This gives instructions for using Spack to install Kokkos and developing packages that depend on Kokkos.
Make sure you have downloaded Spack. The easiest way to configure the Spack environment is:
> source spack/share/spack/setup-env.shwith other scripts available for other shells. You can display information about how to install packages with:
> spack info kokkosThis will print all the information about how to install Kokkos with Spack. For detailed instructions on how to use Spack, see the User Manual.
By default, Spack doesn't 'see' anything on your system - including things like CMake and CUDA.
This can be limited by adding a packages.yaml to your $HOME/.spack folder that includes CMake (and CUDA, if applicable). For example, your packages.yaml file could be:
packages:
cuda:
buildable: false
externals:
- prefix: /opt/local/ppc64le-pwr8-nvidia/cuda/10.1.243
spec: [email protected]
- modules:
- cuda/10.1.243
spec: [email protected]
cmake:
buildable: false
externals:
- prefix: /opt/local/ppc64le/cmake/3.16.8
spec: [email protected]
- modules:
- cmake/3.16.8
spec: [email protected]The modules entry is only necessary on systems that require loading Modules (i.e. most DOE systems).
The buildable flag is useful to make sure Spack crashes if there is a path error,
rather than having a type-o and Spack rebuilding everything because cmake isn't found.
You can verify your environment is set up correctly by running spack graph or spack spec.
For example:
> spack graph kokkos +cuda
o kokkos
|\
o | cuda
/
o cmakeWithout the existing CUDA and CMake being identified in packages.yaml, a (subset!) of the output would be:
o kokkos
|\
| o cmake
| |\
| | | |\
| | | | | |\
| | | | | | | |\
| | | | | | | | | |\
| | | | | | | o | | | libarchive
| | | | | | | |\ \ \ \
| | | | | | | | | |\ \ \ \
| | | | | | | | | | | | |_|/
| | | | | | | | | | | |/| |
| | | | | | | | | | | | | o curl
| | |_|_|_|_|_|_|_|_|_|_|/|
| |/| | | |_|_|_|_|_|_|_|/
| | | | |/| | | | | | | |
| | | | o | | | | | | | | openssl
| |/| | | | | | | | | | |
| | | | | | | | | | o | | libxml2
| | |_|_|_|_|_|_|_|/| | |
| | | | | | | | | | |\ \ \
| o | | | | | | | | | | | | zlib
| / / / / / / / / / / / /
| o | | | | | | | | | | | xz
| / / / / / / / / / / /
| o | | | | | | | | | | rhash
| / / / / / / / / / /
| | | | o | | | | | | nettle
| | | | |\ \ \ \ \ \ \
| | | o | | | | | | | | libuv
| | | | o | | | | | | | autoconf
| | |_|/| | | | | | | |
| | | | |/ / / / / / /
| o | | | | | | | | | perl
| o | | | | | | | | | gdbm
| o | | | | | | | | | readlineSay you have a project "SuperScience" which needs to use Kokkos.
In your package.py file, you would generally include something like:
class SuperScience(CMakePackage):
...
depends_on("kokkos")Often projects want to tweak behavior when using certain features, e.g.
depends_on("kokkos+cuda", when="+cuda")if your project needs CUDA-specific logic to configure and build. This illustrates the general principle in Spack of "flowing-up". A user requests a feature in the final app:
> spack install superscience+cudaThis flows upstream to the Kokkos dependency, causing the kokkos+cuda variant to build.
The downstream app (SuperScience) tells the upstream app (Kokkos) how to build.
Because Kokkos is a performance portability library, it somewhat inverts this principle.
Kokkos "flows-down", telling your application how best to configure for performance.
Rather than a downstream app (SuperScience) telling the upstream (Kokkos) what variants to build,
a pre-built Kokkos should be telling the downstream app SuperScience what variants to use.
Kokkos works best when there is an "expert" configuration installed on your system.
Your build should simply request -DKokkos_ROOT=<BEST_KOKKOS_FOR_MY_SYSTEM> and configure appropriately based on the Kokkos it finds.
Kokkos has many, many build variants.
Where possible, projects should only depend on a general Kokkos, not specific variants.
