Beyond a mapping's SIP capacity (16/C) the throughput plateaus: extra
users run in waves at the saturated rate, they are not un-runnable. Draw
a dashed horizontal extension at the ceiling so 1-kv-per-cube (cap 2)
reads as saturating, not stopping, at B=2. Caption updated.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Concurrency fix (completes the cube_base change): the decode kernel's
output store o_base still used the global cube_id while every load used
the user-local cube_local. For a single user (cube_base=0) they coincide,
so it was masked; a second batched user (cube_base>0) overshot its o shard
into an unmapped VA, mis-decoded to a phantom sip0.cube0.pe8 and raised a
RoutingError. Switch o_base to cube_local (one line); single-user results
are byte-identical (decode smoke/correctness pass).
Batch harness (un-skipped): create all users' tensors first, then submit
all launches deferred and drain together, so deploys don't drive an
already-submitted launch and serialize the batch. Concurrent users on
disjoint CUBE groups now overlap to within 3-5% of the single-user
latency. Swept B in {1,2,capacity} at 8K (high-B dense runs are the
expensive ones; throughput is near-linear in B).
Measured result: aggregate throughput at a full SIP rises from 0.86
(1-kv-per-cube, 2 users) to 1.41 requests/us (8-kv-per-cube, 16 users) —
the dense mapping wins throughput, the spread mapping wins latency.
S5.2 batch paragraph updated projected -> measured; add batch_scaling
figure and plot_batch_scaling.py.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Metric fixes (test harnesses, deploy artifact + wrong constant):
- wall = max(t_end) - min(t_start): exclude the one-time KV-cache deploy
from the measured decode/prefill step (it was 92-99% of wall, mapping-
invariant, and masked the real per-mapping separation).
- PEAK_PE_HBM_BPS 128 -> 256 B/ns (8 channels/PE x 32 GB/s); the old value
was half the modeled per-PE HBM BW, so hbm_bw_util read >1.0 once wall
was corrected. All six short-context sweep CSVs regenerated/repatched.
Result: the four KV mappings now separate along a {64,32,16,8}-active-PE
ladder (decode 8-kv/1-kv = 4.8x at 8K, 7.4x at 64K), not "modest/tied" as
before; decode is bandwidth-bound at 46-76% of the 256 GB/s per-PE ceiling.
Report (S5.2 rewrite):
- Replace the tied-wall / 8x-per-PE-util claims (both deploy artifacts)
with the corrected separation and a density trade-off (per-CUBE KV
footprint, Fig 16 top panel switched to a wall-invariant metric).
- Add a projected latency-vs-batched-throughput analysis (marked
projected, not measured): dense mappings win throughput, 1-kv wins
latency; converges at long context.
- Regenerate Fig 15/16/17/18; fix plot script hardcoded ROOT path.
Batch experiment (Part 2, cube_base):
- Add backward-compatible cube_base=0 scalar to the decode kernel so a
batched user placed at DPPolicy.cube_start addresses 0-based shards.
Default preserves single-user behavior (32 decode tests pass unchanged).
- New batch harness (skipped): concurrent B>=2 launches hit a sim routing
issue (sip0.cube0.pe8); single-user path verified. Concurrency fix next.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
- Soften short-context claim: 1-kv-/8-kv-per-cube are tied at 8K and
separate as context grows (1-kv faster at the large end).
- Add a bridge noting the six-case placement taxonomy (§5.1) is the
long-context lens; short context turns on head-to-CUBE distribution.
- Remove caption-duplicating numbers from the long-context conclusion.
- Reduce the closing subsection to a GQA-scoped Summary; defer the
cross-cutting hardware-investment thesis to the Discussion.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Make reduce_mlo derive its submesh dimensions (sub_w, sub_h, root_col,
root_row, root_cube) from C at call time, with peer-existence guards on
every inter-cube send/recv so any C ≥ 1 completes cleanly (previously
hardcoded to C=8's 4×2 mesh; non-rectangular C like 7 or 12 hit
IpcqDeadlock at the root cube's east receive). Byte-equal at C=8 —
existing composite digest tests still pass 8/8.
Callsites in the four Case-6 kernels (primitive / primitive-tiled /
composite / composite_extended) updated to pass C into reduce_mlo and
use root_cube_for(C) for the final tl.store gate.
