Commit Graph

6 Commits

Author SHA1 Message Date
mukesh 7c346dec1b paper(figures): GQA long-context decode 4-cases comparative figures
Generated by scripts/paper/paper_plot_gqa_decode_long_ctx_4cases.py
from the bench's sweep.json (S_kv=8192, the headline 128K run was
sidestepped — Case 2's single-PE 128-tile sweep is the long pole
and shape/ordering is preserved across scales).

  gqa_decode_long_ctx_4cases_latency.png   end-to-end latency / case
  gqa_decode_long_ctx_4cases_traffic.png   ipcq / dma op-count breakdown
  gqa_decode_long_ctx_4cases_memory.png    KV bytes per cube (slide-11)

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-06-15 17:48:34 -07:00
ywkang 7f437a20bd paper(platform): edit-pass through §2 KernBench Platform done
- Reordered §2 subsections to follow the SIP → CUBE → PE → graph
  flow: Why KernBench → Device and execution model → Latency model
  → Modeled hardware configuration. Readers now meet the device
  hierarchy before the graph abstraction that re-uses it.
- §2.2 Device and execution model: starts with the SIP/CUBE/PE
  hierarchy paragraphs (each anchoring fig:sip-arch, fig:cube-arch,
  fig:pe-arch); then the runtime-API/sim-engine/components bullet
  list; then the atomic-vs-composite command distinction (corrects
  the prior over-narrow framing that read every PE command as
  composite -- atomic single-engine commands exist too, and PE_CPU
  itself runs control-plane work directly).
- §2.3 Latency model: opens with the four-contribution decomposition
  (per-node overhead, per-edge transmission, drain, queuing delay)
  and the latency_model schematic; retains existing The hardware as
  a graph / From graph to DES / Latency contributions / Congestion
  / Control-plane cost model / Accuracy paragraphs. Accuracy
  paragraph now closes on KernBench's sufficiency for *relative*
  HW/SW design trade-offs given analytic + external-simulator
  agreement.
- New figures and assets:
  - figures/sip_architecture.pdf  (SIP-level graph view)
  - figures/cube_architecture.pdf (CUBE-level zoom-in)
  - figures/latency_model.png      (conceptual latency-model
                                   schematic with per-node /
                                   per-edge / drain / queuing-delay
                                   colour coding)
  - figures/pe_architecture.png    (carried over)
- Source-of-truth generator for the latency schematic:
  scripts/paper/paper_latency_model_diagram.py (a report-only
  harness under scripts/paper/ per the /paper isolation rule).
- main.tex preamble: \usepackage{tikz} added (kept from prior
  sequence-diagram draft -- harmless now that the latency model is
  a PNG; left in to keep paragraph numbering stable).

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-06-15 17:17:19 -07:00
ywkang 9270d3435a paper(platform): WIP — §2 KernBench Platform mid-edit
Mid-pass through §2. Captured progress so far:

- §2 intro previews the three new threads (graph view, DES engine,
  congestion) the rewritten latency section weaves together.
- §2.3 retitled to "Latency model: a graph traversed by events" and
  restructured into bold-led paragraphs:
  - The hardware as a graph (nodes = components, edges = links).
  - From graph to discrete-event simulation (node/edge events,
    deterministic ordering, correlation-ID trace).
  - Latency contributions (per-node fixed, per-edge size-aware,
    per-service occupancy).
  - Congestion: per-edge FIFO BW occupancy, HBM per-PC parallelism,
    component serial workers — the mechanisms that surface real
    bottlenecks instead of peak-BW roofline.
  - Control-plane cost model (FIXED + b·R) — unchanged.
  - Accuracy: extended with a second cross-check from the all-reduce
    study (torus vs. analytic startup-plus-per-packet model + FSIM
    external single-device reference). Existing GEMM analytic-vs-
    measured 10-20% check retained.
- §2.4 Hardware-configuration table unchanged.

Still TODO: cross-check accuracy numbers against a fresh
milestone-1h-gemm/ccl re-run; the current artifacts predate the
ADR-0064 Rev2 cost-model and IPCQ Phase-2 race fix and may need a
refresh before §2.3 final lock-in.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-06-11 16:31:42 -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 a965db7056 paper(exec-summary): edit-pass through Executive Summary done
- Title: "Hardware-Software Codesign for Grouped-Query Attention on AHBM"
  (drop "Attention-Centric LLM Kernels" framing; subtitle pillars removed)
- Authors: Mukesh Garg, Jiyoon Lee, Yangwook Kang
- Affiliation: System Technology Group, AGI Computing Lab — 2026 1H
- Executive Summary rewritten into a tight three-paragraph structure:
  (1) GQA motivation + three AHBM architectural requirements (placement,
      inter-PE comm, pipelined memory-compute);
  (2) three matching HW-SW co-design mechanisms (GQA-aware placement,
      PE_IPCQ, composite-command GEMM under PE_SCHEDULER) and headline
      results (38% / 25-35% / 78%), closing on efficient HBM-bandwidth use;
  (3) broader applicability across the AHBM software stack.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-06-11 14:44:45 -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