Performance: pcolormesh PNG export misses the 10× Matplotlib target (5.54× in release audit)

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Assessment

Difficulty
5/5
Estimated time
Over a week
Newbie friendliness
30/100
Issue type
Bug
Clarity
Clearly specified
Activity status
Active
Tech stack
matplotlib, numpy, python, rust
Domain
backend, data-visualization, performance

Research direction

Start with benchmarks/bench_pyplot_vs_matplotlib.py and spec/benchmarks/methodology.md; first apply the described DPI correction to the XY save call, then reproduce the pcolormesh row on a controlled runner and capture dimensions and raw samples. Profile build, rasterization, and PNG encoding to identify the measured bottleneck. Done means meeting the 10× standard target with image correctness preserved, or revising the specification with reproducible evidence; record profile results and add a regression benchmark.

Written by the indexing model from the issue text.

Description

The matched-output release benchmark found a pcolormesh export gap against the existing per-family 10× Matplotlib/Agg goal in the benchmark methodology.

Workload XY total median Matplotlib total median Speedup XY tier
80 x 120 cells 14.96 ms 82.85 ms 5.54× direct

Both outputs are nonblank 1800×840 PNGs. The metric is figure construction through a completed compressed PNG, excluding imports, data generation and warm-up. XY renders this row directly. Preserve mesh geometry, normalization, colorbar behavior and seam-free cell fills.

These are diagnostic results from three repetitions on a shared workstation, with one warm-up, recorded during the 2026-11-05 release audit. Confirm the gap on a controlled runner before selecting an optimization. This does not establish a regression from v0.0.8.

Reproduce

Audited source: 5e58b12c0a1b89a58c4555af370204ef1ed0515e plus local release-validation fixes. Environment: Linux x86_64, 12 logical CPUs, CPython 3.14.6, NumPy 2.5.1, Matplotlib 3.11.0, native XY core, Rust 1.96.0.

Harness prerequisite: the audited upstream bench_pyplot_vs_matplotlib.py omits export DPI in _xy_render. Set its save call to the following before measuring, matching the Matplotlib adapter:

fig.savefig(out, format="png", dpi=EXPORT_DPI)

Both adapters build at 100 DPI and export at 200 DPI. Without that correction, the harness fails its dimension check before producing a valid comparison. The correction and output-size regression test are prepared locally on farhan/release-validation, but have not landed upstream.

cargo build --release
uv sync --extra matplotlib --group dev
uv pip install 'matplotlib==3.11.0'
MPLBACKEND=Agg uv run --no-sync python benchmarks/bench_pyplot_vs_matplotlib.py \
  --profile smoke --reps 3 --warmups 1 \
  --json /tmp/pyplot-smoke.json

The harness runs all six families; inspect the pcolormesh row. For confirmation and the existing standard target gate:

MPLBACKEND=Agg uv run --no-sync python benchmarks/bench_pyplot_vs_matplotlib.py \
  --profile standard --reps 21 --warmups 3 \
  --target-speedup 10 --require-target \
  --json /tmp/pyplot-standard.json

--require-target gates every family, so a failure in another family does not by itself establish this issue's outcome.

Raw observations
[
  {
    "library": "xy.pyplot",
    "total_samples_ms": [
      15.01132,
      14.961026,
      14.659394
    ],
    "build_median_ms": 0.396198,
    "render_median_ms": 14.516297,
    "total_median_ms": 14.961026,
    "total_p95_ms": 15.01132,
    "png_bytes": 276368
  },
  {
    "library": "matplotlib",
    "total_samples_ms": [
      82.847815,
      80.768711,
      86.329222
    ],
    "build_median_ms": 6.175336,
    "render_median_ms": 76.659255,
    "total_median_ms": 82.847815,
    "total_p95_ms": 86.329222,
    "png_bytes": 55310
  }
]
Acceptance
  • Reproduce on a controlled runner and record environment, exact commit, raw samples, output dimensions and tier decisions.
  • Profile build, rasterization and PNG encoding separately; optimize the measured bottleneck.
  • Meet the existing standard-profile 10× total-time target for pcolormesh, or explicitly revise the specification with reproducible evidence explaining why the target is not met.
  • Retain image correctness, labels and dimensions; do not achieve the target by silently reducing visual quality or dropping data.
  • