* Add a new +clanglibcpp option for Boost
Currently, the compile of boost with clang will use the stdlibc++. This patch adds an optional flag to use clangs included libc++ instead.
* Linting
Fix long lines and white space errors
- The shell script uses arrays and hence only works on sophisticated shells and not the default `sh`. For clarity the shebang `#!/bin/bash` has been used instead.
- This fakes out GitFetchStrategy to try code paths for different git
versions.
- This allows us to test code paths for old versions using a newer git
version.
- Tests use a session-scoped mock stage directory so as not to interfere
with the real install.
- Every test is forced to clean up after itself with an additional check.
We now automatically assert that no new files have been added to
`spack.stage_path` during each test.
- This means that tests that fail installs now need to clean up their
stages, but in all other cases the check is useful.
- Be explicit about stage creation during the install process.
- This avoids accidental creation of stages
- e.g., during `spack install --fake`, stages were erroneously recreated
after being destroyed
- This prevents the main spack install from being clusttered by
invocations of `spack test`.
- This uses a session-scoped stage fixture to ensure tests don't
interfere.
- Spack's core package interface was previously overly stateful, in that
calling methods like `do_stage()` could change your working directory.
- This removes Stage's `chdir` and `chdir_to_source` methods and replaces
their usages with `with working_dir(stage.path)` and `with
working_dir(stage.source_path)`, respectively. These ensure the
original working directory is preserved.
- This not only makes the API more usable, it makes the tests more
deterministic, as previously a test could leave the current working
directory in a bad state and cause subsequent tests to fail
mysteriously.
1.64 had issues serialization (make_array and others) when built with
+mpi+python. It appears that those issues are fixed in 1.65.1
so we can remove preferred tag from 1.63.
* initial update of sundials package
* fix bugs in initial sundials update
* add xsdk cmake setup, fix generic math option, add cuda/raja Makefiles to install fixes
* Fix lapack install bug, add new conflicts, clean up formatting
* Address pull requeset comments and make fomatting style consistent
Remove blas variant as blas is only needed when used by an external
linear solver. Set related CMake blas variables as needed depending
enabled external linear solvers.
Add minimum required CMake version.
Additional conflicts and dependencies for external libraries based
on mpi, indextype, and precision.
Fix SuperLU_MT logic to check which threading type SuperLU_MT was
configured with.
Add maintaiers.
Change Sundials solver options to use an array of values.
Consistently use % for formatting.
* change triple-single quotes to single quotes
* Change indextype option to a single int64 option
- Assertion would search for root through all possible paths.
- It's also really slow.
- This isn't needed anymore. We're pretty good at ensuring single-rooted
DAGs, and this assertion has never been thrown.
- This shaves another 6 seconds off r-rminer concretization
- This reduces concretization time for r-rminer from over 1 minute to
only 16 seconds.
- OrderedDict ends up taking a *lot* of time to compare larger specs.
- An OrderedDict isn't actually needed here. It's actually not possible
to find duplicates, and we end up sorting the contents of the
OrderedDict anyway.
- This is an optimization to the way traverse_edges iterates over
successors.
- Previous version called dependencies_dict(), which involved a lot of
redundant work (creating dicts and calling caonical_deptype)
- Spack ends up constructing compilers frequently from YAML data.
- This caches the result of parsing the compiler config
- The logic in compilers/__init__.py could use a bigger cleanup, but this
makes concretization much faster for now.
- on macOS, this also ensures that xcrun is called only twice, as opposed
to every time a new compiler object is constructed.
This adds a workflow section on how to use spack on Docker.
It provides an example on the best-practices I collected over the
last months and circumvents the common pitfalls I tapped in.
Works with MPI, CUDA, Modules, execution as root, etc.
Background: Developed initially for PIConGPU.