We recommend instead adding for each system you build on a Kokkos configuration to your packages.yaml file (usually found in ~/.spack for specific users).
For a Xeon + Volta system, this could look like:
kokkos:
variants: +cuda +openmp +cuda_lambda +wrapper ^[email protected] cuda_arch=70
compiler: [[email protected]]which gives the "best" Kokkos configuration as CUDA+OpenMP optimized for a Volta 70 architecture using CUDA 10.1.
It also enables support for CUDA Lambdas.
The +wrapper option tells Kokkos to build with the special nvcc_wrapper (more below).
Note here that we use the built-in cuda_arch variant of Spack to specify the archicture.
For a Haswell system, we use
kokkos:
variants: +openmp std=14 target=haswell
compiler: [intel@18]which uses the built-in microarchitecture variants of Spack.
Consult the Spack documentation for more details of Spack microarchitectures
and CUDA architectures.
Spack does not currently provide an AMD GPU microarchitecture option.
If building for HIP or an AMD GPU, Kokkos provides an amd_gpu_arch similar to cuda_arch.
kokkos:
variants: +hip amd_gpu_arch=vega900Without an optimal default in your packages.yaml file, it is highly likely that the default Kokkos configuration you get will not be what you want.
For example, CUDA is not enabled by default (there is no easy logic to conditionally activate this for CUDA-enabled systems).
If you don't specify a CUDA build variant in a packages.yaml and you build your Kokkos-dependent project:
> spack install superscienceyou may end up just getting the default Kokkos (i.e. Serial).
Some examples are included in the config/yaml folder for common platforms.
Before running spack install <package> we recommend running spack spec <package> to confirm your dependency tree is correct.
For example, with Kokkos Kernels:
[email protected]%[email protected]~blas build_type=RelWithDebInfo ~cblas~complex_double~complex_float~cublas~cuda cuda_arch=none ~cusparse~diy+double execspace_cuda=auto execspace_openmp=auto execspace_serial=auto execspace_threads=auto ~float~lapack~lapacke+layoutleft~layoutright memspace_cudaspace=auto memspace_cudauvmspace=auto +memspace_hostspace~mkl+offset_int+offset_size_t~openmp+ordinal_int~ordinal_int64_t~serial~superlu arch=linux-rhel7-skylake_avx512
^[email protected]%[email protected]~doc+ncurses+openssl+ownlibs~qt arch=linux-rhel7-skylake_avx512
^[email protected]%[email protected]~aggressive_vectorization~amdavx~armv80~armv81~armv8_thunderx~armv8_tx2~bdw~bgq build_type=RelWithDebInfo ~carrizo~compiler_warnings+cuda cuda_arch=none +cuda_lambda~cuda_ldg_intrinsic~cuda_relocatable_device_code~cuda_uvm~debug~debug_bounds_check~debug_dualview_modify_check~deprecated_code~diy~epyc~examples~explicit_instantiation~fiji~gfx901~hpx~hpx_async_dispatch~hsw~hwloc~kaveri~kepler30~kepler32~kepler35~kepler37~knc~knl~maxwell50~maxwell52~maxwell53~memkind~numactl+openmp~pascal60~pascal61~power7~power8~power9+profiling~profiling_load_print~pthread~qthread~rocm~ryzen~serial~skx~snb std=14 ~tests~turing75~vega+volta70~volta72+wrapper~wsm arch=linux-rhel7-skylake_avx512
^[email protected]%[email protected] arch=linux-rhel7-skylake_avx512
^kokkos-nvcc-wrapper@old%[email protected] build_type=RelWithDebInfo +mpi arch=linux-rhel7-skylake_avx512
^[email protected]%[email protected]~cuda+cxx_exceptions fabrics=none ~java~legacylaunchers~memchecker patches=073477a76bba780c67c36e959cd3ee6910743e2735c7e76850ffba6791d498e4 ~pmi schedulers=none ~sqlite3~thread_multiple+vt arch=linux-rhel7-skylake_avx512The output can be very verbose, but we can verify the expected kokkos:
[email protected]%[email protected]~aggressive_vectorization~amdavx~armv80~armv81~armv8_thunderx~armv8_tx2~bdw~bgq build_type=RelWithDebInfo ~carrizo~compiler_warnings+cuda cuda_arch=none +cuda_lambda~cuda_ldg_intrinsic~cuda_relocatable_device_code~cuda_uvm~debug~debug_bounds_check~debug_dualview_modify_check~deprecated_code~diy~epyc~examples~explicit_instantiation~fiji~gfx901~hpx~hpx_async_dispatch~hsw~hwloc~kaveri~kepler30~kepler32~kepler35~kepler37~knc~knl~maxwell50~maxwell52~maxwell53~memkind~numactl+openmp~pascal60~pascal61~power7~power8~power9+profiling~profiling_load_print~pthread~qthread~rocm~ryzen~serial~skx~snb std=11 ~tests~turing75~vega+volta70~volta72+wrapper~wsm arch=linux-rhel7-skylake_avx512We see that we do have +volta70 and +wrapper, e.g.
