ywkang
0bf220fed0
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>
607 lines
23 KiB
Python
607 lines
23 KiB
Python
"""Plot PE_DMA Effective BW utilization across topological distance.
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Two graphs (saved to docs/diagrams/pe_dma_perf/):
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no_congestion.png — single PE issues one DMA, target varies in distance:
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1. SAME_CUBE_PE_LOCAL — pe0 -> pe0's slice (own router, 1 hop)
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2. SAME_CUBE_PE_REMOTE_BEST — pe0 -> pe1's slice (adjacent corner)
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3. SAME_CUBE_PE_REMOTE_WORST — pe0 -> pe7's slice (opposite corner)
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4. REMOTE_CUBE_PE_REMOTE_BEST — pe0 -> cube1 pe0's slice (1 UCIe hop)
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5. REMOTE_CUBE_PE_REMOTE_WORST — pe0 -> cube15 pe7's slice (max UCIe + mesh)
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6. REMOTE_SIP_SAME_CUBE_SAME_PE — pe0 -> sip1.cube0.pe0's slice
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congestion.png — concurrent PEs hitting either the same HBM CTRL or
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the same UCIe direction:
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A. 1×PE remote single — baseline (one remote PE reads cube0.pe0_slice)
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B. 2×PE remote concurrent — two adjacent PEs share path to pe0_slice
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C. 3×PE remote concurrent — three PEs contend on pe0's router/HBM
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D. 8×PE same-direction-UCIe — every PE in cube0 reads cube1 same-PE slice
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E. 8×PE all-hit-PE0 — every PE reads cube0.pe0_slice (hottest HBM CTRL)
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Effective BW = (total bytes transferred) / (wall-clock time)
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no_congestion: nbytes / total_ns
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congestion: n_issuers × nbytes / makespan_ns (aggregate throughput)
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Peak BW = the path bottleneck (slowest single-edge bandwidth on the
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first issuer's path). For shared-resource congestion scenarios the
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aggregate effective BW can exceed this single-path peak when the
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shared resource provides parallel lanes (e.g. UCIe has 4 connections
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× 128 GB/s = 512 GB/s aggregate even though each connection is 128).
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Utilization% = effective / peak × 100.
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Outputs ``summary.csv`` (including breakdown components for any future
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analysis) so the plot can be re-rendered without re-running the
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simulator.
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"""
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from __future__ import annotations
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import csv
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import math
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from collections import defaultdict
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from dataclasses import dataclass
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from pathlib import Path
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from typing import Iterable
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import matplotlib.pyplot as plt
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from kernbench.policy.address.phyaddr import PhysAddr
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from kernbench.runtime_api.kernel import PeDmaMsg
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from kernbench.sim_engine.engine import GraphEngine
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from kernbench.topology.builder import load_topology
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REPO = Path(__file__).resolve().parent.parent
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TOPOLOGY_PATH = REPO / "topology.yaml"
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OUT_DIR = REPO / "docs" / "diagrams" / "pe_dma_perf"
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DEFAULT_NBYTES = 16 * 1024 # 16 KB per DMA
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# Category order (stacked bottom-to-top) and colours.
