Merge pull request #937

Fix documentation build

Author: luraess

docs/make.jl

@@ -82,6 +82,11 @@ makedocs(
     # wants to run the linkchecks locally they can set the environment variable `CI=true`.
     linkcheck = get(ENV, "CI", "false") == "true",
     linkcheck_useragent = nothing,
+    linkcheck_ignore = [
+        "https://mooseframework.inl.gov/help/troubleshooting.html",
+        r"https://docs\.open-mpi\.org/.*",
+        r"https://mpi4py\.readthedocs\.io/.*",
+    ],
 )
 
 deploydocs(

docs/src/configuration.md

@@ -51,11 +51,11 @@ This is the recommended way to use MPI.jl. By default, MPI.jl will use
 `MPICH_jll` as jll MPI backend.
 
 You can select from four different jll MPI binaries:
-- [`MPICH_jll`](http://www.mpich.org/),  the default
+- [`MPICH_jll`](https://www.mpich.org/),  the default
 - [`OpenMPI_jll`](https://www.open-mpi.org/), an alternative to MPICH
 - [`MPItrampoline_jll`](https://github.com/eschnett/MPItrampoline), a
   forwarding MPI implementation that uses another MPI implementation
-- [`MicrosoftMPI_jll`](https://docs.microsoft.com/en-us/message-passing-interface/microsoft-mpi)
+- [`MicrosoftMPI_jll`](https://learn.microsoft.com/en-us/message-passing-interface/microsoft-mpi)
   for Windows
 
 For example, to switch to OpenMPI, you would first use MPIPreferenes.jl to switch:

docs/src/usage.md

@@ -97,19 +97,19 @@ send and receive buffers for point-to-point and collective operations (they may
 
 ### CUDA
 
-Successfully running the [alltoall\_test\_cuda.jl](../examples/alltoall_test_cuda.jl)
+Successfully running the [alltoall\_test\_cuda.jl](https://github.com/JuliaParallel/MPI.jl/blob/master/docs/examples/alltoall_test_cuda.jl)
 should confirm your MPI implementation to have the CUDA support enabled. Moreover, successfully running the
-[alltoall\_test\_cuda\_multigpu.jl](../examples/alltoall_test_cuda_multigpu.jl) should confirm
+[alltoall\_test\_cuda\_multigpu.jl](https://github.com/JuliaParallel/MPI.jl/blob/master/docs/examples/alltoall_test_cuda_multigpu.jl) should confirm
 your CUDA-aware MPI implementation to use multiple Nvidia GPUs (one GPU per rank).
 
 If using OpenMPI, the status of CUDA support can be checked via the
 [`MPI.has_cuda()`](@ref) function.
 
 ### ROCm
 
-Successfully running the [alltoall\_test\_rocm.jl](../examples/alltoall_test_rocm.jl)
+Successfully running the [alltoall\_test\_rocm.jl](https://github.com/JuliaParallel/MPI.jl/blob/master/docs/examples/alltoall_test_rocm.jl)
 should confirm your MPI implementation to have the ROCm support (AMDGPU) enabled. Moreover, successfully running the
-[alltoall\_test\_rocm\_multigpu.jl](../examples/alltoall_test_rocm_multigpu.jl) should confirm
+[alltoall\_test\_rocm\_multigpu.jl](https://github.com/JuliaParallel/MPI.jl/blob/master/docs/examples/alltoall_test_rocm_multigpu.jl) should confirm
 your ROCm-aware MPI implementation to use multiple AMD GPUs (one GPU per rank).
 
 If using OpenMPI, the status of ROCm support can be checked via the