mukesh
a563169e89
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>
142 lines
4.1 KiB
Python
142 lines
4.1 KiB
Python
"""Re-render pe2pe latency PNGs from the existing summary.csv with the
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current (no-consume) labels. Used after a label-only test edit to avoid
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re-measuring (~5 min) when the data on disk is already correct.
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Reads docs/diagrams/pe2pe_latency_plots/summary.csv. Plots 2 curves:
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"IPCQ no-consume" (from the ipcq_no_consume rows if present, else from
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the ipcq rows) and "Raw DMA" (raw rows).
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"""
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from __future__ import annotations
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import csv
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from pathlib import Path
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import matplotlib.pyplot as plt
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ROOT = Path(__file__).resolve().parent.parent
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PLOT_DIR = ROOT / "docs" / "diagrams" / "pe2pe_latency_plots"
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CSV_PATH = PLOT_DIR / "summary.csv"
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def _load_records():
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rows = []
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with open(CSV_PATH, newline="") as f:
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for r in csv.DictReader(f):
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rows.append({
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"hop": r["hop"],
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"label": r["label"],
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"size_bytes": int(r["size_bytes"]),
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"path": r["path"],
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"total_ns": float(r["total_ns"]),
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})
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return rows
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def _ipcq_rows(records, hop):
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# Prefer ipcq_no_consume if present (older 3-path CSV); fall back to ipcq
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# (current single-path CSV where ipcq IS no-consume).
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nc = [r for r in records
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if r["hop"] == hop and r["path"] == "ipcq_no_consume"]
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if nc:
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return sorted(nc, key=lambda r: r["size_bytes"])
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return sorted(
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[r for r in records if r["hop"] == hop and r["path"] == "ipcq"],
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key=lambda r: r["size_bytes"],
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)
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def _raw_rows(records, hop):
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return sorted(
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[r for r in records if r["hop"] == hop and r["path"] == "raw"],
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key=lambda r: r["size_bytes"],
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)
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def _hops(records):
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seen = []
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for r in records:
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if r["hop"] not in {h["id"] for h in seen}:
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seen.append({"id": r["hop"], "label": r["label"]})
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return seen
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def _plot_per_hop(records, hop, path):
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ipcq = _ipcq_rows(records, hop["id"])
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raw = _raw_rows(records, hop["id"])
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fig, ax = plt.subplots(figsize=(8, 5))
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if ipcq:
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ax.plot(
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[r["size_bytes"] for r in ipcq],
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[r["total_ns"] for r in ipcq],
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marker="o", color="tab:blue",
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label="IPCQ no-consume (send/recv, no slot read)",
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)
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if raw:
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ax.plot(
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[r["size_bytes"] for r in raw],
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[r["total_ns"] for r in raw],
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marker="s", color="tab:orange",
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label="Raw DMA (load+store)",
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)
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ax.set_xlabel("Data size (bytes)")
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ax.set_ylabel("Latency (ns)")
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ax.set_title(hop["label"])
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ax.grid(True, alpha=0.3)
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ax.legend()
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fig.tight_layout()
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fig.savefig(path, dpi=120)
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plt.close(fig)
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def _plot_overview(records, hops, path):
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fig, axes = plt.subplots(2, 2, figsize=(13, 9))
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axes = axes.flatten()
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for i, hop in enumerate(hops):
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ax = axes[i]
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ipcq = _ipcq_rows(records, hop["id"])
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raw = _raw_rows(records, hop["id"])
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if ipcq:
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ax.plot(
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[r["size_bytes"] for r in ipcq],
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[r["total_ns"] for r in ipcq],
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marker="o", color="tab:blue",
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label="IPCQ no-consume",
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)
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if raw:
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ax.plot(
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[r["size_bytes"] for r in raw],
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[r["total_ns"] for r in raw],
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marker="s", color="tab:orange",
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label="Raw DMA",
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)
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ax.set_title(hop["label"], fontsize=10)
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ax.set_xlabel("bytes")
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ax.set_ylabel("ns")
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ax.grid(True, alpha=0.3)
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ax.legend(fontsize=8)
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for j in range(len(hops), len(axes)):
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axes[j].axis("off")
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fig.suptitle(
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"PE-to-PE latency: IPCQ no-consume vs raw DMA",
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fontsize=14,
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)
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fig.tight_layout()
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fig.savefig(path, dpi=120)
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plt.close(fig)
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def main():
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records = _load_records()
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hops = _hops(records)
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for hop in hops:
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out = PLOT_DIR / f"{hop['id']}.png"
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_plot_per_hop(records, hop, out)
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print(f"wrote {out}")
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overview = PLOT_DIR / "overview.png"
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_plot_overview(records, hops, overview)
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print(f"wrote {overview}")
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if __name__ == "__main__":
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main()
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