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CATEGORIES = [
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("pe_setup", "#3b82f6"), # blue
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("noc_mesh", "#10b981"), # green
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("ucie", "#f59e0b"), # amber
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("fabric", "#8b5cf6"), # purple (switch + io chiplet for cross-SIP)
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("streaming", "#6366f1"), # indigo (bulk = (n_flits-1)/bottleneck)
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("hbm_ctrl", "#ef4444"), # red (final-chunk commit = chunk_time)
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("contention", "#9ca3af"), # grey (actual − formula, surfaces serialization)
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]
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@dataclass
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class Scenario:
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name: str
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label: str
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src_sip: int
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src_cube: int
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src_pe: int
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dst_sip: int
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dst_cube: int
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dst_pe: int
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def _slice_bytes(spec) -> int:
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mm = spec["cube"]["memory_map"]
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return mm["hbm_total_gb_per_cube"] * (1 << 30) // mm["hbm_slices_per_cube"]
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def _hbm_pa(*, sip: int, cube: int, pe_id: int, offset: int, slice_bytes: int) -> int:
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return PhysAddr.pe_hbm_addr(
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sip_id=sip, die_id=cube, pe_id=pe_id,
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pe_local_hbm_offset=offset, slice_size_bytes=slice_bytes,
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).encode()
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def _categorise_node(node) -> str | None:
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nid = node.id
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if ".pe_dma" in nid:
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return "pe_setup"
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if node.kind == "noc_router":
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return "noc_mesh"
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if "ucie" in nid:
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return "ucie"
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if node.kind == "hbm_ctrl":
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return "hbm_ctrl"
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if node.kind in ("switch", "pcie_ep", "io_cpu", "io_noc"):
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return "fabric"
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return None
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def _categorise_edge_kind(kind: str | None) -> str | None:
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if kind in ("pe_to_router", "router_to_pe", "pe_internal"):
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return "pe_setup"
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if kind in ("router_mesh",):
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return "noc_mesh"
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if kind in ("router_to_hbm", "hbm_to_router"):
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return "hbm_ctrl"
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# UCIe transit. Includes the cube↔io_chiplet UCIe crossings.
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if kind and "ucie" in kind:
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return "ucie"
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if kind in ("cube_to_io", "io_to_cube"):
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return "ucie"
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# Cross-SIP fabric: switch port + IO chiplet internal NoC + pcie link.
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if kind in (
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"io_to_switch", "switch_to_io", "io_internal",
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"conn_to_io_noc", "io_noc_to_conn",
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"pcie", "command", "fabric",
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):
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return "fabric"
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return None
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def _bottleneck_bw(path: list[str], edge_map: dict) -> float | None:
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"""Min ``bw_gbs`` over edges with positive bandwidth on the path."""
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bws = [e.bw_gbs for i in range(len(path) - 1)
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if (e := edge_map.get((path[i], path[i + 1]))) and e.bw_gbs]
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return min(bws) if bws else None
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def _path_breakdown(
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path: list[str], nbytes: int, graph, edge_map, ns_per_mm: float,
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) -> dict[str, float]:
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"""Wormhole-pipelined breakdown of a path's expected latency.
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Model:
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total ≈ first_flit_arrival_time
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+ (n_flits - 1) × bottleneck_per_flit_time
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+ last_chunk_commit_time
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Each summand is categorised:
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* Per-component overheads + first-flit wire transfers are attributed
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by component class (pe_setup / noc_mesh / ucie).
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* ``streaming`` is the bulk-transfer cost = (n_flits-1) × per_flit
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at the slowest wire bandwidth in the path.
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* ``hbm_ctrl`` is the HBM CTRL overhead + the final chunk's PC commit
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(= chunk_time). Earlier chunks overlap with arrival.
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"""
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cats: dict[str, float] = defaultdict(float)
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# 1) Per-component overheads (first-flit).
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for nid in path:
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node = graph.nodes.get(nid)
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if node is None:
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continue
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cat = _categorise_node(node)
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if cat is None:
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continue
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cats[cat] += float(node.attrs.get("overhead_ns", 0.0))
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# 2) Per-edge first-flit transfer = prop_ns + flit_bytes / bw_gbs.
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bws: list[float] = []
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flit_bytes = 256 # see ADR-0033 (matches default HBM burst_bytes)
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for i in range(len(path) - 1):
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e = edge_map.get((path[i], path[i + 1]))
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if e is None:
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continue
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prop_ns = e.distance_mm * ns_per_mm
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first_flit_xfer = (flit_bytes / e.bw_gbs) if e.bw_gbs else 0.0
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cat = _categorise_edge_kind(e.kind)
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if cat:
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cats[cat] += prop_ns + first_flit_xfer
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if e.bw_gbs:
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bws.append(e.bw_gbs)
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# 3) Streaming: (n_flits - 1) × per-flit at bottleneck.
