perf(prover): device-resident rounds 2-4 and fused NTT for GPU continuations

[ColoCarletti]

Second GPU optimization round on top of #863: ethrex 10tx continuations on RTX 5090 go from 12.01s to 9.75s (-18.8%, 8 ABBA pairs, sd 1.3%). Proofs are unchanged and cross-verify against the previous prover.

• R2 composition parts, their Merkle tree, R3 OOD evals and R4 openings now read the resident device buffer instead of round-tripping ~200MB per table through the host.
• FRI keeps each layer's evaluations on device; the query phase gathers only the ~219 opened positions per layer.
• The multiplicity subset tree of preprocessed tables stays resident (no per-epoch node download and host rebuild).
• Kernels: first 8 NTT levels fused in shared memory on the row-major path, small Merkle levels built in one launch, batch-inverse total inverted on device, big trace uploads staged through pinned memory.
• LogUp aux build no longer pays a host transpose the GPU path discards.

[ColoCarletti]

/bench-gpu

[github-actions]

GPU Benchmark (ABBA) — ac3dee9e36 vs main (14 pairs)

_{RTX 5090 · Vast.ai datacenter @ $0.8013888888888888/hr · prover/cuda · ethrex 100tx continuations · drift-free A/B/B/A}

=== ABBA paired result  (improvement: - = PR faster) ===
  pairs: 14   mean A (PR): 60.956s   mean B (base): 74.924s

  [parametric] paired-t   mean -18.64%   sd 0.72%   se 0.19%
               95% CI: [-19.06%, -18.22%]   (t df=13 = 2.16)
  [robust]     median -18.48%   Wilcoxon W+=0 W-=105  p(exact)=0.0001  (z=-3.26)

  --- server stability (this run; compare across servers) ---
  run-to-run jitter:    A CV 1.02%   B CV 0.62%        (lower = steadier)
  within-session drift: -0.15% over the run, 1st->2nd half -0.13%
    (jitter -> Tier-1 cached gate floor; drift -> whether the cached baseline can be trusted)

  VERDICT: REAL IMPROVEMENT - PR faster by ~18.64% (t-CI and Wilcoxon agree)

  raw pairs: /tmp/abba_run/pairs.csv

_{- = PR faster. Trust the verdict when paired-t and Wilcoxon agree.}

[ColoCarletti]

/bench-gpu