Continuous Integration (CI) Testing Maintenance Tasks

In Continuous Integration (CI) Testing, we described RAJA CI workflows. This section describes how to perform common RAJA CI testing maintenance tasks.

GitLab CI Tasks

The tasks in this section apply to GitLab CI testing on Livermore Computing (LC) platforms.

Changing build and test configurations

The build for each test we run is defined by a Spack spec in one of two places, depending on whether it is shared with other projects or it is specific to RAJA. The details are described in Launching CI pipelines (step 2).

Remove a configuration

To remove a RAJA-specific test configuration, simply delete the entry for it in the RAJA/.gitlab/<MACHINE>-build-and-test-extra.yml file where it is defined.

To remove a shared configuration, it must be removed from the appropriate <MACHINE>-build-and-test.yml file in the RADIUSS Shared CI project. Create a branch there, remove the job entry, and create a pull request.

Add a configuration

To add a RAJA-specific test configuration, add the entry for it to the RAJA/.gitlab/<MACHINE>-build-and-test-extra.yml file, where MACHINE is the name of the LC platform where it will be run. When adding a RAJA-specific test configuration, it is important to note two items that must be set properly:

  • the unique job label, which identifies it in the machine configuration file and also on a web page for a GitLab CI pipeline
  • the build Spack spec, which identifies the compiler and version, compiler flags, build options, etc.

For example, an entry for a build using the clang 12.0.1 compiler with CUDA 11.5.0 on the LC lassen machine would be something like this:

clang_12_0_1_cuda_11_5_0:
  variables:
    SPEC: " ~shared +openmp +tests +cuda cuda_arch=70 %clang@12.0.1 ^cuda@11.5.0"
  extends: .build_and_test_on_lassen

Here, we enable OpenMP and CUDA, both of which must be enabled to use them, and specify the CUDA target architecture ‘sm_70’.

To add a shared configuration, it must be added to the appropriate <MACHINE>-build-and-test.yml file in the RADIUSS Shared CI project. Create a branch there, add the job entry, and create a pull request.

Modifying a configuration

To change an existing configuration, change the relevant information in the configuration, such variant(s) or target compute architecture, in the appropriate RAJA/.gitlab/<MACHINE>-build-and-test-extra.yml file. Make sure to also modify the job label, so it is descriptive of the configuration.

To modify a shared configuration, it must be changed in the appropriate <MACHINE>-build-and-test.yml file in the RADIUSS Shared CI project. Create a branch there, modify the job entry, and create a pull request.

Important

Build spec information used in RAJA GitLab CI pipelines must exist in the compilers.yaml file and/or packages.yaml file for the appropriate system type in the RADIUSS Spack Configs repo.

If the desired entry is not there, but exists in a newer version of the RADIUSS Spack Configs project, update the RAJA submodule to use the newer version. If the information does not exist in any version of the RADIUSS Spack Configs project, create a branch there, add the needed spec info, and create a pull request. Then, when that PR is merged, update the RAJA submodule to the new version.

Changing run parameters

The parameters for each system/scheduler on which we run GitLab CI for RAJA, such as job time limits, resource allocations, etc. are defined in the RAJA/.gitlab/custom-jobs-and-variables.yml file. This information can remain as is, for the most part, and should not be changed unless absolutely necessary.

Sometimes a particular job will take longer to build and run than the default allotted time for jobs on a machine. In this case, the time for the job can be adjusted in the job entry in the associated RAJA/.gitlab/<MACHINE>-build-and-test-extra.yml file. For example:

gcc_8_1_0:
variables:
  SPEC: " ${PROJECT_RUBY_VARIANTS} %gcc@8.1.0 ${PROJECT_RUBY_DEPS}"
  RUBY_BUILD_AND_TEST_JOB_ALLOC: "--time=60 --nodes=1"
extends: .build_and_test_on_ruby

This example sets the build and test allocation time to 60 minutes and the the run resource to one node.

Allowing failures

Sometimes a shared job configuration is known to fail for RAJA. To allow the job to fail without the CI check associated with it failing, we can annotate the job for this. For example:

ibm_clang_9_0_0:
  variables:
    SPEC: " ${PROJECT_LASSEN_VARIANTS} %clang@ibm.9.0.0 ${PROJECT_LASSEN_DEPS}"
  extends: .build_and_test_on_lassen
  allow_failure: true

Important

When a shared job needs to be modified for RAJA specifically, we call that “overriding”: The job label must be kept the same as in the <MACHINE>-build-and-test.yml file in the RADIUSS Shared CI, and the job implementation can be adapted. If you override a shared job, please add a comment to describe the change.