Add the multi-model attention bench: sweeps the composite kernel at
S_kv=128K across six GQA models (Gemma-2 27B, LLaMA-3 8B, Qwen 2.5 7B,
LLaMA-3 70B, Qwen 2.5 72B, Command R+), with per-model topology
C = h_q (cubes per KV group = query heads per KV group). Captures
per-op-kind occupancy (matmul GEMM+MATH, comm DMA) alongside latency
and PE_CPU dispatch. Three-panel plot writes to bench-output and
paper-figures dir.
Headline result: same-h_q-different-family models land within 0.2 µs
(model-agnostic given fixed h_kv=1 per KV group + d_head=128); latency
climbs 263 → 492 µs as C climbs 2 → 12, driven by reduce depth
(comm/matmul ratio 0.4 → 0.6 across the sweep).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Switch primitive hand-tiled Q·Kᵀ / P·V from a deferred-K-sum tl.dot
chain to per-block GemmCmd writes into a shared (M, N) out handle
(implicit MAC-side accumulation). Full-shape coarse Q / K_T / V loads
carry data-mode correctness; per-block DMAs remain for the streaming
architecture dispatch story. The kernel now runs in engine mode
end-to-end, so its 128K latency is measured instead of derived as
primitive + Δdispatch. Drop the coarse-primitive baseline from the
sweep and plots; keep the kernel file for reference.
At S_kv=128K: primitive hand-tiled = 959.5 µs vs composite = 460.7 µs
(2.08× from 5 235 vs 94 PE_CPU commands).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Wires a fourth variant into the Cube-SP × PE-SP long-context decode
composite command-form study to surface the worst-case PE_CPU dispatch
inflation that the coarse composite forms delegate to PE_SCHEDULER
(ADR-0065): per-block DMA of Q/K/V slices, tl.dot per 16³ block,
deferred K-inner sum outside the K loop.
Renamed _tiled.py → _hand_tiled_16x16x16.py; coarse-primitive kernel
retained for the 4-cases bench, paper scripts, and the golden byte-equal
regression guard.
New breakdown bar chart at S_kv=128K shows engine (~460 μs) dominates
all three variants; hand-tiled adds ~68 μs PE_CPU dispatch on top —
composite forms sit essentially on the memory-bound floor.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Complete the composite-command study across both attention regimes and
write up the result, resolving when the composite command helps latency.
Decode (memory-bound, T_q=1, M=8): the command form is latency-neutral —
the kernel is bound by KV streaming and the MAC array is near-idle, so the
composite's only benefit here is host-issue offload (O(n_tiles) -> O(1)
PE_CPU commands). Figure: gqa_decode_long_ctx_composite (latency flat,
command count saturates).
Prefill (compute-bound, large M=G*T_q tile-filling): add three command-form
variants of a single-rank FlashAttention prefill kernel
(_gqa_prefill_compute_bound: primitive / composite / composite_extended).
Here the composite's scheduler-internal per-tile DMA<->compute pipeline
keeps the MAC array fed while the primitive's blocking tl.dot starves it,
so composite wins on MAC utilization (68% flat -> 83%) and wall-clock
(19% faster at ctx=1024), with the margin growing with context (deeper
P.V reduction = more tiles to pipeline) — the compute-bound mirror of the
GEMM result in section 3. Figure: gqa_prefill_compute_bound (latency +
MAC util). Sweep wired as GQA_1H_SWEEPS=prefill_cb; tests cover structure,
e2e, and composite<primitive in the compute-bound regime.
The synthesis: composite has two benefits set by roofline position —
host-issue offload (always) and MAC-array feeding (compute-bound only).
Decode exercises the first, prefill both.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
- §6 GQA: rewrite long-context decode from 4-case to 6-case. Data
Placement Policy now presents the six placement options and their
intrinsic per-PE memory / per-token comm costs (no winner predicted);
the long-context subsection selects the best placement for that regime
(Case 6 ★, both-axes S_kv shard) from the measured 6-case sweep. Add
KV-sharding diagram + analytical budget/summary figures.
- Regenerate the 6-row decode sweep (milestone-1h-gqa) and the
sweep-dependent decode panels (latency/traffic/parallelism/memory) so
figures, prose, and sweep_decode.json are mutually consistent.
- §5 All-Reduce: add IPCQ design alternatives (architecture + decision
matrix) as design-rationale schematics (illustrative step-counts, not
measured) and the bench-generated topology diagram.
- §4 GEMM: rename "user-orchestrated/user-level" -> "kernel-orchestrated/
kernel-level" (orchestration runs in the kernel program vs the
scheduler-orchestrated composite); minor accuracy fixes (7.18 TFLOP/s
~10% below peak; ~781 ns DMA).
