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Add the latest pybind11 release.
Since release 2.6.0 was a major release with many changes in the
build system, prefer 2.5.0 for now.
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Also added maintainers for the package recipe.
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Co-authored-by: Massimiliano Culpo <massimiliano.culpo@gmail.com>
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At least xcb-proto 1.14.1 is only avaiable from the new URL, so change
both to be future-proof.
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* Add sionlib and linktest packages
* fix flake8
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* Add relocatable code flag to trilinos
* Make CUDA RDC and varainat
* adjust default of cuda_rdc
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* fix: leading . is not needed in extension kwarg
* mfem: add support for NVIDIA AmgX
fix: proper spacing
* mfem: use conflict to indicate that AmgX is expected to depend on CUDA
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Disable dataman by default. It pulls heavy dependencies that are
often not needed for HPC (ZMQ) and it currently does not link
with popular compilers.
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fixes #19966
Global arrays supports GCC 10 since version 5.7.1,
therefore a conflict has been added to avoid old
releases to error at build-time.
Removed the 'blas' and 'lapack' variant since
BLAS and LAPACK are always a dependency, and
if not specified during configure, a version
of these APIs vendored with Global Arrays is
built.
Fixed a few options in configuration.
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* simde: New package
* remove 0.5.0.
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The point of this variant is to give the end user an option to use system
installed fabrics such as mofed instead of upstream fabrics such as rdma-core.
This was found to avoid run time errors on some systems.
Co-authored-by: nithintsk <nithintsk@github.com>
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This PR fixes two problems with clang/llvm's version detection. clang's
version output looks like this:
```
clang version 11.0.0
Target: x86_64-unknown-linux-gnu
```
This caused clang's version to be misdetected as:
```
clang@11.0.0
Target:
```
This resulted in errors when trying to actually use it as a compiler.
When using `spack external find`, we couldn't determine the compiler
version, resulting in errors like this:
```
==> Warning: "llvm@11.0.0+clang+lld+lldb" has been detected on the system but will not be added to packages.yaml [reason=c compiler not found for llvm@11.0.0+clang+lld+lldb]
```
Changing the regex to only match until the end of the line fixes these
problems.
Fixes: #19473
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When building llvm with CUDA support, it needs to find libffi. Without
pkg-config, libffi will not be found.
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* Updated the cuDNN recipe to generate the proper version names for only
the arhcitecture that you are on. This prevents the concretizer from
selecting a source code version that is incompatible with your current
architecture. Additionally, add constraints to ensure that the
corresponding CUDA version is properly set as well.
* Added maintainer
* Fixed renaming for darwin systems
* Fixed flake8
* Fixed flake8
* Fixed range typo
* Update var/spack/repos/builtin/packages/cudnn/package.py
Co-authored-by: Adam J. Stewart <ajstewart426@gmail.com>
* Fixed style issues
Co-authored-by: Adam J. Stewart <ajstewart426@gmail.com>
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* seems to have been introduced errorously by users using gitk-based
workflows. This should be handled by the git package
* fixes build problems on OSX bigsur
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* AMD ROCm 3.9.0 release: Bump up version for aomp, roctracer-dev and updates to hip/hip-rocclr
* Update package.py
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* charmpp: various fixes
- change URLs to https
- address deprecated/renamed versions
- make it build with the cmake build system
* flake8
* Apply suggestions from code review
Co-authored-by: Adam J. Stewart <ajstewart426@gmail.com>
Co-authored-by: Adam J. Stewart <ajstewart426@gmail.com>
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* py-ipykernel: fix bug in phase method
* Fix bug in executable calling
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Updated boto3 dependency and removed useless comments.
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* create HipPackage base class and do some refactoring
* comments and added conflict to raja for openmp with hip
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* [hep] add hep tag to relevant packages
* [lcio] add hep label
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There is a post-install routine in `ipykernel` that needs to be
called for proper registration with jupyter.
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* updated @master to point to the master branch
* also added a @spack that points to a fixed commit
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Users can add test() methods to their packages to run smoke tests on
installations with the new `spack test` command (the old `spack test` is
now `spack unit-test`). spack test is environment-aware, so you can
`spack install` an environment and then run `spack test run` to run smoke
tests on all of its packages. Historical test logs can be perused with
`spack test results`. Generic smoke tests for MPI implementations, C,
C++, and Fortran compilers as well as specific smoke tests for 18
packages.
Inside the test method, individual tests can be run separately (and
continue to run best-effort after a test failure) using the `run_test`
method. The `run_test` method encapsulates finding test executables,
running and checking return codes, checking output, and error handling.
This handles the following trickier aspects of testing with direct
support in Spack's package API:
- [x] Caching source or intermediate build files at build time for
use at test time.
- [x] Test dependencies,
- [x] packages that require a compiler for testing (such as library only
packages).
See the packaging guide for more details on using Spack testing support.
Included is support for package.py files for virtual packages. This does
not change the Spack interface, but is a major change in internals.
Co-authored-by: Tamara Dahlgren <dahlgren1@llnl.gov>
Co-authored-by: wspear <wjspear@gmail.com>
Co-authored-by: Adam J. Stewart <ajstewart426@gmail.com>
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* Added -level_zero -rocm -opencl flags and sha256 for TAU v2.30.
* Removed the depends_on clause for OpenCL and added a variant for OneAPI level_zero.
* remove depends_on rocm
* remove depends_on rocprofiler
Co-authored-by: eugeneswalker <eugenesunsetwalker@gmail.com>
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This commit address the case of concretizing a root spec with a
transitive conditional dependency on a virtual package, provided
by an external. Before these modifications default variant values
for the dependency bringing in the virtual package were not
respected, and the external package providing the virtual was added
to the DAG.
The issue stems from two facts:
- Selecting a provider has higher precedence than selecting default variants
- To ensure that an external is preferred, we used a negative weight
To solve it we shift all the providers weight so that:
- External providers have a weight of 0
- Non external provider have a weight of 10 or more
Using a weight of zero for external providers is such that having
an external provider, if present, or not having a provider at all
has the same effect on the higher priority minimization.
Also fixed a few minor bugs in concretize.lp, that were causing
spurious entries in the final answer set.
Cleaned concretize.lp from leftover rules.
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This commit introduces a new rule:
real_node(Package) :- not external(Package), node(Package).
that permits to distinguish between an external node and a
real node that shouldn't trim dependency. It solves the
case of concretizing ninja with an external Python.
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As reported, conflicts with compiler ranges were not treated
correctly. This commit adds tests to verify the expected behavior
for the new concretizer.
The new rules to enforce a correct behavior involve:
- Adding a rule to prefer the compiler selected for
the root package, if no other preference is set
- Give a strong negative weight to compiler preferences
expressed in packages.yaml
- Maximize on compiler AND compiler version match
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If preferred variants are present, they'll
set the default value of a variant. Otherwise
the default value is what is encoded
in package.py
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updated sha256sum of download .tgz
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