This package provides a high level interface for PETSc, enabling the use of PETSc as an AbstractArray.
A low level interface is also available in the submodule PETSc.C. The package supports 64-bit integers the PetscInt type described in the PETSc documentation, and Float64, Float32, and Complex128 for the PetscScalar type. In a default build of the package, all types can be used simultaneously, using multiple dispatch to determine which version of PETSc to use.

This package requires the MPI.jl package be installed. Once it is installed you should be able to run both Julia and Petsc in parallel using MPI for all communication. The testing verifies that PETSc can be used both serially and in parallel.

To use the package, simply put using PETSc at the top of your Julia source file. The module exports the names of all the functions, as well as the PETSc data type aliases and constants such as PETSC_DECIDE.

In general, it is possible to run PETSc in parallel. To do so with 4 processors, do:

mpirun -np 4 julia ./name_of_file

Note that this launches 4 independent Julia processes. They are not aware of each other using Julia's built-in parallelism, and MPI is used for all communications.

To run in serial, do:

julia ./name_of_file

Even when running serially, the MPI.jl package must be installed.

An example of using a Krylov subspace method to solve a linear system is in test/test_ksp.jl, which solves a simple system with a Krylov subspace method and compares the result with a direct solve using Julia's backslash operator. This works in serial and in parallel. It requires some variables declared at the top of runtests.jl to work.

• Make the script for building PETSc more flexible, e.g. allowing more configuration options like building BLAS or LAPCK, while ensure it remains completely autonomous (needed for Travis testing)
• Wrap more KSP functions

The AbstractArray for PetscVec is implemented. Some additional PETSc BLAS functions are wrapped as well.

The AbstractArray interface for PetscMat is implemented. Preallocation is supported through optional keyword arguments to the matrix constructor or the setpreallocation function. It possible to set multiple values in the matrix without intermediate assembly using the assemble function or by setting the Mat object field assembling to false and calling setindex repeatedly.

Just enough KSP functions are implimented to do a GMRES solve. Adding more functionality is the current priority.

/src : source files. PETSc.jl is the main file containing initialization, with the functions for each type of Petsc object in its own file. All constants are declared in petsc_constants.jl.

/src/generated: auto generated wrappers from Clang.jl. Not directly useful, but easy to modify to make useful

/test : contains runtest.jl, which does some setup and runs all tests on all three version of Petsc currently supported. Tests for each type of Petsc object (mirroring the files in /src) are contained in separate files.

/deps : builds Petsc if needed. See description below

By default, building the package will build 3 versions of PETSc in the /deps directory, and writes the file lib_locations.jl to the /deps directory to tell the package the location of the libraries. Note that this builds the debug versions of PETSc, which are recommended to use for all development. If you wish to do high performance computations, you should build the optimized versions of the library. See the PETSc website for details.

If you wish to build fewer than 3 version of PETSc or to use your own build of PETSc rather than having the package build it for you, there a several environmental variables that control what the build system will do. For all the variables listed below, name is one of RealDouble, RealSingle, or ComplexDouble, and specifies which version of the library the variable describes.

If the varibles JULIA_PETSC_name_DIR and JULIA_PETSC_name_ARCH are set to the PETSC_DIR and PETSC_ARCH of an existing PETSc installation, the build system will use that PETSc installation for the version of PETSc specified by name.

If the variable JULIA_PETSC_name_NOBUILD exists (the value does not matter), then the package will not build a version the named version of PETSc.

If the variable JULIA_PETSC_OPT exists (the value does not matter), then a set of default optimization flags are passed to the PETSc configure script.

If the variable JULIA_PETSC_FLAGS exists and JULIA_PETSC_OPT does not, its value is used passed to the PETSc configure script (for all builds). The user should never specify --with-64-bit-indices, --with-scalar-type or --with-precision, because this would break the build process for the different version of PETSc.

If neither of the above variables exist, a standard build is performed.

PETSc uses preprocessor variables to decide what code to include when compiling the library. Clang does not know what preprocessor variables were defined at compile time, so it does not correctly detect the typealiases PetscScalar, PetscReal, etc. To correctly autogenerate wrappers, the proper variables must be passed to Clang with the -D switch. Note that users will not need to generate their own wrappers because they have already been generated and commit to the repo.