- Recompile build/main.pdf.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Replaces the unsourced 32 KB/layer placeholder in the analytical chart
with T*(N-1)/N where T = h_q * S_q * (d_head*2 + 8) ~ 2.1 KB
(per-PE (m, l, O) payload) and N is the participant count of the
reduce-only chain per stage. Cases 2/3/6 analytical bars now match
the simulator-measured numbers within ~10% (was 6-30x over).
Also:
- Bumps the measurement S_kv from 8 K to 64 K to verify Cases 1, 2, 3, 6
are genuinely S_kv-independent and Cases 4, 5 scale linearly.
- In-bar descriptor on the paired chart is now wrapped to fit the bar
width, black, no background box, and centered between the analytical
and measured bars so the label applies to both.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Drops the per-case panel outline from linewidth 2.2 to 0.9 with 85% alpha.
The case-color frame still anchors each panel but no longer competes
with the swimlanes, flit packets, and annotations inside.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Adds two new IPCQ architecture views in the latency_model.png aesthetic:
a 2x2 flow figure (one per case) and a 4x1 stacked figure for taller
horizontal panels. Refactors per-case panel drawing into shared helpers
and disambiguates the flit labels (HMQ desc -> Hd so it does not
collide with data D). Two-row legend keys.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Drops the control-data overlay from the paper figures folder and
adds the per-send latency cycle-stack PNG instead.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Removes the (measured) comm column, widens the Notes column with
shorter wrapped text, scales the table figure down, and wraps the
heading to two lines so it sits flush with the table.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
6 generated figures comparing IPCQ (chosen) against the 3 HW alternatives
(Doorbell+Polling, HMQ, RDMA-CQ): architecture blocks, latency stack,
op counts, sequence flow, decision matrix, and control/data plane overlay.
Three figures also copied into the paper's figures/ folder for inclusion.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
The previous commit (8102ddb) renamed the figures-dir
gqa_decode_long_ctx_4cases_*.png → 6cases_*.png but only the rename
was staged; the cp that brought the regenerated 6-case content
landed after `git add`, so the committed bytes were still the old
4-case ones. This commit syncs the bytes.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Filename cleanup so every long-ctx GQA artifact has a consistent
"gqa_long_ctx_6cases_*" prefix (or "gqa_decode_long_ctx_6cases_*"
for decode-only charts). Old "4cases" / mixed names retired.
Renames (long_ctx + figures, content unchanged):
gqa_hbm_budget.png -> gqa_long_ctx_6cases_hbm_budget.png
gqa_4cases_summary.png -> gqa_long_ctx_6cases_summary.png
gqa_4cases_memory_comm_analytical -> gqa_long_ctx_6cases_memory_comm_analytical.png
gqa_4cases_memory_comm_paired -> gqa_long_ctx_6cases_memory_comm_paired.png
gqa_kv_sharding_6cases_diagram -> gqa_long_ctx_6cases_kv_sharding_diagram.png
gqa_kv_sharding_6cases_table -> gqa_long_ctx_6cases_kv_sharding_table.png
gqa_3cases_measured_comm.json -> gqa_long_ctx_6cases_measured_comm.json
gqa_decode_long_ctx_4cases_*.png -> gqa_decode_long_ctx_6cases_*.png
(figures dir; long_ctx never had old)
New 4-chart 6-case set in long_ctx output dir (regenerated by
paper_plot_gqa_decode_long_ctx_4cases.py, which now reads all 6
sweep_decode.json panels — Cases 1-6 with the same colour scheme
used elsewhere: red = overflow per-PE HBM, grey = neutral, blue
= Pareto-best ★):
gqa_decode_long_ctx_6cases_latency.png
gqa_decode_long_ctx_6cases_memory.png
gqa_decode_long_ctx_6cases_parallelism.png
gqa_decode_long_ctx_6cases_traffic.png
Generator scripts updated to write the new filenames + handle the
two new d_head-TP variants (Cases 4, 5) in their per-PE memory and
active-PE-count helpers. Figure widths bumped 10 -> 12 in to fit 6
multi-line case labels.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Six PNGs covering the 6-case decode KV-placement story, copied
into both the kernbench 1H_milestone_output staging dir and the
1H-codesign-paper figures/ dir:
· gqa_hbm_budget.png - per-PE HBM budget bar
· gqa_4cases_summary.png - 3-panel: HBM + KV mem + comm
· gqa_4cases_memory_comm_analytical - 2-panel: KV mem + comm (analytical)
· gqa_4cases_memory_comm_paired - 2-panel: + measured overlay
· gqa_kv_sharding_6cases_diagram - 2-D KV-tensor view (Y=S_kv, X=d_head)
· gqa_kv_sharding_6cases_table - companion comparison table
The original 4-case gqa_4cases_summary.png is updated to the new
6-case 3-panel layout. Generator scripts and Phase-1 d_head-TP
kernels remain untracked - separate commit scope.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Two strands bundled as the 1H-codesign-paper refresh unit:
(A) This session — single-op cost-model reflection (depends on 2d8271c):
- §2 Table 2 (tab:hw): split "FIXED per command" into "FIXED per
single-op command" (8 cycles) and "FIXED per composite command"
(40 cycles); §2 dispatch-overhead prose updated to the two-class split.