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if bws and nbytes > flit_bytes:
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n_flits = math.ceil(nbytes / flit_bytes)
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min_bw = min(bws)
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cats["streaming"] = (n_flits - 1) * (flit_bytes / min_bw)
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# 4) HBM CTRL: last-chunk commit time (earlier chunks overlap arrival).
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if path:
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hbm_node = graph.nodes.get(path[-1])
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if hbm_node and hbm_node.kind == "hbm_ctrl" and nbytes > 0:
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burst = int(hbm_node.attrs.get("burst_bytes", 256))
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pc_bw = float(hbm_node.attrs.get("pc_bw_gbs", 32.0))
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cats["hbm_ctrl"] += burst / pc_bw # chunk_time of final chunk
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return dict(cats)
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# ── No-congestion scenarios ───────────────────────────────────────────
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def _no_congestion_scenarios() -> list[Scenario]:
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return [
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Scenario("local",
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"SAME_CUBE\nPE_LOCAL",
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0, 0, 0, 0, 0, 0),
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Scenario("same_cube_best",
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"SAME_CUBE\nREMOTE_BEST\n(pe0→pe1)",
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0, 0, 0, 0, 0, 1),
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Scenario("same_cube_worst",
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"SAME_CUBE\nREMOTE_WORST\n(pe0→pe7)",
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0, 0, 0, 0, 0, 7),
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Scenario("remote_cube_best",
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"REMOTE_CUBE\nREMOTE_BEST\n(cube0→cube1)",
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0, 0, 0, 0, 1, 0),
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Scenario("remote_cube_worst",
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"REMOTE_CUBE\nREMOTE_WORST\n(cube0→cube15.pe7)",
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0, 0, 0, 0, 15, 7),
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Scenario("remote_sip",
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"REMOTE_SIP\nSAME_CUBE_SAME_PE\n(sip0→sip1)",
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0, 0, 0, 1, 0, 0),
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]
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def _run_pe_dma(engine: GraphEngine, scn: Scenario, nbytes: int,
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slice_bytes: int) -> tuple[float, list[str]]:
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pa = _hbm_pa(sip=scn.dst_sip, cube=scn.dst_cube, pe_id=scn.dst_pe,
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offset=0x1000, slice_bytes=slice_bytes)
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msg = PeDmaMsg(
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correlation_id="pedma-perf", request_id=scn.name,
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src_sip=scn.src_sip, src_cube=scn.src_cube, src_pe=scn.src_pe,
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dst_pa=pa, nbytes=nbytes,
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)
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h = engine.submit(msg)
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engine.wait(h)
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_, trace = engine.get_completion(h)
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# Resolve the path for breakdown analysis (engine doesn't keep it).
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dst_node = engine._resolver.resolve(PhysAddr.decode(pa))
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src = f"sip{scn.src_sip}.cube{scn.src_cube}.pe{scn.src_pe}"
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path = engine._router.find_path(src, dst_node)
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return float(trace["total_ns"]), path
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def _run_no_congestion(nbytes: int):
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graph = load_topology(TOPOLOGY_PATH)
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edge_map = {(e.src, e.dst): e for e in graph.edges}
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ns_per_mm = graph.spec.get("system", {}).get("ns_per_mm", 0.01)
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slice_bytes = _slice_bytes(graph.spec)
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rows = []
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for scn in _no_congestion_scenarios():
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engine = GraphEngine(load_topology(TOPOLOGY_PATH))
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total_ns, path = _run_pe_dma(engine, scn, nbytes, slice_bytes)
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br = _path_breakdown(path, nbytes, graph, edge_map, ns_per_mm)
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formula_sum = sum(br.values())
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br["contention"] = max(0.0, total_ns - formula_sum)
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peak_bw = _bottleneck_bw(path, edge_map) or 0.0
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eff_bw = nbytes / total_ns if total_ns > 0 else 0.0
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util = (eff_bw / peak_bw * 100.0) if peak_bw > 0 else 0.0
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rows.append({
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"graph": "no_congestion",
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"scenario": scn.name,
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"label": scn.label,
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"nbytes": nbytes,
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"n_issuers": 1,
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"path": " -> ".join(_short_path(path)),
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"total_ns": total_ns,
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"bottleneck_bw_gbs": peak_bw,
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"effective_bw_gbs": eff_bw,
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"util_pct": util,
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**{c: br.get(c, 0.0) for c, _ in CATEGORIES},
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})
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return rows
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# ── Congestion scenarios ──────────────────────────────────────────────
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@dataclass
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class CongestionScenario:
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name: str
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label: str
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issues: list[tuple[int, int, int, int, int, int]]
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"""List of (src_sip, src_cube, src_pe, dst_sip, dst_cube, dst_pe)."""
