datadeps: cache hierarchical schedules; fix similar; fix JuMPExt import

bench/datadeps_schedulers/Project.toml

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+[deps]
+CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
+Dagger = "d58978e5-989f-55fb-8d15-ea34adc7bf54"
+HiGHS = "87dc4568-4c63-4d18-b0c0-bb2238e4078b"
+JuMP = "4076af6c-e467-56ae-b986-b466b2749572"

bench/datadeps_schedulers/TUOLUMNE_README.md

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+# Tuolumne MI300a one-shot benchmark
+
+One Julia session produces every number the paper needs for Tuolumne coverage:
+single-node (M1), 2-node (M2), and 4-node (M4) MI300a APU regimes, for two
+workloads × three tile counts × five schedulers × seven seeds, plus a post-hoc
+optimality-gap pass and a schedule-cache demonstration.
+
+You run it **once**. There are no re-runs — validate with `--smoke` first.
+
+---
+
+## 1. Prerequisites (do these on a login node, with network)
+
+1. **Check out the paper branch** at the pinned commit. The script verifies at
+   startup that these four scheduler commits are in `HEAD` (matched by subject,
+   so a rehash is fine) and aborts with a clear message if any is missing:
+   - "stochastic reconstruction for IteratedGreedy"
+   - "faithful Ruiz-Stützle acceptance …"
+   - "Orsila SA tuning …"
+   - "producer-finish term in greedy …"
+   plus the MILP-objective instrumentation (`JuMPExt.LAST_MILP_SOLVE`).
+
+2. **ROCm + AMDGPU.jl.** ROCm ≥ 6.0 (MI300a needs a recent ROCm; match what your
+   AMDGPU.jl build expects — `AMDGPU.versioninfo()` should show your APUs). The
+   script uses `AMDGPU` as the GPU backend and Dagger's ROCExt.
+
+3. **Instantiate the self-contained env** (developer-mode Dagger from this repo):
+   ```bash
+   julia --project=bench/datadeps_schedulers/tuolumne_env \
+         -e 'using Pkg; Pkg.develop(path="."); Pkg.instantiate()'
+   ```
+   After this, launch with **no** `--project` — the script activates the env
+   itself.
+
+---
+
+## 2. Recommended allocation
+
+- **4 MI300a nodes**, **8 hours** wall-time (≈6 h expected; the margin covers
+  cold JIT and the 60 s IG/SA/MILP caps that dominate the large-K cells).
+- Launch mechanism is **your call** — Flux Framework, Slurm, whatever Tuolumne
+  uses. The script does **not** spawn workers; it consumes whatever
+  `Dagger.all_processors()` reports and groups them into nodes by hostname, then
+  scopes M1/M2/M4 to the first 1/2/4 nodes. Start Julia with one Dagger worker
+  per node (each with all local CPU threads + the node's APU GPU visible), added
+  through Dagger's DistributedNext so Dagger can see them.
+- If fewer than 4 nodes are available, the regimes that don't fit are skipped and
+  logged — M1 still runs on a single node.
+
+---
+
+## 3. Run — two commands, in order
+
+**a) Smoke test (~30–60 s) — validate the stack before committing the full run:**
+```bash
+julia bench/datadeps_schedulers/tuolumne_oneshot.jl --smoke
+```
+It prints the detected topology (nodes / procs / APU GPUs), runs one cholesky
+nt=2 cell with RR and Greedy end-to-end, and ends with a single line:
+`SMOKE: PASS` or `SMOKE: FAIL`. **Do not proceed unless it PASSes and reports at
+least one APU GPU.**
+
+**b) Full grid (~6–8 h):**
+```bash
+julia bench/datadeps_schedulers/tuolumne_oneshot.jl
+```
+Progress prints per config/cell so you can see forward motion. Partial CSVs are
+flushed every 5 minutes, so a wall-time expiry won't lose completed work. Any
+single cell that fails (correctness residual over threshold, MILP exception,
+worker crash) is caught, logged, marked in the CSV, and the run continues.
+
+---
+
+## 4. Output (lands in your invocation directory)
+
+| File | Contents |
+|---|---|
+| `tuolumne_regime_m1_1node.csv` | M1 per-cell: wall_ms, aot_ms, residual, n_tasks, milp_status, milp_obj, bs, n_nodes |
+| `tuolumne_regime_m2_2node.csv` | M2 per-cell (same columns) |
+| `tuolumne_regime_m4_4node.csv` | M4 per-cell |
+| `tuolumne_medians.csv` | per-cell medians + stddev over 7 seeds (feeds Table 1) |
+| `tuolumne_optgap.csv` | AOT-only Greedy/IG/SA `cost_of_schedule` vs MILP-optimal, with ratio (headline optimality-gap) |
+| `tuolumne_cache.csv` | schedule-cache demo: `aot_ms_first_call` vs `aot_ms_cache_hit` per regime |
+| `tuolumne_run_summary.txt` | git SHA, launch/elapsed times, failure count, flagged cells, full log |
+
+Notes:
+- **bs**: 4096 for nt ∈ {2,4}; nt=8 tries 4096 first (MI300a's 128 GB unified
+  HBM3 may fit it) and falls back to 2048 on OOM — the actual bs is recorded per
+  cell and logged.
+- **optgap**: only cells where MILP proved `OPTIMAL` (small K) contribute; that's
+  by design — a time-limited MILP is not a valid lower bound.
+- **cache**: `aot_ms_cache_hit` should be ≈0 (structural-equivalence lookup). If
+  the two columns are similar, the cache isn't hitting on your Julia version —
+  flag it to us.
+
+---
+
+## 5. What to send back
+
+All seven files above. If the smoke test failed, send `tuolumne_run_summary.txt`
+(or the console output) and your `AMDGPU.versioninfo()` / ROCm version so we can
+diagnose the stack before you spend the allocation.