = HYPRE.BiCGSTAB(; Precond = precond)
# Compute the solution
x = HYPRE.solve(bicg, A, b)