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def _congestion_scenarios() -> list[CongestionScenario]:
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same_cube_same_target_pe0 = lambda srcs: [
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(0, 0, p, 0, 0, 0) for p in srcs
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]
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return [
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# A-C: 1, 2, 3 remote PEs concurrently access pe0's slice in same cube
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CongestionScenario(
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"ctrl_hot_1",
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"1×PE → pe0_slice",
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same_cube_same_target_pe0([1]),
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),
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CongestionScenario(
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"ctrl_hot_2",
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"2×PE → pe0_slice",
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same_cube_same_target_pe0([1, 2]),
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),
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CongestionScenario(
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"ctrl_hot_3",
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"3×PE → pe0_slice",
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same_cube_same_target_pe0([1, 2, 3]),
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),
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# D: every PE in cube0 sends to corresponding PE in cube1 (same UCIe direction)
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CongestionScenario(
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"ucie_eastbound",
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"8×PE corresp.\ncube0→cube1",
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[(0, 0, p, 0, 1, p) for p in range(8)],
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),
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# E: every PE in cube0 hits pe0's slice → worst HBM CTRL hotspot
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CongestionScenario(
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"all_pe_to_pe0",
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"8×PE → pe0_slice",
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same_cube_same_target_pe0(list(range(8))),
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),
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]
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def _run_congestion(nbytes: int):
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graph = load_topology(TOPOLOGY_PATH)
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edge_map = {(e.src, e.dst): e for e in graph.edges}
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ns_per_mm = graph.spec.get("system", {}).get("ns_per_mm", 0.01)
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slice_bytes = _slice_bytes(graph.spec)
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rows = []
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for scn in _congestion_scenarios():
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engine = GraphEngine(load_topology(TOPOLOGY_PATH))
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handles = []
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first_path = None
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for i, (ss, sc, sp, ds, dc, dp) in enumerate(scn.issues):
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pa = _hbm_pa(sip=ds, cube=dc, pe_id=dp,
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offset=0x1000 + i * 0x100, slice_bytes=slice_bytes)
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msg = PeDmaMsg(
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correlation_id="pedma-cong", request_id=f"{scn.name}-{i}",
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src_sip=ss, src_cube=sc, src_pe=sp,
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dst_pa=pa, nbytes=nbytes,
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)
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handles.append(engine.submit(msg))
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if first_path is None:
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dst_node = engine._resolver.resolve(PhysAddr.decode(pa))
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first_path = engine._router.find_path(
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f"sip{ss}.cube{sc}.pe{sp}", dst_node)
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for h in handles:
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engine.wait(h)
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latencies = [engine.get_completion(h)[1]["total_ns"] for h in handles]
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makespan = max(latencies)
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# Breakdown uses the first issuer's path as a representative;
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# ``contention`` absorbs serialization across requests.
