Convert the multidevice allreduce correctness + latency/buffer-kind sweeps
to run through the real PyTorch-distributed path
(init_process_group(backend="ahbm") -> mp.spawn -> dist.all_reduce) instead
of direct ctx.launch, and reorganize the CCL/allreduce tests into a
tests/sccl/ package split one test per file.
Production change (required for the distributed path on non-square SIP grids):
- AhbmCCLBackend now reads explicit system.sips.w/h from the spec, with a
square-only sqrt fallback that raises on ambiguity, instead of silently
guessing round(sqrt(count)). This fixes the 2x3 / 3x2 torus + mesh cases,
which previously resolved to a wrong 2x2 grid. Mirrors the test helper's
_sip_topo_dims precedence (explicit w/h > square fallback > raise).
Test reorganization (tests/sccl/):
- _allreduce_helpers.py: shared plumbing (distributed driver, config writers,
direct-launch run_allreduce parity reference, sweep/buffer-kind constants,
plot aggregators, topology-diagram + FSIM-comparison emitters).
- test_allreduce_ring_torus_mesh.py: correctness across ring/torus/mesh.
- test_distributed_default_topology.py: full distributed path on topology.yaml.
- test_plot_latency_sweep.py / test_plot_buffer_kind_sweep.py: sweep rows.
- test_plot_topology_diagram.py / test_plot_comparison_fsim.py: plot emitters.
- test_intercube_root_center.py: moved in (ADR-0032 center-root latency guard).
Also:
- Move the FSIM comparison plot generator out of scripts/ into the sccl suite.
- Delete superseded test files (test_allreduce_multidevice,
test_distributed_lrab_hierarchical_allreduce, test_allreduce_buffer_kind_sweep)
and repoint conftest aggregators + the ipcq buffer-kind importers.
- Regenerate the allreduce_latency_plots derived artifacts from the full sweep.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Rename the intercube all-reduce identity to lrab_hierarchical_allreduce
(module, config key, distributed test) so the name reflects both levels
it implements: LRAB intra-SIP (local reduce to center root + broadcast)
and the hierarchical inter-SIP topology exchange (ring/torus/mesh).
ADR-0032 slug kept as the stable decision id; pure rename, no logic change.
Also in this batch:
- ADR-0032 (EN+KO): document the shipped center-root bidirectional reduce
(doc was stale corner-root); annotate ccl.yaml root_cube as a placeholder.
- Rename allreduce + pe2pe latency plots to descriptive, title-matching
filenames and retitle the in-plot headings; drop overview/overview_log.
- Point the PPTX image refs at the new plot names.
Doc + derived-artifact + rename only; no simulation behavior changed.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Move benches/ -> src/kernbench/benches/ and src/kernbench/cli/probe.py ->
src/kernbench/probes/probe.py. Each bench self-registers via
@bench(name=..., description=...); kernbench list enumerates benches
with auto-assigned indices, --bench accepts kebab-case name or numeric
index. Audit at package-import time fails if any non-underscore module
forgets the decorator. ADR-0010 (EN + KO) updated to reflect the new
resolver path, list subcommand, and probes package separation.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
User asked to surface system-wide congestion (more accurate than
single-cube), bring back the latency-breakdown plot under a separate
filename, and rename the obscure ``streaming`` category.
Scenarios:
Renamed all_pe_to_pe0 → all_pe_cube0_to_pe0 (clarify cube scope).
Added two SIP-wide scenarios:
sip_local_all — every PE in sip0 (128 total) accesses its own
local slice. All paths disjoint (each PE owns
its own hbm_ctrl.peX), so the model should
scale linearly with cube count.
sip_hotspot_pe0 — every PE in sip0 (128 total) targets
sip0.cube0.pe0_slice. Worst-case hotspot:
UCIe inbound + r0c0→hbm_ctrl.pe0 saturated.
Each bar now carries an ``N=...`` annotation showing the issuer
count, and the chart titles say the scope explicitly.
Effective BW + util at 16 KB:
sip_local_all N=128 eff= 27.2 TB/s util_a= 83 %
sip_hotspot_pe0 N=128 eff= 134 GB/s util_a= 93 %
(UCIe-into-cube0 saturated)
Plots:
no_congestion.png + congestion.png — Effective BW utilization
(two bars: single vs aggregate peak)
breakdown_no_congestion.png +
breakdown_congestion.png — stacked latency breakdown
(renamed from previous)
summary.csv with columns for both views.
