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Drop the high-memory label from WeakConvergence3 - #4063

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Drop the high-memory label from WeakConvergence3#4063
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ChrisRackauckas-Claude:unpin-weakconvergence3

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Please ignore this PR until it has been reviewed by @ChrisRackauckas.

WeakConvergence3 (srk_weak_final_non_diagonal.jl) is pinned to a high-memory runner label that no runner in the SciML fleet carries, so the job is never scheduled — it sits queued for 24 h, is expired by GitHub, and holds the whole Sublibrary CI run open behind it (#4030).

The label is not needed. This group's studies pass a reducing output_func to EnsembleProblem and an expected_value to test_convergence:

ensemble_prob = EnsembleProblem(prob;
    output_func = (sol, ctx) -> (h2(sol.u[end][1]), false), prob_func = prob_func)

so each trajectory contributes 8 bytes to the retained result rather than a whole solution object — measured at exactly 8.0 B/trajectory. It is a slow group, not a memory-hungry one.

Measured

Run to completion on an 8 vCPU box, GROUP=WeakConvergence3, JULIA_NUM_THREADS=8:

peak RSS 2.56 GiB
wall     1:26:23
         Testing StochasticDiffEqWeak tests passed

2.56 GiB fits the standard self-hosted pool with room to spare, so point it there. The 300-minute timeout is left alone — at 86 minutes the group genuinely is slow, and that is the real reason it is expensive.

Scope

Only this group. The other four high-memory groups in this file are being measured separately and are not touched here — three of them did not finish inside the two-hour budget I first gave them, so I have no completed figure for those yet and will not unpin on a projection. I have already been wrong once about which groups a memory fix rescues (SROCKC2WeakConvergence turned out unaffected by the test_convergence retention fix in #4056), so these are going one at a time on completed runs only.


Links

🤖 Generated with Claude Code

https://claude.ai/code/session_013WW2jGeoWZVZu7AiscUNSz

WeakConvergence3 (srk_weak_final_non_diagonal.jl) is pinned to a `high-memory`
runner label that no runner in the SciML fleet carries, so the job is never
scheduled: it sits queued for 24 h and is expired by GitHub, holding the whole
Sublibrary CI run open behind it (SciML#4030).

The label is not needed. The group's studies pass a reducing `output_func` to
`EnsembleProblem` and an `expected_value` to `test_convergence`, so each trajectory
contributes 8 bytes to the retained result rather than a whole solution object.
Running the group to completion on an 8 vCPU box:

  peak RSS 2.56 GiB, wall 1:26:23, tests passed

That fits the standard self-hosted pool with room to spare, so point it there. The
300-minute timeout is left alone — at 86 minutes the group is slow, which is the
actual reason it is expensive, and that has nothing to do with memory.

Only this group is changed. The other four `high-memory` groups in this file are
being measured separately and are not touched here; three of them did not finish
inside the two-hour budget I first gave them, so I have no completed figure for
them yet.

Co-Authored-By: Chris Rackauckas <accounts@chrisrackauckas.com>
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_013WW2jGeoWZVZu7AiscUNSz
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2 participants