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br = _path_breakdown(first_path or [], nbytes, graph, edge_map, ns_per_mm)
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formula_sum = sum(br.values())
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br["contention"] = max(0.0, makespan - formula_sum)
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peak_bw = (_bottleneck_bw(first_path or [], edge_map) or 0.0)
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total_bytes = nbytes * len(scn.issues)
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eff_bw = total_bytes / makespan if makespan > 0 else 0.0
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util = (eff_bw / peak_bw * 100.0) if peak_bw > 0 else 0.0
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rows.append({
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"graph": "congestion",
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"scenario": scn.name,
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"label": scn.label,
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"nbytes": nbytes,
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"n_issuers": len(scn.issues),
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"first_path": " -> ".join(_short_path(first_path or [])),
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"makespan_ns": makespan,
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"min_lat_ns": min(latencies) if latencies else 0.0,
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"bottleneck_bw_gbs": peak_bw,
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"effective_bw_gbs": eff_bw,
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"util_pct": util,
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**{c: br.get(c, 0.0) for c, _ in CATEGORIES},
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})
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return rows
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# ── Plotting ───────────────────────────────────────────────────────────
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def _short_path(path: Iterable[str]) -> list[str]:
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return [".".join(p.split(".")[-2:]) for p in path]
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def _plot_bw_utilization(rows, title, out_path):
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"""Plot Effective BW utilization (%) per scenario.
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Each bar is util_pct = effective_bw / peak_bottleneck_bw × 100.
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Annotation shows effective and peak in GB/s. A horizontal dashed
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line marks 100 % (single-path peak); bars exceeding it indicate
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the scenario uses multiple parallel resources (e.g. UCIe's 4
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connections) beyond the bottleneck of any single path.
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"""
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n = len(rows)
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labels = [r["label"] for r in rows]
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util = [r.get("util_pct", 0.0) for r in rows]
|
||
eff = [r.get("effective_bw_gbs", 0.0) for r in rows]
|
||
peak = [r.get("bottleneck_bw_gbs", 0.0) for r in rows]
|
||
|
||
fig, ax = plt.subplots(figsize=(max(8, n * 1.4), 5.5))
|
||
# Colour bars by utilization band for quick scanning.
|
||
colours = ["#10b981" if u >= 70 else "#f59e0b" if u >= 40 else "#ef4444"
|
||
for u in util]
|
||
ax.bar(labels, util, color=colours, edgecolor="white", linewidth=0.5)
|
||
ax.axhline(100.0, color="grey", linestyle="--", linewidth=0.8,
|
||
label="single-path peak")
|
||
|
||
# Annotate each bar with util%, effective, and peak.
|
||
y_max = max(util + [100.0]) * 1.2
|
||
for i, (u, e, p) in enumerate(zip(util, eff, peak)):
|
||
ax.text(i, u + y_max * 0.012,
|
||
f"{u:.1f}%\n{e:.0f} / {p:.0f} GB/s",
|
||
ha="center", va="bottom", fontsize=8)
|
||
|
||
ax.set_ylabel("Effective BW utilization (%)")
|
||
ax.set_title(title)
|
||
ax.set_ylim(0, y_max)
|
||
ax.tick_params(axis="x", labelsize=8)
|
||
ax.legend(loc="upper right", fontsize=9, frameon=False)
|
||
fig.tight_layout()
|
||
fig.savefig(out_path, dpi=150)
|
||
plt.close(fig)
|
||
|
||
|
||
# ── CSV ────────────────────────────────────────────────────────────────
|
||
|
||
|
||
def _write_csv(no_cong_rows, cong_rows, out_path):
|
||
fields = [
|
||
"graph", "scenario", "label", "nbytes", "n_issuers",
|
||
"total_ns", "makespan_ns", "min_lat_ns",
|
||
"bottleneck_bw_gbs", "effective_bw_gbs", "util_pct",
|
||
"pe_setup", "noc_mesh", "ucie", "fabric", "streaming",
|
||
"hbm_ctrl", "contention",
|
||
"path", "first_path",
|
||
]
|
||
with open(out_path, "w", newline="") as f:
|
||
w = csv.DictWriter(f, fieldnames=fields, extrasaction="ignore")
|
||
w.writeheader()
|
||
for r in no_cong_rows + cong_rows:
|
||
w.writerow(r)
|
||
|
||
|
||
# ── Self-verification ──────────────────────────────────────────────────
|
||
|
||
|
||
def _verify(rows_no_cong, rows_cong) -> list[str]:
|
||
"""Return a list of human-readable issues; empty means PASS.
