ywkang
a759d58007
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>
603 lines
23 KiB
Python
603 lines
23 KiB
Python
"""Plot PE_DMA performance: latency breakdown 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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Latency is broken down by component class:
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pe_setup — first-flit PE_DMA overhead + PE↔router wire transfer
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noc_mesh — mesh routers' first-flit overheads + mesh wire transfers
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ucie — UCIe ports' first-flit overheads + UCIe wire transfers
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streaming — (n_flits-1) × per-flit time at the bottleneck link
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(the dominant term for bulk transfers, set by the slowest wire)
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hbm_ctrl — HBM CTRL overhead + final-chunk PC commit (= chunk_time)
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fabric — switch + IO chiplet overheads + wires (cross-SIP paths)
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contention — actual − formula_sum; primary signal for the congestion
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graph (serialization across concurrent issuers) and a
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model-fidelity probe for single-request scenarios
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Outputs ``summary.csv`` so the plot can be re-rendered without re-running
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the simulator (the heavy step).
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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 _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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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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"path": " -> ".join(_short_path(path)),
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"total_ns": total_ns,
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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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# ``unaccounted`` absorbs contention/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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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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**{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_stacked(rows, value_key, title, out_path):
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n = len(rows)
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labels = [r["label"] for r in rows]
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fig, ax = plt.subplots(figsize=(max(8, n * 1.4), 5.5))
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bottoms = [0.0] * n
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for cat, colour in CATEGORIES:
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heights = [r.get(cat, 0.0) for r in rows]
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ax.bar(labels, heights, bottom=bottoms, color=colour, label=cat,
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edgecolor="white", linewidth=0.5)
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bottoms = [b + h for b, h in zip(bottoms, heights)]
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# Total annotation on top of each bar.
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for i, r in enumerate(rows):
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ax.text(i, bottoms[i] * 1.01, f"{r[value_key]:.0f} ns",
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ha="center", va="bottom", fontsize=8)
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ax.set_ylabel("Latency (ns)")
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ax.set_title(title)
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ax.legend(loc="upper left", fontsize=9, frameon=False)
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ax.set_ylim(0, max(bottoms) * 1.15)
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ax.tick_params(axis="x", labelsize=8)
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fig.tight_layout()
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fig.savefig(out_path, dpi=150)
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plt.close(fig)
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# ── CSV ────────────────────────────────────────────────────────────────
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def _write_csv(no_cong_rows, cong_rows, out_path):
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fields = [
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||
"graph", "scenario", "label", "nbytes", "n_issuers",
|
||
"total_ns", "makespan_ns", "min_lat_ns",
|
||
"pe_setup", "noc_mesh", "ucie", "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.
|
||
|
||
Verification covers:
|
||
(1) No-congestion: latency monotonically grows with topological distance.
|
||
(2) Same-cube scenarios contain zero UCIe budget (mesh-only path).
|
||
(3) Remote-cube/SIP scenarios contain non-zero UCIe budget.
|
||
(4) Breakdown is internally consistent: formula sum ≤ actual total
|
||
(categories don't overcount the pipelined model) and the
|
||
``contention`` slack is < 50% of total for single-request
|
||
scenarios (the named categories explain most latency).
|
||
(5) Streaming term matches nbytes / bottleneck within 5%.
|
||
(6) Congestion makespan grows with issuer count on the hot-target series.
|
||
(7) 8-PE hotspot strictly exceeds 3-PE hotspot.
|
||
"""
|
||
issues = []
|
||
by_name = {r["scenario"]: r for r in rows_no_cong}
|
||
cong_map = {r["scenario"]: r for r in rows_cong}
|
||
|
||
# (1) distance monotonicity
|
||
order = [
|
||
"local",
|
||
"same_cube_best",
|
||
"same_cube_worst",
|
||
"remote_cube_best",
|
||
"remote_cube_worst",
|
||
]
|
||
prev = 0.0
|
||
for n in order:
|
||
if n in by_name and by_name[n]["total_ns"] <= prev:
|
||
issues.append(
|
||
f"no_congestion: {n} latency ({by_name[n]['total_ns']:.1f} ns) "
|
||
f"not strictly > previous scenario ({prev:.1f} ns)"
|
||
)
|
||
prev = max(prev, by_name.get(n, {}).get("total_ns", prev))
|
||
|
||
if "remote_sip" in by_name and "remote_cube_best" in by_name:
|
||
if by_name["remote_sip"]["total_ns"] < by_name["remote_cube_best"]["total_ns"]:
|
||
issues.append(
|
||
f"no_congestion: remote_sip ({by_name['remote_sip']['total_ns']:.1f}) "
|
||
f"< remote_cube_best ({by_name['remote_cube_best']['total_ns']:.1f})"
|
||
)
|
||
|
||
# (2) same-cube → ucie == 0
|
||
for n in ("local", "same_cube_best", "same_cube_worst"):
|
||
if by_name.get(n, {}).get("ucie", 1) != 0:
|
||
issues.append(
|
||
f"no_congestion: {n} should have zero UCIe budget; "
|
||
f"got {by_name[n]['ucie']}"
|
||
)
|
||
|
||
# (3) remote-cube / remote-sip → ucie > 0
|
||
for n in ("remote_cube_best", "remote_cube_worst", "remote_sip"):
|
||
if by_name.get(n, {}).get("ucie", 0) <= 0:
|
||
issues.append(
|
||
f"no_congestion: {n} must have positive UCIe budget; "
|
||
f"got {by_name[n].get('ucie')}"
|
||
)
|
||
|
||
# (4) breakdown consistency
|
||
for r in rows_no_cong + rows_cong:
|
||
actual = r.get("total_ns", r.get("makespan_ns", 0.0))
|
||
if actual <= 0:
|
||
continue
|
||
for cat, _ in CATEGORIES:
|
||
if r.get(cat, 0.0) < 0:
|
||
issues.append(f"{r['scenario']}: negative {cat}={r[cat]}")
|
||
formula_sum = sum(r.get(c, 0.0) for c, _ in CATEGORIES
|
||
if c != "contention")
|
||
if formula_sum > actual + 1e-3:
|
||
issues.append(
|
||
f"{r['scenario']}: formula sum {formula_sum:.1f} exceeds "
|
||
f"actual {actual:.1f} (categories overcount pipelined model)"
|
||
)
