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.

Installation

HYPRE.jl can be installed from the Pkg REPL (press ] in the Julia REPL to enter):

All notable changes are documented in CHANGELOG.md.

Usage

Some basic usage examples are shown below. See the documentation for details.

Example: Single-core solve with standard sparse matrices

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)

Example: Multi-core solve using PartitionedArrays.jl

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)

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