The visual y-cap on BW utilization is 150 %. Bars exceeding it (e.g.
sip_local_all's util_single = 10,639 %) are drawn at the cap with an
upward arrow and the real value annotated. The verification rule for
``util_single`` is loosened to ``≤ n_issuers × 100 % + 5 %`` so
massively-parallel disjoint scenarios pass.
Category renamed: ``streaming`` → ``wire_transfer``. It is the
bulk-transfer time = (n_flits − 1) × flit_bytes / bottleneck_bw — the
cost of streaming the rest of the payload through the slowest wire
after the first flit has arrived.
All checks PASS.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Each scenario now shows TWO bars:
util_single = effective_bw / single-path peak × 100
(peak = min bw_gbs on first issuer's path)
util_aggregate = effective_bw / aggregate-resource peak × 100
(peak = max-min fair share across concurrent paths)
Aggregate peak uses a max-min fair-share computation: each concurrent
path's sustainable share on an edge is bw_gbs / usage_count, the
per-path throughput is the min share along its edges, and the aggregate
peak is the sum across paths. This produces the correct answer for both
shared-bottleneck scenarios (N paths converge on one wire → aggregate =
wire BW) and multi-lane shared resources (UCIe's 4 connections used in
parallel → aggregate ≈ 4 × per-conn BW), without enumerating max-flow.
Single-issuer (no_congestion) → util_single == util_aggregate by
definition. Congestion exposes the divergence:
ctrl_hot_{1,2,3}, all_pe_to_pe0 → both metrics agree (one shared
bottleneck: r0c0→hbm_ctrl.pe0 @ 256 GB/s)
8×PE eastbound → util_single=106 % (single conn @ 128 GB/s) but
util_aggregate=85 % (UCIe-W.conn0 @ 7-way shared,
aggregate peak ≈ 160 GB/s under the current
cross-cube routing that funnels via cube1.r0c0).
Verification updated to assert:
(2) util_aggregate ≤ 100 % (effective BW can't exceed the aggregate
resource peak, by construction).
(3) single-issuer util_single == util_aggregate.
(7) ucie_eastbound: util_aggregate is meaningfully smaller than
util_single (the multi-lane peak correction is observable).
CSV grows with peak_aggregate_bw_gbs and util_aggregate_pct columns;
breakdown columns retained.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Replaces the latency-breakdown stacked bars with a single utilization
bar per scenario. Each bar shows ``effective_bw / peak_bottleneck_bw``
with both values annotated, and a horizontal "single-path peak" line at
100 %. The colour band (green ≥70 %, amber ≥40 %, red <40 %) makes the
no-congestion distance roll-off scannable at a glance.
Definitions:
effective_bw = (total bytes transferred) / wall-clock time
no_congestion: nbytes / total_ns
congestion: n_issuers × nbytes / makespan_ns (aggregate)
peak_bw = min(edge.bw_gbs) on first issuer's path
util_pct = effective_bw / peak_bw × 100
The congestion graph shows that 8×PE eastbound exceeds 100 % of a
single-path peak (106.4 %): UCIe-N's 4 connections × 128 GB/s give
512 GB/s of aggregate eastbound capacity, so concurrent issuers across
disjoint conns sum past any single conn's 128 GB/s. The 8×PE→pe0_slice
hotspot reaches 91.7 %, almost saturating the shared r0c0→hbm_ctrl.pe0
bottleneck — the simulator's address-based PC striping + per-flit
arbitration model amortises the cost cleanly.
Self-verification updated to BW invariants:
(1) effective BW shrinks as topological distance grows
(2) util_pct ∈ (0, 250 %]
(3) single-issuer util_pct ≤ 100 %
(4) effective_bw = nbytes / total_ns for single requests
(5) congestion aggregate BW grows monotonically with issuer count
on the hot-target series
(6) 8-PE all-hit-pe0 saturates ≥ 70 % of shared peak
All checks PASS at the current model.
The CSV retains all breakdown components (pe_setup, noc_mesh, ucie,
fabric, streaming, hbm_ctrl, contention) so a future replot can still
recover the latency-breakdown view without re-running the simulator.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
scripts/plot_pe_dma_perf.py runs the simulator across six
no-congestion scenarios (SAME_CUBE_PE_LOCAL / REMOTE_BEST /
REMOTE_WORST, REMOTE_CUBE_BEST / REMOTE_WORST, REMOTE_SIP) and
five congestion scenarios (1/2/3 PE hot-target, 8-PE corresp.
cube-to-cube, 8-PE all-hit-pe0). It categorises actual total /
makespan into pe_setup, noc_mesh, ucie, fabric, streaming,
hbm_ctrl, and a contention residual using a wormhole-pipelined
model (first-flit arrival + (n_flits-1)/bottleneck + final
chunk_time).