|
||
# For single-request scenarios the named categories must explain
|
||
# most of the latency. Cross-SIP paths cross two non-flit-aware
|
||
# boundaries (sip0.pcie_ep -> switch -> sip1.pcie_ep) which force
|
||
# store-and-forward re-streaming that the simple wormhole formula
|
||
# under-counts; allow a looser threshold for those rows. For
|
||
# congestion scenarios ``contention`` IS the primary signal, so
|
||
# don't bound its share — directional invariants in checks (6)
|
||
# and (7) cover that.
|
||
path_str = r.get("path") or r.get("first_path", "")
|
||
cross_sip = "switch0" in path_str
|
||
max_cont_frac = 0.7 if cross_sip else 0.5
|
||
if r.get("graph") == "no_congestion":
|
||
cont_frac = r.get("contention", 0.0) / actual
|
||
if cont_frac > max_cont_frac:
|
||
issues.append(
|
||
f"{r['scenario']}: contention fraction {cont_frac:.1%} > "
|
||
f"{max_cont_frac:.0%} in a single-request scenario — named "
|
||
f"categories should explain most latency "
|
||
f"(actual={actual:.1f}, cont={r['contention']:.1f})"
|
||
)
|
||
|
||
# (5) streaming matches nbytes / bottleneck within slack
|
||
# nbytes / bottleneck for local (256 GB/s) at 16 KB = 64ns (off by per-flit gap)
|
||
if "local" in by_name:
|
||
n = by_name["local"]
|
||
nbytes = n["nbytes"]
|
||
# streaming = (n_flits-1) * (256 / 256_gbs) for 256 GB/s = (n_flits-1) ns
|
||
n_flits = math.ceil(nbytes / 256)
|
||
expected = (n_flits - 1) * (256 / 256.0) # 256 GB/s pe→router bottleneck
|
||
got = n.get("streaming", 0)
|
||
if abs(got - expected) > expected * 0.05 + 0.5:
|
||
issues.append(
|
||
f"no_congestion local: streaming={got:.1f} vs expected≈{expected:.1f}"
|
||
)
|
||
|
||
# (6) congestion makespan monotonic with issuer count
|
||
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]["makespan_ns"] < last:
|
||
issues.append(
|
||
f"congestion: {n} makespan dropped below prior "
|
||
f"({cong_map[n]['makespan_ns']:.1f} < {last:.1f})"
|
||
)
|
||
last = cong_map.get(n, {}).get("makespan_ns", last)
|
||
|
||
# (7) 8-PE hotspot strictly slower than 3-PE
|
||
if "all_pe_to_pe0" in cong_map and "ctrl_hot_3" in cong_map:
|
||
if cong_map["all_pe_to_pe0"]["makespan_ns"] <= cong_map["ctrl_hot_3"]["makespan_ns"]:
|
||
issues.append(
|
||
f"congestion: all_pe_to_pe0 ({cong_map['all_pe_to_pe0']['makespan_ns']:.1f}) "
|
||
f"should exceed ctrl_hot_3 "
|
||
f"({cong_map['ctrl_hot_3']['makespan_ns']:.1f})"
|
||
)
|
||
|
||
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"pe={r['pe_setup']:.1f} mesh={r['noc_mesh']:.1f} "
|
||
f"ucie={r['ucie']:.1f} stream={r['streaming']:.1f} "
|
||
f"hbm={r['hbm_ctrl']:.1f} cont={r['contention']:.1f}")
|
||
print("\n-- Congestion summary --")
|
||
for r in cong:
|
||
print(f" {r['scenario']:22s} makespan={r['makespan_ns']:7.1f} ns "
|
||
f"min={r['min_lat_ns']:7.1f} "
|
||
f"pe={r['pe_setup']:.1f} mesh={r['noc_mesh']:.1f} "
|
||
f"ucie={r['ucie']:.1f} stream={r['streaming']:.1f} "
|
||
f"hbm={r['hbm_ctrl']:.1f} cont={r['contention']:.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_stacked(
|
||
no_cong, "total_ns",
|
||
f"PE_DMA latency breakdown (no congestion, nbytes={nbytes})",
|
||
OUT_DIR / "no_congestion.png",
|
||
)
|
||
_plot_stacked(
|
||
cong, "makespan_ns",
|
||
f"PE_DMA latency breakdown (congestion, 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))
|