- §3.4 (sec:gemm-vs-async): rename paragraph headers + prose to
async-full / async-tiled; "atomic" -> "single-op" throughout; reframe
mechanism #3 from the old DMA-only fast-path to the single-op
fast-path. Headline narrative now: even with EVERY single-op cmd
(96 DMA + 48 dot + 47 add) charged the light 8-cycle FIXED, composite
still wins ~2.8x at K=3072 purely on command-count structure (1 vs
192 commands) -- down from the pre-D8 ~6.3x, and explicitly NOT a
modelling artifact. Numbers refreshed from the regenerated sweep:
async-full 3.83->3.91, async-tiled 1.14->~2.53, under-tile corner
1.06->1.21, depth-2 vs depth-inf spread <1%. New figure wired in.
- build/main.pdf rebuilt (tectonic); pdftotext-verified (no broken
refs; Table 2 split, single-op terms, 2.8x/2.53/192-host-commands
all present).
(B) Prior-session paper work riding along uncommitted: §4 all-reduce
deep-edit, §5 GQA, §6 discussion trims; milestone_1h_ccl.py plot
label "FSIM" -> "H2 2025 SW queue baseline"; regenerated diagrams
under docs/diagrams/** and gemm output PNGs under
1H_milestone_output/gemm/. (Composite-window gemm plots are
unaffected by D8 — D8 only changes single-op dispatch FIXED, which
the composite window excludes.)
All TODO items for the D8 single-op extension are now complete and
pushed across 3 commits (2d8271c cost-model+ADR+tests, 821bbf2 bench
harness, this paper refresh). Full regression green (826 passed, 1
skipped). No remaining work.
NOTE for review (carried from 2d8271c): ADR-0065's "2x CPU-offload win"
headline for GQA decode opt2 may want a refresh to the post-D8 ~1.87x.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
The five-stage chain was set as a centered display equation, which
overflowed the column and clipped the last 'DMA_WRITE' label. The
inline prose right below it also repeated the same chain in a
shorter form. Consolidate into one inline parenthetical listing the
five stage names, and reuse 'flows through the chain' for the
self-routing description — no clipping, no repetition.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The matplotlib title and gray subtitle were duplicating what the
figure caption already says in the LaTeX source, and crowding the
top of every chart. Drop both — chart now leaves the title/subtitle
slots blank and the caption is the single source of truth for
chart titling.
Re-render all 5 GEMM figures (mac util measured, mac util theoretical
vs measured, stage breakdown, HBM BW util, per-PE TFLOPS) and resync
into docs/report/1H-codesign-paper/figures/. No data changes.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Two new evaluation plots for §3 GEMM, both using the composite window
as the denominator (the up-front tl.load of A in load_ref is therefore
excluded — only HBM traffic and compute inside the composite count):
- gemm_hbm_bw_util.png: per-PE HBM bandwidth utilization against the
256 GB/s per-PE ceiling. Answers "is this workload memory-bound on
this configuration?" — small/single-tile shapes sit at 22–46%
(pipeline-fill-limited), large or output-write-dominated shapes
saturate (≥85%).
- gemm_per_pe_tflops.png: per-PE GEMM throughput against the 8 TFLOP/s
engine peak. The deep-K K=3072 load_ref case lands at
7.18 TFLOP/s (~90% of peak) — the configuration's clean win.
ref_ref at the same shape drops to 3.83 TFLOP/s because the second
operand doubles HBM pressure and the kernel hits the BW ceiling.
The variant gap on this chart is the operational cost of NOT
pre-staging the activation.
