Use `conflicts` directive whenever possible.
This allows failing early when conflicting variants are used.
Do not silently ignore `+parmetis` variant when `~metis`.
Instead throw an error during concretization.
Simplify the "Makefile.inc" generation.
This will make easier to add new variants in the future.
* Added version patch for 1.4.0 tag on mpark-variant
Redirected urls to git and github tags.
* Updated to commit hashes
* Update var/spack/repos/builtin/packages/mpark-variant/package.py
Co-authored-by: Axel Huebl <axel.huebl@plasma.ninja>
* Update var/spack/repos/builtin/packages/mpark-variant/package.py
Co-authored-by: Axel Huebl <axel.huebl@plasma.ninja>
* Update var/spack/repos/builtin/packages/mpark-variant/package.py
Co-authored-by: Anthony J Zukaitis <zukaitis@lanl.gov>
Co-authored-by: Axel Huebl <axel.huebl@plasma.ninja>
Original commit message:
This feature of CMake allows packages to increase reproducibility, especially between
Spack- and manual builds. It also allows packages to sidestep certain parsing bugs in
extremely long ``cmake`` commands, and to avoid system limits on the length of the
command line.
Adding:
Co-authored by: Chris White <white238@llnl.gov>
This reverts commit c4f0a3cf6c.
CachedCMakePackage is a specialized class for packages built using CMake initial cache.
This feature of CMake allows packages to increase reproducibility, especially between
Spack- and manual builds. It also allows packages to sidestep certain parsing bugs in
extremely long ``cmake`` commands, and to avoid system limits on the length of the
command line.
Autoconf before 2.70 will erroneously pass ifx's -loopopt argument to the
linker, requiring all packages to use autoconf 2.70 or newer to use ifx.
This is a hotfix enabling ifx to be used in Spack. Instead of bothering
to upgrade autoconf for every package, we'll just strip out the
problematic flag if we're in `ld` mode.
- [x] Add a conditional to the `cc` wrapper to skip `-loopopt` in `ld`
mode. This can probably be generalized in the future to strip more
things (e.g., via an environment variable we can constrol from
Spack) but it's good enough for now.
- [x] Add a test ensuring that `-loopopt` arguments are stripped in link
mode, but not in compile mode.
Since `lazy_lexicographic_ordering` handles `None` comparison for us, we
don't need to adjust the spec comparators to return empty strings or
other type-specific empty types. We can just leverage the None-awareness
of `lazy_lexicographic_ordering`.
- [x] remove "or ''" from `_cmp_iter` in `Spec`
- [x] remove setting of `self.namespace` to `''` in `MockPackage`
We have been using the `@llnl.util.lang.key_ordering` decorator for specs
and most of their components. This leverages the fact that in Python,
tuple comparison is lexicographic. It allows you to implement a
`_cmp_key` method on your class, and have `__eq__`, `__lt__`, etc.
implemented automatically using that key. For example, you might use
tuple keys to implement comparison, e.g.:
```python
class Widget:
# author implements this
def _cmp_key(self):
return (
self.a,
self.b,
(self.c, self.d),
self.e
)
# operators are generated by @key_ordering
def __eq__(self, other):
return self._cmp_key() == other._cmp_key()
def __lt__(self):
return self._cmp_key() < other._cmp_key()
# etc.
```
The issue there for simple comparators is that we have to bulid the
tuples *and* we have to generate all the values in them up front. When
implementing comparisons for large data structures, this can be costly.
This PR replaces `@key_ordering` with a new decorator,
`@lazy_lexicographic_ordering`. Lazy lexicographic comparison maps the
tuple comparison shown above to generator functions. Instead of comparing
based on pre-constructed tuple keys, users of this decorator can compare
using elements from a generator. So, you'd write:
```python
@lazy_lexicographic_ordering
class Widget:
def _cmp_iter(self):
yield a
yield b
def cd_fun():
yield c
yield d
yield cd_fun
yield e
# operators are added by decorator (but are a bit more complex)
There are no tuples that have to be pre-constructed, and the generator
does not have to complete. Instead of tuples, we simply make functions
that lazily yield what would've been in the tuple. If a yielded value is
a `callable`, the comparison functions will call it and recursively
compar it. The comparator just walks the data structure like you'd expect
it to.
The ``@lazy_lexicographic_ordering`` decorator handles the details of
implementing comparison operators, and the ``Widget`` implementor only
has to worry about writing ``_cmp_iter``, and making sure the elements in
it are also comparable.
Using this PR shaves another 1.5 sec off the runtime of `spack buildcache
list`, and it also speeds up Spec comparison by about 30%. The runtime
improvement comes mostly from *not* calling `hash()` `_cmp_iter()`.
* New package py-argh
* Fixed deps
* Changed setuptools type
* Update var/spack/repos/builtin/packages/py-argh/package.py
Co-authored-by: Adam J. Stewart <ajstewart426@gmail.com>
Co-authored-by: Adam J. Stewart <ajstewart426@gmail.com>
* Make -j flag less exceptional
The -j flag in spack behaves differently from make, ctest, ninja, etc,
because it caps the number of jobs to an arbitrary number 16.
Spack will behave like other tools if `spack install` uses a reasonable
default, and `spack install -j <num>` *overrides* that default.
This will be particularly useful for Spack usage outside of a traditional
HPC context and for HPC centers that encourage users to compile on
login nodes with many cores instead of on compute nodes, which has
become increasingly common as individual nodes have more cores.
This maintains the existing default value of min(num_cpus, 16). However,
as it is right now, Spack does a poor job at determining the number of
cpus on linux, since it doesn't take cgroups into account. This is
particularly problematic when using distributed builds with slurm. This PR
also introduces `spack.util.cpus.cpus_available()` to consolidate
knowledge on determining the number of available cores, and improves
core detection for linux. This should also improve core detection for Docker/
Kubernetes, which also use cgroups.
