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P3M benchmark randomly fails CI #2924
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This is a recurring error: espresso/maintainer/benchmarks/p3m.py Line 167 in b7812cb
With error message: Exception: calc_long_range_energies failed: ERROR: number of cells 1 is smaller than minimum 8 (interaction range too large or min_num_cells too large) in function void dd_create_cell_grid() . It happens at random. The P3M benchmark script is MPI-capable, but its test currently runs without MPI. I never had any issue running this benchmark without MPI, even with a larger number of particles.
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Either the p3m tuning doesnot respect box_l/2 as maximum real space cutoff |
I'm looking into this, I think there is a bug in the tuning. |
I can't reproduce the bug locally nor locally in the docker container, but I can reproduce it on coyote8 in the docker container (after 175 tries). I'll try again while printing the random seed and check if it's deterministic. |
Please don't spend any more time on this, I will fix it, I know what the error is. |
Actually, the maximum safe skin might be available from s.cell_system.get_state()["max_skin"] |
Did this re-occur, since the adjust_max_skin was added in the tune_skin() call? |
Hasn't yet in CI. But I was able to trigger a similar error today on coyote8 after 493 retries:
The last 3 lines changed. The error now throws in |
So that means that tune_skin() doesn't chose the maximum permissible skin for adjust_max_skin=True correctly?
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There is a global 'max_skin'
domain_decomposition.cpp: max_skin = min_cell_size - max_cut;
which is set to sth different than in tune_skin()
double const max_permissible_skin = 0.5 * min_local_box_l - max_cut;
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The first one is correct, the second one isn't. You should also maybe have a look at #3053, which clarifies some of these things. |
I've just merged #3053 locally in python and was able to get the same error message, plus a new one:
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master is still failing: |
even after merging #3132, it's still possible to get the p3m benchmark to fail on coyote7 after 200 trials: |
Out of ideas. De-milestoning. |
We might as well disable this test in CI. We already know this benchmark cannot be used due to the non-deterministic nature of the P3M tuning function. It also fails due to the other, less frequent bug reported above. We can re-enable the test once the tuning function gets re-implemented. |
I agree do disable it now. We could also use fixed parameters to avoid the
problem.
…On Thu, Nov 28, 2019, 23:39 Jean-Noël Grad ***@***.***> wrote:
We might as well disable this test in CI. We already know this benchmark
cannot be used due to the non-deterministic nature of the P3M tuning
function. It also fails due to the other, less frequent bug reported above
<#2924 (comment)>.
We can re-enable the test once the tuning function gets re-implemented.
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https://gitlab.icp.uni-stuttgart.de/espressomd/espresso/pipelines/7734
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