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The redistributions use alltoall communication and are expensive. By reducing these, I could see slightly better parallel efficiency in Rayleigh-Benard.

@pancetta pancetta merged commit 5fccc9f into Parallel-in-Time:master Oct 9, 2024
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brownbaerchen added a commit to brownbaerchen/pySDC that referenced this pull request Oct 26, 2025
#!!!!!!!!!! WARNING: RUFF FAILED !!!!!!!!!!: 

#pySDC/projects/GPU/analysis_scripts/compare_RBC3D.py:494:9: E722 Do not use bare `except`
#    |
Parallel-in-Time#492 |                 k[_s > 1e-16], _s[_s > 1e-16], color=last_line.get_color(), ls=last_line.get_linestyle(), label=label
Parallel-in-Time#493 |             )
Parallel-in-Time#494 |         except:
#    |         ^^^^^^ E722
Parallel-in-Time#495 |             pass
#    |
#
#pySDC/projects/GPU/analysis_scripts/compare_RBC3D.py:593:5: F841 Local variable `Delta_Nu` is assigned to but never used
#    |
Parallel-in-Time#591 |     t = data['t']
Parallel-in-Time#592 |     avg_Nu = np.array([np.mean(Nu[40 : 40 + i + 1]) for i in range(len(Nu[40:]))])
Parallel-in-Time#593 |     Delta_Nu = np.array([abs(avg_Nu[i + 1] - avg_Nu[i]) for i in range(len(avg_Nu) - 1)])
#    |     ^^^^^^^^ F841
Parallel-in-Time#594 |     # ax.plot(data['t'][40:-1], Delta_Nu / avg_Nu[:-1])
Parallel-in-Time#595 |     # ax.plot(data['t'], np.abs(avg_Nu - avg_Nu[-1]) / avg_Nu[-1])
#    |
#    = help: Remove assignment to unused variable `Delta_Nu`:
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2 participants