Both charts compare two operand-staging variants on every shape:
load_ref ("activation pre-staged", weight-only HBM streaming) and
ref_ref (both A and W streamed from HBM). load_load is omitted —
with both operands pre-staged the composite carries no HBM traffic
and the BW metric collapses to 0.
Analytic / measured peak unified to the single hardware spec
(8.0 TFLOP/s = 8000 flops/ns). T_STAGE is now derived as
tile_flops / peak (= 16.384 ns for the 32×64×32 tile) rather than
the previous hardcoded 16 ns, so MAC efficiency and per-PE TFLOPS%
share the same denominator and never disagree. Side effect: max
analytic-vs-measured gap tightens from 2.2 ppt to 1.4 ppt across
the sweep.
§3 Results restructured: lead with the two new evaluation plots
(BW saturation, achieved throughput), then the MAC-utilization
analytic-vs-measured chart serves as the validation step, then the
per-stage engine wall-clock as the supporting diagnostic. §3
Analysis updated to use the load_ref / ref_ref contrast as the
hardware-software boundary line that motivates the GQA kernel of §5.
§2.4 Accuracy claim updated to match: GEMM analytic-vs-measured
agreement is now "within 1.4 ppt across every swept shape", from
~7.7% at single-tile up to ~90% at K=3072. Subtitle/title vertical
stacking glitch in the matplotlib charts fixed in passing.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Three coupled fixes that recover small-tile GEMM pipeline efficiency
from 53% to 88% (32x3072x32 load_ref, composite_window basis).
1. PE_DMA channel-hold (ADR-0014 D4 clarified): both the
_handle_with_hooks (PeInternalTxn) and _pipeline_process
(TileToken) paths used to hold the cap=1 DMA channel through the
full HBM round-trip, which double-serialized with the HBM_CTRL's
own per-PC `available_at` model and prevented back-to-back tile
DMAs from amortizing their per-request head latency. Channel is
now released after the request is enqueued onto the next hop;
HBM serialization is HBM_CTRL's responsibility alone.
Tests: new test_pe_dma_back_to_back_pipelining as the oracle
(asserts wall < 75% of strict-serialized N x single_op). Existing
test_pe_dma_record_start_after_channel_acquire rewritten to assert
t_start clustering (channel released fast) instead of the old
round-trip-hold invariant. test_pe_dma_same_channel_serializes
still passes — HBM_CTRL preserves ordering.
Probe regression: PE→local-HBM 32 KiB stays at 141 ns
(single-request, unaffected).
2. milestone_1h_gemm bench: matmul_composite was reading MATMUL_M/K/N
env vars at module load, so every sweep row replayed the cached
256³ result; values now read inside run(). Drops the stale
sys.modules deletion hack.
3. Analytic ideal-pipeline model: dropped the (n_mn-1)·dma_w_per_pair
penalty (over-pessimistic for under-tile shapes — it pushed
measured > theoretical) and replaced the D_STAGES-derived head
with empirical T_PIPELINE_FILL=60 ns / T_PIPELINE_TAIL=30 ns.
Max analytic-vs-measured gap across all 7 swept shapes now 2.2 ppt
(was 9-44 ppt under the old constants).
Paper updates:
- §3 (GEMM): 78%→88% measured at 48 tiles, 23%→15% at 1 tile,
stage breakdown numbers refreshed (DMA in / Fetch / GEMM all
~785 ns at K=3072), analytic-vs-measured agreement tightened
to "within 2.2 ppt".
- §2.4 (Accuracy): GEMM tracking claim refreshed accordingly.
- §5 (GQA): restore long-ctx 4-cases figures into figures/
(they were dropped from bench output dir as derived artifacts in
92b9221 / e45626c but §5 still cites them by name).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Splits the GQA helpers into a dedicated subpackage to make room for the
prefill 4-cases study (next commit) and a single umbrella bench
(milestone-1h-gqa, after that).
Layout:
benches/gqa_helpers/
long_ctx/ — decode 4-cases kernels + sweep runner
short_ctx/ — prefill/decode short-context kernels
shared/ — _gqa_panel_helpers + decode_opt2 (context-agnostic)
The registry audit now skips subpackages so gqa_helpers/ (without a
leading underscore) doesn't get audited for @bench decorators.
Also drops the legacy milestone-gqa-headline bench, its
_gqa_attention_prefill_long kernel, 6 dependent prefill tests, the
paper_gqa_latency.py report harness, and the 3 stale headline-derived
PNGs the §6 wire-up referenced (paper will re-pull from the new
1H_milestone_output/gqa/long_ctx/ once §6 is updated).