|
||
|
||
BW-utilization invariants:
|
||
(1) No-congestion: effective BW shrinks as topological distance grows.
|
||
(2) Per-row utilisation is in (0, 250] %; values above 100 % are only
|
||
allowed when the path bottleneck is a SHARED resource with
|
||
parallel lanes (UCIe per-conn × 4) and aggregate transfer
|
||
exploits those lanes.
|
||
(3) Single-issuer utilisation cannot exceed 100 %.
|
||
(4) Effective BW for a single request equals nbytes / latency.
|
||
(5) Congestion aggregate BW grows monotonically with issuer count
|
||
on the hot-target series (more bytes / same wall-clock peak).
|
||
(6) 8-PE all-hit-pe0 aggregate must approach the path bottleneck
|
||
(≥ 70 % util) — the shared bottleneck is fully amortised.
|
||
"""
|
||
issues = []
|
||
by_name = {r["scenario"]: r for r in rows_no_cong}
|
||
cong_map = {r["scenario"]: r for r in rows_cong}
|
||
|
||
# (1) No-congestion effective BW shrinks as distance grows
|
||
order = [
|
||
"local",
|
||
"same_cube_best",
|
||
"same_cube_worst",
|
||
"remote_cube_best",
|
||
"remote_cube_worst",
|
||
]
|
||
prev_bw = float("inf")
|
||
for n in order:
|
||
if n in by_name and by_name[n]["effective_bw_gbs"] >= prev_bw:
|
||
issues.append(
|
||
f"no_congestion: {n} effective BW "
|
||
f"({by_name[n]['effective_bw_gbs']:.1f} GB/s) not strictly "
|
||
f"smaller than previous ({prev_bw:.1f})"
|
||
)
|
||
prev_bw = min(prev_bw, by_name.get(n, {}).get("effective_bw_gbs", prev_bw))
|
||
|
||
# (2) Utilisation in (0, 250 %]; values > 100 only allowed on shared
|
||
# multi-lane resources (UCIe per_conn × 4 → 4-fold parallelism).
|
||
for r in rows_no_cong + rows_cong:
|
||
u = r.get("util_pct", 0.0)
|
||
if u <= 0:
|
||
issues.append(f"{r['scenario']}: non-positive util_pct={u}")
|
||
if u > 250:
|
||
issues.append(
|
||
f"{r['scenario']}: util_pct={u:.1f}% exceeds 250 % — "
|
||
f"likely a peak-BW or effective-BW miscompute"
|
||
)
|
||
|
||
# (3) Single-issuer utilisation cannot exceed 100 %.
|
||
for r in rows_no_cong:
|
||
u = r.get("util_pct", 0.0)
|
||
if u > 100.0 + 1e-3:
|
||
issues.append(
|
||
f"no_congestion {r['scenario']}: util_pct={u:.1f}% > 100% "
|
||
f"for single-issuer scenario (eff={r['effective_bw_gbs']:.1f}, "
|
||
f"peak={r['bottleneck_bw_gbs']:.1f})"
|
||
)
|
||
|
||
# (4) Effective BW for a single request = nbytes / total_ns
|
||
for r in rows_no_cong:
|
||
expected = r["nbytes"] / r["total_ns"] if r["total_ns"] > 0 else 0
|
||
got = r["effective_bw_gbs"]
|
||
if abs(got - expected) > 1e-3:
|
||
issues.append(
|
||
f"no_congestion {r['scenario']}: eff_bw={got:.3f} != "
|
||
f"nbytes/total_ns={expected:.3f}"
|
||
)
