paper: add /paper skill + 1H HW-SW codesign report (GEMM, All-Reduce, fused GQA)

New `/paper` slash-command skill that synthesizes ADR/SPEC content and live
KernBench benchmark results into a sectioned LaTeX technical paper compiled
to PDF with Tectonic (auto-installed). The skill negotiates a TOC, grounds
every number in committed artifacts or fresh bench runs, and keeps
report-only benches isolated.

This commit also includes the first generated report:
- docs/report/1H-codesign-paper/ — main.tex + per-section .tex, figures,
  toc.md contract, and the built 8-page main.pdf. Covers the platform
  (source-level kernels, latency model + accuracy, HW config from
  topology.yaml), GEMM via composite command, All-Reduce via PE_IPCQ, and
  fused GQA combining both, plus discussion/conclusion/2H future work.
- scripts/paper/ — isolated report harnesses (not registered benches):
  paper_gqa_latency.py harvests per-panel GQA end-to-end latency + engine
  occupancy (the milestone only emitted op-counts); paper_plot_gqa.py
  renders the GQA figures.

GEMM/All-Reduce reuse committed milestone figures/CSVs; GQA results are
generated fresh. Honest flags retained: PE_CPU dispatch cost is 0 in this
config, and the proposed two-composite softmax_merge decode is marked
designed-not-measured.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

docs/report/1H-codesign-paper/toc.md

@@ -0,0 +1,73 @@
+# KernBench — 2026 1H HW-SW Codesign Report — Table of Contents
+
+> Agreed outline (the `/paper` contract). `/paper build` follows this.
+> Period: 2026 1H. Language: English. References: external literature only
+> (no ADR/SPEC named anywhere in the paper). PDF via Tectonic.
+
+## Section order → files
+
+1. **Executive Summary** — `sections/00-exec-summary.tex`
+   The attention-optimization goal, the two enabling optimizations (GEMM
+   composite command + PE_IPCQ communication), the fused GQA capstone, the
+   headline results, and the bottom-line recommendation on which HW changes
+   are worth keeping. Front-loads conclusions; may run slightly longer than
+   a terse abstract.
+
+2. **Introduction** — `sections/01-introduction.tex`
+   1H focus is **attention-kernel optimization**: FlashAttention-style
+   tiling and Grouped Query Attention (GQA), building on prior work on
+   Multi-Head Attention (MHA, studied earlier — referenced as the
+   established baseline, not re-derived). Optimizing a fused attention
+   kernel requires two enabling optimizations, each studied in its own
+   right and then **combined inside the fused kernel**:
+   (a) **GEMM optimization** (the composite command), and
+   (b) **communication optimization** (PE_IPCQ).
+   Narrative arc: *two enablers → the fused GQA capstone that uses both.*
+   Motivate why HW-SW codesign (not software alone) is required.
+
+3. **The KernBench Platform** — `sections/02-platform.tex`
+   - 3.1 *Why KernBench* — source-level kernel execution, no compiler /
+     SW-stack dependency; isolate algorithm-level optimization.
+   - 3.2 *Execution model* — discrete-event graph; runtime API → sim_engine
+     → components; PE pipeline + composite commands; 2-pass data/timing.
+   - 3.3 *Latency model & accuracy* — traversal-based golden invariants;
+     structural CPU-dispatch cost model; known simplifications + calibration.
+   - 3.4 *Modeled Hardware Configuration* — shared platform-wide config from
+     `topology.yaml` (hierarchy counts, clocks, capacities/BW, link params,
+     cost-model constants). Per-experiment workload params live in §4–§6.
+
+4. **GEMM Acceleration via Composite Command** — `sections/03-gemm.tex`
+   necessity · design · results · analysis. (+ GEMM workload params)
+
+5. **All-Reduce Acceleration via PE_IPCQ** — `sections/04-allreduce.tex`
+   necessity · design · results · analysis. (+ All-Reduce topologies/sizes)
+
+6. **Fused Grouped Query Attention** (composite + PE_IPCQ) — `sections/05-gqa.tex`
+   necessity · design · results · analysis. (+ GQA seq/head/user configs)
+
+7. **Discussion** — `sections/06-discussion.tex`
+   Which HW changes are meaningful, and under what regimes (cross-cutting).
+
+8. **Conclusion** — `sections/07-conclusion.tex`
+   The codesign thesis, stated plainly, supported by the measured results.
+
+9. **Future Work — 2H** — `sections/08-future-work.tex`
+   Add the FFN/MoE layer toward full LLM decoding; how compute & data should
+   be distributed for agentic / MoE workloads.
+
+10. **References** *(optional)* — external literature only (FlashAttention,
+    Megatron-LM, GPT-3, Llama 3). No ADR/SPEC entries.
+
+## Per-section structure (§4/§5/§6)
+
+Each of the three optimization sections follows the same four beats:
+**(a) necessity → (b) design → (c) experimental results → (d) analysis & meaning.**
+
+## Build notes
+
+- GEMM (§4) and All-Reduce (§5): reuse committed figures/CSVs under
+  `src/kernbench/benches/1H_milestone_output/{gemm,ccl}/`.
+- GQA (§6): committed `sweep.json` holds op-counts only — latency + figures
+  are generated by an isolated harness under `scripts/paper/` at build time.
+  Measure only the implemented path; mark proposed designs (e.g. the
+  flat-ops `softmax_merge` decode opt2) as designed-not-measured.