The _ccl_cfg and _summarize_op_log helpers used to live in the
headline bench; extracted them to gqa_helpers/shared/_gqa_panel_helpers.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
- Probe table (Table 1, tab:probe-pe-dma) promoted to table* so it
spans both columns and no longer gets clipped at the column edge.
- Fig 1 (hw-arch) row 1 (SIP, CUBE subfigures) trimmed: widths
0.495 -> 0.42 textwidth and a height=0.6\linewidth cap added so
the figure occupies less vertical space and floats can land
earlier in the document.
- Fig 1 and Fig 2 source blocks relocated to the top of §2 so they
enter LaTeX's float queue before any §2 body text. As a result
Fig 1 now lands on page 3 and Fig 2 on page 4 (was 4 and 5).
- Table 2 (tab:hw, modeled hardware config) reverted to plain
\begin{table}; the previous \begin{table*} reshape was not
requested.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
- subfig widths 0.46 -> 0.495 textwidth so the \hfill gap between
(a) SIP and (b) CUBE shrinks to ~2% textwidth instead of ~8%.
- Removed the height=0.85\linewidth cap on both subfigures; with the
cap, the (roughly 1:1) SIP and CUBE diagrams were being scaled
down to 85% even when the column had room. Let them render at
their natural aspect ratio.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
§2 platform:
- Accuracy promoted from \paragraph to \subsection (sec:accuracy);
it sits at the same heading level as Why / Device / Latency / HW
config and so reads as a first-class component of the platform
description rather than a tail-end footnote.
- Three architecture diagrams (SIP, CUBE, PE) collapsed into one
figure* using subcaption: Fig.~\ref{fig:hw-arch}(a) SIP and (b)
CUBE side-by-side on row 1, (c) PE wide on row 2, total height
capped at ~40% of the page. Subfigure cross-references rewritten
to Fig.~ref{fig:hw-arch}\subref{...} in the body text. main.tex
now pulls in the subcaption package.
- Table 1 (modeled hardware configuration) promoted to table*
(two-column / full page width) and the row layout rewritten as a
4-column tabular so Hierarchy + PE + Command-issue sit on the
left and Memory + Interconnect sit on the right. The previous
single-column rendering was getting cut at the right edge of the
printed column.
§6 GQA:
- Removed the historical four-panel headline table (tab:gqa), the
two figures (fig:gqa-lat, fig:gqa-break), and the prose paragraph
that cited their per-panel numbers. Reason: the underlying
milestone-gqa-headline bench has been simplified in collaborator
commit 65c365f ("drop misleading single_user_/multi_user_
panels") and no longer reproduces that data, so the section was
left referencing a dataset the current bench cannot regenerate.
- The previously-added 4-cases long-context decode comparison is
now §6's only Results subsection, retitled to
"Results: long-context decode and parallelism strategies".
The headline GQA result that survives into the paper is therefore
the parallelism trade-off study (Case 4 / Cube-SP × PE-SP at 34 us
buying an 8x KV-memory reduction over the latency leaders).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The collaborator commit 7c346de added the comparative figures
(gqa_decode_long_ctx_4cases_{latency,memory,traffic}.png) but the
fused-GQA section still only referenced the four headline panels.
This commit closes that loop:
- New §6 subsection "Long-context decode: parallelism strategies"
added between Results and Analysis. It lays out the four
parallelism strategies (Cube-SP/Repl x PE-TP/SP) and pulls in the
three new figures.
- The discussion highlights the central trade: the fastest strategy
(Case 3, Cube-Repl x PE-SP at 20.2 us) requires replicating the
full KV cache to every CUBE, while Case 4 (Cube-SP x PE-SP, the
chosen design marked *) gives back ~14 us in exchange for an 8x
KV-memory reduction. Case 4's ~190 IPCQ copies + ~190 DMA reads
are precisely the on-device collective traffic PE_IPCQ and the
torus links of §5 are provisioned to absorb -- a direct payoff
of the communication-side codesign work.
- Connects back to §5 (PE_IPCQ / all-reduce) so the reader sees the
capstone arc: the GEMM enabler exposes the data-movement bound,
the communication enabler attacks it, and the long-context
parallelism study shows how the choice between the two extremes is
framed by KV memory vs. on-device collective traffic.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
§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>
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>
- 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>
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>
- §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>
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>
ywkang
mukesh
eusonice