|
||
|
||
# (5) Congestion aggregate BW grows monotonically with issuer count on
|
||
# the hot-target series (same shared bottleneck, more bytes / same peak).
|
||
seq = ["ctrl_hot_1", "ctrl_hot_2", "ctrl_hot_3"]
|
||
last = 0.0
|
||
for n in seq:
|
||
if n in cong_map and cong_map[n]["effective_bw_gbs"] < last - 1e-6:
|
||
issues.append(
|
||
f"congestion: {n} aggregate BW dropped below prior "
|
||
f"({cong_map[n]['effective_bw_gbs']:.1f} < {last:.1f})"
|
||
)
|
||
last = max(last, cong_map.get(n, {}).get("effective_bw_gbs", last))
|
||
|
||
# (6) all_pe_to_pe0 must approach single-path peak (≥ 70 % util) —
|
||
# the shared r0c0 → hbm_ctrl.pe0 bottleneck is fully amortised when
|
||
# all 8 PEs target it.
|
||
if "all_pe_to_pe0" in cong_map:
|
||
u = cong_map["all_pe_to_pe0"]["util_pct"]
|
||
if u < 70.0:
|
||
issues.append(
|
||
f"congestion all_pe_to_pe0: util_pct={u:.1f}% < 70 % — "
|
||
f"8-PE hotspot should saturate the shared HBM CTRL path"
|
||
)
|
||
|
||
return issues
|
||
|
||
|
||
# ── Entry point ────────────────────────────────────────────────────────
|
||
|
||
|
||
def main(nbytes: int = DEFAULT_NBYTES) -> int:
|
||
OUT_DIR.mkdir(parents=True, exist_ok=True)
|
||
|
||
print(f"== PE_DMA perf @ {nbytes} B per request ==")
|
||
print("Collecting NO-congestion scenarios...")
|
||
no_cong = _run_no_congestion(nbytes)
|
||
print("Collecting CONGESTION scenarios...")
|
||
cong = _run_congestion(nbytes)
|
||
|
||
print("\n-- No-congestion summary --")
|
||
for r in no_cong:
|
||
print(f" {r['scenario']:22s} total={r['total_ns']:7.1f} ns "
|
||
f"eff={r['effective_bw_gbs']:6.1f} peak={r['bottleneck_bw_gbs']:6.1f} "
|
||
f"GB/s util={r['util_pct']:5.1f}%")
|
||
print("\n-- Congestion summary --")
|
||
for r in cong:
|
||
agg_bytes = r["nbytes"] * r["n_issuers"]
|
||
print(f" {r['scenario']:22s} makespan={r['makespan_ns']:7.1f} ns "
|
||
f"agg_bytes={agg_bytes:>7d} "
|
||
f"eff={r['effective_bw_gbs']:6.1f} peak={r['bottleneck_bw_gbs']:6.1f} "
|
||
f"GB/s util={r['util_pct']:5.1f}%")
|
||
|
||
issues = _verify(no_cong, cong)
|
||
print("\n-- Self-verification --")
|
||
if not issues:
|
||
print(" PASS")
|
||
else:
|
||
for i, msg in enumerate(issues, 1):
|
||
print(f" [{i}] {msg}")
|
||
|
||
_plot_bw_utilization(
|
||
no_cong,
|
||
f"PE_DMA Effective BW utilization (no congestion, nbytes={nbytes})",
|
||
OUT_DIR / "no_congestion.png",
|
||
)
|
||
_plot_bw_utilization(
|
||
cong,
|
||
f"PE_DMA Effective BW utilization (congestion, "
|
||
f"agg = n_issuers × nbytes / makespan, nbytes={nbytes})",
|
||
OUT_DIR / "congestion.png",
|
||
)
|
||
_write_csv(no_cong, cong, OUT_DIR / "summary.csv")
|
||
|
||
print(f"\nWrote:\n {OUT_DIR / 'no_congestion.png'}\n"
|
||
f" {OUT_DIR / 'congestion.png'}\n"
|
||
f" {OUT_DIR / 'summary.csv'}")
|
||
|
||
return 0 if not issues else 1
|
||
|
||
|
||
if __name__ == "__main__":
|
||
import argparse
|
||
|
||
p = argparse.ArgumentParser()
|
||
p.add_argument("--n-bytes", type=int, default=DEFAULT_NBYTES,
|
||
help="bytes per DMA (default 16384)")
|
||
args = p.parse_args()
|
||
raise SystemExit(main(nbytes=args.n_bytes))
|