WIP: Use HPC for CI (#386)

Works on #415 

Added sync with Gitlab, now also for pull requests

---------

Co-authored-by: Robert Speck <[email protected]> and Thomas Baumann <[email protected]>

.github/workflows/ci_pipeline.yml

@@ -151,8 +151,6 @@ jobs:
 #        run: |
 #          pytest --continue-on-collection-errors -v --durations=0 pySDC/tests -m ${{ matrix.env }}
 
-
-
 # #      - name: Get and prepare artifacts
 # #        run: |
 # #          pipeline_id=$(curl --header "PRIVATE-TOKEN: ${{ secrets.GITLAB_SECRET_H }}" --silent "https://gitlab.hzdr.de/api/v4/projects/3525/repository/commits/${{ github.head_ref || github.ref_name }}" | jq '.last_pipeline.id')
@@ -196,7 +194,7 @@ jobs:
         run: |
           ls -artl cpu-test-artifacts
           cp cpu-test-artifacts/data_3.10/* data/.
-          python -m coverage combine cpu-test-artifacts/coverage_*_3.10.dat
+          python -m coverage combine cpu-test-artifacts/coverage_*.dat
           python -m coverage xml
           python -m coverage html
 

.github/workflows/gitlab_ci.yml

@@ -1,3 +1,5 @@
+---
+
 name: Mirror to Gitlab to trigger CI
 
 on:
@@ -37,7 +39,7 @@ jobs:
               echo 'Permissions granted'
               exit 0
             else
-              echo 'Not enough permissions'
+              echo 'Not enough permissions. Please ask a member of Parallel-in-Time to rerun the job.'
               exit 1
           fi
       - name: Pass if workflow from push or schedule
@@ -105,7 +107,7 @@ jobs:
       - name: Mirror and wait for Gitlab-CI
         uses: jakob-fritz/[email protected]
         env:
-          MODE: 'all' # Either 'mirror', 'get_status', 'get_artifact', or 'all'
+          MODE: 'all'  # Either 'mirror', 'get_status', 'get_artifact', or 'all'
           GITLAB_TOKEN: ${{ secrets.GITLAB_SECRET }}
           FORCE_PUSH: "true"
           GITLAB_HOSTNAME: "gitlab.jsc.fz-juelich.de"

.gitlab-ci.yml

@@ -1,9 +1,82 @@
+---
+
 stages:
   - test
   - benchmark
+  - execute
   - upload
 
-#test_kit:
+
+# job_juwels_compute:
+#   stage: execute
+#   variables:
+#     SCHEDULER_PARAMETERS: '--account=cstma --nodes=1 --partition=devel'
+#   tags:
+#     - juwels
+#     - jacamar
+#     - compute
+#     - slurm
+#   artifacts:
+#     paths:
+#       - test.file
+#   script:
+#     - echo $SYSTEMNAME
+#     - touch test.file
+#   after_script:
+#     - hostname
+#     - id
+
+
+variables:
+  JUWELS_ACCOUNT: "cstma"
+
+
+test_JUWELS:
+  stage: benchmark
+  rules:
+    - if: $CI_COMMIT_MESSAGE !~ /.*\[CI-no-benchmarks\]/
+  tags:
+    - jacamar
+    - juwels
+    - login
+    - shell
+  parallel:
+    matrix:
+      - SHELL_SCRIPT: ['benchmark', 'cupy']
+  artifacts:
+    when: always
+    paths:
+      - coverage_*.dat
+      - sbatch.err
+      - sbatch.out
+  before_script:
+    - mkdir -p benchmarks
+    # load the latest Python module (currently 3.11)
+    - module --force purge
+    - module load Stages/2024
+    - module load GCC
+    - module load OpenMPI
+    - module load FFTW
+    - module load mpi4py
+    - module load SciPy-Stack
+    - module load CuPy
+    - pip install -e .
+    - pip install pytest-benchmark coverage
+  script:
+    # - touch benchmarks/output.json
+    - echo $SYSTEMNAME
+    - sbatch --wait etc/juwels_${SHELL_SCRIPT}.sh
+    - touch .coverage.empty
+    - python -m coverage combine
+    - mv .coverage coverage_${SHELL_SCRIPT}.dat
+  after_script:
+    - echo "Following Errors occured:"
+    - cat sbatch.err
+    - echo "Following was written to stdout:"
+    - cat sbatch.out
+
+
+# test_kit:
 #  image: rcaspart/micromamba-cuda
 #  stage: benchmark
 #  variables:
@@ -34,7 +107,7 @@ stages:
 #    - mv data data_${PYTHON}
 