The encouraged way to use Spack is with Spack environments (more details here). Rather than installing packages one-at-a-time, you add packages to an environment. After adding all packages, you concretize and install them all. Using environments, one can explicitly add a desired Kokkos for the environment, e.g.
> spack add kokkos +cuda +cuda_lambda +volta70
> spack add my_project +my_variant
> ...
> spack installAll packages within the environment will build against the CUDA-enabled Kokkos, even if they only request a default Kokkos.
Kokkos is a C++ project, but often builds for the CUDA backend.
This is particularly problematic with CMake. At this point, nvcc does not accept all the flags that normally get passed to a C++ compiler.
Kokkos provides nvcc_wrapper that identifies correctly as a C++ compiler to CMake and accepts C++ flags, but uses nvcc as the underlying compiler.
nvcc itself also uses an underlying host compiler, e.g. GCC.
In Spack, the underlying host compiler is specified as below, e.g.:
> spack install package %[email protected]This is still valid for Kokkos. To use the special wrapper for CUDA builds, request a desired compiler and simply add the +wrapper variant.
> spack install kokkos +cuda +wrapper %[email protected]Downstream projects depending on Kokkos need to override their compiler.
Kokkos provides the compiler in a kokkos_cxx variable,
which points to either nvcc_wrapper when needed or the regular compiler otherwise.
Spack projects already do this to use MPI compiler wrappers.
def cmake_args(self):
options = []
...
options.append("-DCMAKE_CXX_COMPILER=%s" % self.spec["kokkos"].kokkos_cxx)
...
return optionsNote: nvcc_wrapper works with the MPI compiler wrappers.
If building your project with MPI, do NOT set your compiler to nvcc_wrapper.
Instead set your compiler to mpicxx and nvcc_wrapper will be used under the hood.
def cmake_args(self):
options = []
...
options.append("-DCMAKE_CXX_COMPILER=%s" % self.spec["mpi"].mpicxx)
...
return optionsTo accomplish this, nvcc_wrapper must depend on MPI (even though it uses no MPI).
This has the unfortunate consequence that Kokkos CUDA projects not using MPI will implicitly depend on MPI anyway.
This behavior is necessary for now, but will hopefully be removed later.
When using environments, if MPI is not needed, you can remove the MPI dependency with:
> spack add kokkos-nvcc-wrapper ~mpiSpack has historically been much more suited to deployment of mature packages than active testing or developing.
However, recent features have improved support for development.
Future releases are likely to make this even easier and incorporate Git integration.
The most common commands will do a full build and install of the packages.
If doing development, you may wish to merely set up a build environment.
This allows you to modify the source and re-build.
In this case, you can stop after configuring.
Suppose you have Kokkos checkout in the folder kokkos-src:
> spack dev-build -d kokkos-src -u cmake kokkos@develop +wrapper +openmpThis sets up a development environment for you in kokkos-src which you can use (Bash example shown):
Note: Always specify develop as the version when doing dev-build, except in rare cases.
You are usually developing a feature branch that will merge into develop,
hence you are making a new develop branch.
> cd kokko-src
> source spack-build-env.txt
> cd spack-build
> makeBefore sourcing the Spack development environment, you may wish to save your current environment:
> declare -px > myenv.shWhen done with Spack, you can then restore your original environment:
> source myenv.sh