Outputs:
docs/diagrams/pe_dma_perf/no_congestion.png — single-PE latency
by topological distance. Visualises monotonic growth from
SAME_CUBE_PE_LOCAL (77 ns) up to REMOTE_CUBE_PE_REMOTE_WORST
(573 ns) and REMOTE_SIP (409 ns).
docs/diagrams/pe_dma_perf/congestion.png — makespan as concurrent
issuer count grows. ctrl_hot_{1,2,3}=82/158/230 ns; 8-PE
eastbound UCIe = 963 ns; 8-PE all-hit-pe0 = 558 ns.
docs/diagrams/pe_dma_perf/summary.csv — raw rows for re-plotting.
Built-in --verify harness asserts:
(1) distance monotonicity for no-congestion;
(2) same-cube paths contain zero UCIe budget;
(3) remote-cube/SIP paths carry positive UCIe budget;
(4) breakdown is internally consistent (formula ≤ actual);
(5) streaming term matches (n_flits-1) × flit_bytes /
bottleneck_bw within 5 % for the local scenario;
(6) congestion makespan is monotonic in issuer count;
(7) 8-PE hotspot strictly exceeds 3-PE hotspot.
Cross-SIP gets a looser 70 % contention slack because the path
crosses two non-flit-aware (pcie_ep) boundaries that force
store-and-forward re-streaming the simple formula does not
attribute. Single-cube scenarios stay under 25 % residual.
All checks PASS at the current model (post ADR-0019 D1/D4
per-PE HBM CTRL restoration).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Two related issues caused measured pipeline efficiency to look
worse than the simulator's actual behavior:
1. DMA timing recorded too early. The op-log start timestamp
for a DMA op fired when the request entered the queue, and
the DMA channel was released as soon as the request was
issued. Back-to-back DMAs therefore appeared to grab the
channel simultaneously, with per-op duration drifting
upward as queue depth grew - an artifact, not real cost.
Fix: defer the start timestamp until after the channel is
acquired, and hold the channel through the full HBM
round-trip until the response returns. Per-op duration is
now constant and equal to the actual transfer interval;
serialization is visible as queue wait, not as inflated
service time.
2. Sweep timing window folded in pre-composite work. The PE
timing window spanned every PE engine record, which
included the upfront pinned-operand DMA issued before the
composite GEMM begins. For large-K shapes that one-shot
load can be nearly half of the window, conflating
operand-staging cost with composite-pipeline behavior.
Fix: add a second window scoped to the composite pipeline
by filtering op_log records to those tagged with a
tile-pipeline stage; the legacy operand-load path is
untagged and naturally excluded. For 32x3072x32 load_ref
the window drops from 1765ns to 992ns and measured eff
lines up with the steady-state DMA-bound stage limit
instead of being penalized for the one-time load.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
scripts/build_overview_slides.py renders a 5-slide PPTX
(kernbench2_overview.pptx) summarizing architecture, model
correctness, IPCQ, allreduce, and buffer-kind tier comparison.
scripts/emit_overview_with_external_ref.py renders log-y and
broken-y variants of the allreduce overview (overview_log.png,
overview_broken.png) including a 366 µs ext-sim reference marker
at 96 KB / PE.
Also includes cube_mesh_view.png rendered from the SVG.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The pe2pe overview compared IPCQ (tl.send + tl.recv) against raw DMA
(tl.load + tl.store), but DMA is one-sided — DST never reads — while
tl.recv pays a slot-read on DST. The comparison was unfair: IPCQ
looked slower partly because it does more work.
Adds tl.recv_no_consume() — a separate, diagnostic-only entry point
that blocks for slot arrival but skips the slot-read (and bank-hop)
charge on DST. Production tl.recv is unchanged (no `consume` kwarg
on the public API), so the diagnostic flag can never accidentally
leak into real workloads.
Updates test_pe_to_pe_latency to call tl.recv_no_consume so the
overview.png shows IPCQ no-consume vs raw DMA on equal footing.
Also fixes PLOT_DIR back to docs/diagrams/pe2pe_latency_plots/
(was lost in a merge). Adds scripts/replot_pe2pe.py for label-only
re-renders without re-measuring.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
mukesh
ywkang