 
-#test_kit_bare:
+# test_kit_bare:
 #  stage: test
 #  rules:
 #    - if: $CI_COMMIT_MESSAGE !~ /.*\[CI-no-GPU\].*/
@@ -64,6 +137,9 @@ stages:
 benchmark:
   image: mambaorg/micromamba
   stage: benchmark
+  when: manual
+  tags:
+    - docker
   rules:
     - if: $CI_COMMIT_MESSAGE !~ /.*\[CI-no-benchmarks\]/
   artifacts:
@@ -79,7 +155,7 @@ benchmark:
     - mkdir -p benchmarks
     - pytest --continue-on-collection-errors -v pySDC/tests -m "benchmark" --benchmark-json=benchmarks/output.json
 
-#benchmark_kit:
+# benchmark_kit:
 #  image: rcaspart/micromamba
 #  stage: benchmark
 #  variables:
@@ -104,7 +180,7 @@ benchmark:
 #    - pytest --continue-on-collection-errors -v pySDC/tests -m "benchmark" --benchmark-json=benchmarks/output.json
 
 
-#bundle:
+# bundle:
 #  image: mambaorg/micromamba
 #  stage: upload
 #  artifacts:

CODE_OF_CONDUCT.md

@@ -61,7 +61,7 @@ representative at an online or offline event.
 ## Enforcement
 
 Instances of abusive, harassing, or otherwise unacceptable behavior may be
-reported to the community leaders responsible for enforcement 
+reported to the community leaders responsible for enforcement
 [here](mailto:[email protected]).
 All complaints will be reviewed and investigated promptly and fairly.
 
@@ -118,15 +118,15 @@ the community.
 
 This Code of Conduct is adapted from the [Contributor Covenant][homepage],
 version 2.0, available at
-https://www.contributor-covenant.org/version/2/0/code_of_conduct.html.
+<https://www.contributor-covenant.org/version/2/0/code_of_conduct.html>.
 
 Community Impact Guidelines were inspired by [Mozilla's code of conduct
-enforcement ladder](https://github.com/mozilla/diversity).
+enforcement ladder](<https://github.com/mozilla/diversity>).
 
-[homepage]: https://www.contributor-covenant.org
+[homepage]: <https://www.contributor-covenant.org>
 
 For answers to common questions about this code of conduct, see the FAQ at
-https://www.contributor-covenant.org/faq. Translations are available at
-https://www.contributor-covenant.org/translations.
+<https://www.contributor-covenant.org/faq>. Translations are available at
+<https://www.contributor-covenant.org/translations>.
 
-:arrow_left: [Back to main page](./README.md)
+:arrow_left: [Back to main page](./README.md)

docs/contrib/02_continuous_integration.md

@@ -103,6 +103,37 @@ pytest -v pySDC/tests
 > pytest -v pySDC/tests/test_nodes.py  # only test nodes generation
 > ```
 