Azure CI Tasks

The tasks in this section apply to RAJA Azure Pipelines CI.

Changing Builds/Container Images

The builds we run in Azure are defined in the RAJA/azure-pipelines.yml file.

Linux/Docker

To update or add a new compiler / job to Azure CI we need to edit both azure-pipelines.yml and Dockerfile.

If we want to add a new Azure pipeline to build with compilerX, then in azure-pipelines.yml we can add the job like so:

-job: Docker
  ...
  strategy:
    matrix:
      ...
      compilerX:
        docker_target: compilerX

Here, compilerX: defines the name of a job in Azure. docker_target: compilerX defines a variable docker_target, which is used to determine what part of the Dockerfile to run.

In the Dockerfile we will want to add our section that defines the commands for the compilerX job.:

FROM ghcr.io/rse-ops/compilerX-ubuntu-20.04:compilerX-XXX AS compilerX
ENV GTEST_COLOR=1
COPY . /home/raja/workspace
WORKDIR /home/raja/workspace/build
RUN cmake -DCMAKE_CXX_COMPILER=compilerX ... && \
    make -j 6 &&\
    ctest -T test --output-on-failure

Each of our docker builds is built up on a base image maintained by RSE-Ops, a table of available base containers can be found here. We are also able to add target names to each build with AS .... This target name correlates to the docker_target: ... defined in azure-pipelines.yml.

The base containers are shared across multiple projects and are regularly rebuilt. If bugs are fixed in the base containers the changes will be automatically propagated to all projects using them in their Docker builds.

Check here for a list of all currently available RSE-Ops containers. Please see the RSE-Ops Containers Project on GitHub to get new containers built that aren’t yet available.

Windows / MacOS

We run our Windows / MacOS builds directly on the Azure virtual machine instances. In order to update the Windows / MacOS instance we can change the pool under -job: Windows or -job: Mac:

-job: Windows
  ...
  pool:
    vmImage: 'windows-2019'
  ...
-job: Mac
  ...
  pool:
    vmImage: 'macOS-latest'

Changing Build/Run Parameters

Linux/Docker

We can edit the build and run configurations of each docker build, in the RUN command. Such as adding CMake options or changing the parallel build value of make -j N for adjusting throughput.

Each base image is built using spack. For the most part the container environments are set up to run our CMake and build commands out of the box. However, there are a few exceptions where we need to spack load specific modules into the path.

  • Clang requires us to load LLVM for OpenMP runtime libraries.:

    . /opt/spack/share/spack/setup-env.sh && spack load llvm

    CUDA for the cuda runtime.:

    . /opt/spack/share/spack/setup-env.sh && spack load cuda

    HIP for the hip runtime and llvm-amdgpu runtime libraries.:

    . /opt/spack/share/spack/setup-env.sh && spack load hip llvm-amdgpu

    SYCL requires us to run setupvars.sh:

    source /opt/view/setvars.sh

Windows / MacOS

Windows and MacOS build / run parameters can be configured directly in azure-pipelines.yml. CMake options can be configured with CMAKE_EXTRA_FLAGS for each job. The -j value can also be edited directly in the Azure script definitions for each job.

The commands executed to configure, build, and test RAJA for each pipeline in Azure are located in the RAJA/Dockerfile file. Each pipeline section begins with a line that ends with AS ... where the ellipses in the name of a build-test pipeline. The name label matches an entry in the Docker test matrix in the RAJA/azure-pipelines.yml file mentioned above.

RAJA Performance Suite CI Tasks

The RAJA Performance Suite project CI testing processes, directory/file structure, and dependencies are nearly identical to that for RAJA, which is described in Continuous Integration (CI) Testing. Specifically,

The main difference is that for GitLab CI, is that the Performance Suite uses the RAJA submodules for uberenv and radiuss-spack-configs located in the RAJA submodule to avoid redundant submodules. This is reflected in the RAJAPerf/.uberenv_config.json file which point at the relevant RAJA submodule locations.

Apart from this minor difference, all CI maintenance and development tasks for the RAJA Performance Suite follow the guidance in Continuous Integration (CI) Testing Maintenance Tasks.