Commit Graph

3 Commits

Author SHA1 Message Date
ywkang e3f08972da paper: §2 polish + §5 fresh all-reduce data + kernbench probe table
§2 platform:
- Add a "memory-centric AHBM" sentence to the device-and-execution
  intro: each PE is paired with dedicated HBM bandwidth and on-PE
  TCM, so the performance question is about feeding compute from
  locally-attached memory + moving the unavoidable inter-PE traffic
  efficiently. Makes the AHBM character of the platform visible
  before §2.3 starts unpacking the latency model.
- Promote "Congestion and contention modeling" from \paragraph to
  \subsubsection: this is the platform's core differentiator over a
  peak-BW roofline, so it deserves its own heading.
- Rename "Control-plane (issue) cost model" to "Command dispatch
  overhead model" -- describes what it actually charges (the PE_CPU
  paying a fixed + per-byte cost to push a command to one of the
  accelerator engines) without the more abstract "control-plane"
  framing.
- Add a third Accuracy cross-check from kernbench probe: a
  PE→HBM DMA-distance sweep that confirms monotonic hop progression,
  bandwidth saturation matching the per-edge model, and the built-in
  invariants (D2H >= H2D, cross-CUBE best < worst). New
  Table~\ref{tab:probe-pe-dma} reports per-traversal latency and
  utilisation at 32KiB / 1MiB across five hop classes.
- Captured probe output as figures/probe_pe_dma_summary.txt for
  reproducibility.

§5 PE_IPCQ / all-reduce:
- Re-ran milestone-1h-ccl on current sim_engine (post-ADR-0064 Rev2
  and the IPCQ slot-wrap Phase-2 race fix). Updated the topology-
  comparison table and the buffer-kind caption to the fresh raw
  latencies. Headline ratios are preserved:
    torus vs mesh saving at 96 KB/PE: 24.8% (was 24.8%) -> ~25%
    torus vs ring saving at 96 KB/PE: 19.3% (was 19.3%) -> ~19%
    TCM vs HBM saving at 64 KB/PE: 13.4% (was 13.9%) -> ~13%
    TCM vs SRAM saving at 64 KB/PE: 37.2% (was 38.3%) -> ~37%
  The Executive Summary's "up to ~14%" / "up to ~38%" framing stays
  consistent with these post values.

Artifacts refreshed in src/kernbench/benches/1H_milestone_output/:
- ccl/summary.csv + per-topology PNGs + buffer-kind CSV/PNG
- ccl/comparison_mesh_vs_ring_vs_2DTorus_vs_theoretical_vs_fsim.png
- gemm/* (re-run yields identical pe_window structure; PNGs
  refreshed)

Paper figures synced to the fresh artifacts:
- figures/allreduce_comparison.png
- figures/allreduce_buffer_kind.png

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-06-16 10:38:47 -07:00
ywkang d5267ec718 paper(intro): edit-pass through §1 Introduction done
- §1 Introduction rewritten with AHBM-first opening, in-paper KernBench
  platform overview, GQA motivation and three architectural requirements,
  three matching HW-SW co-design mechanisms (GQA-aware placement,
  PE_IPCQ, composite-command GEMM under PE_SCHEDULER), broader-
  applicability outlook (PE_IPCQ as collective substrate, composite
  command for fused operator pipelines), and outline. Consistent with
  the Executive Summary structure.
- Executive Summary: extended broader-applicability closing with a
  forward-looking line previewing FFN/MoE integration.
- §5 section heading renamed from
  "All-Reduce Acceleration via PE_IPCQ" to
  "PE_IPCQ and Collective Communication".
- Title page date line refined to "2026 H1 Report".

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-06-11 15:13:01 -07:00
ywkang dd525bfcb7 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>
2026-06-10 22:15:14 -07:00