+## Running CI on HPC from pull requests
+
+By syncing the GitHub repository to a certain Gitlab instance, CI-Jobs can be run on HPC machines. This can be helpful for benchmarks or when running on accelerators that are not available as GitHub runners.
+
+For security and accounting reasons, a few extra steps are needed in order to run the contents of a pull request on HPC:
+
+- The pull request needs to have the tag "gitlab-mirror" assigned to it.
+- A person with write-permission for the Parallel-in-Time pySDC repository needs to trigger the workflow. Ask for someone with the required permissions to rerun the workflow if needed.
+- The workflow checks if the code can be merged. If this is not the case, the code is not mirrored and the workflow fails. In this case, please merge upstream changes, fix all conflicts, and rerun the workflow.
+
+> :bell: Note that direct pushes to Parallel-in-Time/pySDC will always trigger the HPC pipeline on Gitlab
+
+Regardless of why the Gitlab pipeline was triggered, the following holds true:
+
+- The return-state from Gitlab is transmitted to GitHub (Success/Failure) leading to the same result in GitHub
+- Logs from Gitlab are also transferred. The full logs of all jobs can be read from within GitHub. For better overview, these are folded, so unfolding is needed before reading.
+- Artifacts from Gitlab jobs are also transferred back to GitHub
+- Information, such as coverage is transferred to GitHub, but not yet merged across multiple GitHub workflows. Therefore, there is no complete summary of e.g. coverage-reports across all jobs in all workflows.
+
+> :warning: The coverage report from the HPC tests is not yet merged with other reports. The test coverage will not show up on the respective website or in the badge. We are working on this.
+
+### HPC test environments
+
+In order to run tests on GPUs, please use the pytest marker `cupy`.
+
+If you want to create a new HPC test environment, the following steps need to be completed:
+
+- Create a new slurm job-script in `etc/juwels_*.sh`. The name and location of the file is important.
+- Adapt `.gitlab-ci.yml` to include the new job-script. For this, add a name in the job "test_JUWELS" in the section `parallel: matrix: SHELL_SCRIPT`. The name there must match the name of the newly created file.
+As a starting point it is recommended to copy and adapt an existing file (e.g. `etc/juwels_cupy.sh`).
+
 ## Code coverage
 
 This stage allows to checks how much of the `pySDC` code is tested by the previous stage. It is based on the [coverage](https://pypi.org/project/coverage/) library and currently applied to the following directories :

etc/juwels_benchmark.sh

@@ -0,0 +1,9 @@
+#!/bin/bash -x
+#SBATCH --account=cstma
+#SBATCH --nodes=1
+#SBATCH --time=00:10:00
+#SBATCH --partition=devel
+#SBATCH --output=sbatch.out
+#SBATCH --error=sbatch.err
+
+srun python -m pytest --continue-on-collection-errors -v pySDC/tests -m "benchmark" --benchmark-json=benchmarks.json

etc/juwels_cupy.sh

@@ -0,0 +1,9 @@
+#!/bin/bash -x
+#SBATCH --account=cstma
+#SBATCH --nodes=1
+#SBATCH --time=00:10:00
+#SBATCH --partition=develgpus
+#SBATCH --output=sbatch.out
+#SBATCH --error=sbatch.err
+
+srun python -m coverage run -m pytest --continue-on-collection-errors -v pySDC/tests -m "cupy"

pySDC/implementations/problem_classes/AllenCahn_1D_FD.py

@@ -762,9 +762,7 @@ def eval_f(self, u, t):
         f = self.dtype_f(self.init)
         f.impl[:] = self.A.dot(u)
         f.expl[:] = (
-            -2.0 / self.eps**2 * u * (1.0 - u) * (1.0 - 2.0 * u)
-            - 6.0 * self.dw * u * (1.0 - u)
-            + 0.0 / self.eps**2 * u
+            -2.0 / self.eps**2 * u * (1.0 - u) * (1.0 - 2.0 * u) - 6.0 * self.dw * u * (1.0 - u) + 0.0 / self.eps**2 * u
         )
         self.work_counters['rhs']()
         return f
@@ -849,9 +847,7 @@ def eval_f(self, u, t):
         f = self.dtype_f(self.init)
         f.comp1[:] = self.A.dot(u)
         f.comp2[:] = (
-            -2.0 / self.eps**2 * u * (1.0 - u) * (1.0 - 2.0 * u)
-            - 6.0 * self.dw * u * (1.0 - u)
-            + 0.0 / self.eps**2 * u
+            -2.0 / self.eps**2 * u * (1.0 - u) * (1.0 - 2.0 * u) - 6.0 * self.dw * u * (1.0 - u) + 0.0 / self.eps**2 * u
         )
         self.work_counters['rhs']()
         return f

pySDC/implementations/problem_classes/Battery.py

@@ -329,6 +329,7 @@ class battery(battery_n_capacitors):
     ----
     This class has the same attributes as the class it inherits from.
     """
+
     dtype_f = imex_mesh
 
     def __init__(self, ncapacitors=1, Vs=5.0, Rs=0.5, C=None, R=1.0, L=1.0, alpha=1.2, V_ref=None):
@@ -455,6 +456,7 @@ class battery_implicit(battery):
     work_counters : WorkCounter
         Counts different things, here: Number of Newton iterations is counted.
     """
+
     dtype_f = mesh
 
     def __init__(

pySDC/implementations/problem_classes/GrayScott_2D_PETSc_periodic.py

@@ -132,9 +132,7 @@ def formJacobian(self, snes, X, J, P):
                 row.field = 1
                 col.field = 1
                 val = 1.0 - self.factor * (
-                    self.prob.Dv * (-2.0 / self.dx**2 - 2.0 / self.dy**2)
-                    + 2.0 * x[i, j, 0] * x[i, j, 1]
-                    - self.prob.B
+                    self.prob.Dv * (-2.0 / self.dx**2 - 2.0 / self.dy**2) + 2.0 * x[i, j, 0] * x[i, j, 1] - self.prob.B
                 )
                 P.setValueStencil(row, col, val)
                 row.field = 1

pySDC/implementations/problem_classes/GrayScott_MPIFFT.py

@@ -590,6 +590,7 @@ class grayscott_mi_linear(grayscott_imex_linear):
     The problem in this class will be treated in a *multi-implicit* way for time-stepping, i.e., for the system containing
     the diffusion part will be solved by FFT, and for the linear part a Newton solver is used.
     """
+
     dtype_f = comp2_mesh
 
     def __init__(

pySDC/implementations/problem_classes/HeatEquation_ND_FD_CuPy.py

@@ -302,6 +302,7 @@ class heatNd_unforced(heatNd_forced):
 
     The implementation is this class uses the ``CuPy`` package in order to make ``pySDC`` available for GPUs.
     """
+
     dtype_f = cupy_mesh
 
     def eval_f(self, u, t):

pySDC/implementations/problem_classes/LogisticEquation.py

@@ -34,6 +34,7 @@ class logistics_equation(ptype):
     stop_at_nan : bool, optional
         Indicates if the Newton solver stops when nan values arise.
     """
+
     dtype_u = mesh
     dtype_f = mesh
 

pySDC/implementations/problem_classes/PenningTrap_3D.py

@@ -122,10 +122,7 @@ def fast_interactions(N, pos, sig, q):
 
             for j in range(N):
                 dist2 = (
-                    (pos[0, i] - pos[0, j]) ** 2
-                    + (pos[1, i] - pos[1, j]) ** 2
-                    + (pos[2, i] - pos[2, j]) ** 2
-                    + sig**2
+                    (pos[0, i] - pos[0, j]) ** 2 + (pos[1, i] - pos[1, j]) ** 2 + (pos[2, i] - pos[2, j]) ** 2 + sig**2
                 )
                 contrib += q[j] * (pos[:, i] - pos[:, j]) / dist2**1.5
 

pySDC/implementations/problem_classes/generic_ND_FD.py

@@ -76,6 +76,7 @@ class GenericNDimFinDiff(ptype):
     xvalues : np.1darray
         Values of spatial grid.
     """
+
     dtype_u = mesh
     dtype_f = mesh
 

pySDC/projects/Resilience/work_precision.py

@@ -1109,9 +1109,11 @@ def get_configs(mode, problem):
         configurations[-1] = {
             'strategies': [
                 ERKStrategy(useMPI=True),
-                ARKStrategy(useMPI=True)
-                if problem.__name__ in ['run_Schroedinger', 'run_AC']
-                else ESDIRKStrategy(useMPI=True),
+                (
+                    ARKStrategy(useMPI=True)
+                    if problem.__name__ in ['run_Schroedinger', 'run_AC']
+                    else ESDIRKStrategy(useMPI=True)
+                ),
             ],
             'num_procs': 1,
         }

pySDC/projects/parallelSDC/GeneralizedFisher_1D_FD_implicit_Jac.py

@@ -19,9 +19,7 @@ def eval_jacobian(self, u):
         """
 
         # noinspection PyTypeChecker
-        dfdu = self.A[1:-1, 1:-1] + sp.diags(
-            self.lambda0**2 - self.lambda0**2 * (self.nu + 1) * u**self.nu, offsets=0
-        )
+        dfdu = self.A[1:-1, 1:-1] + sp.diags(self.lambda0**2 - self.lambda0**2 * (self.nu + 1) * u**self.nu, offsets=0)
 
         return dfdu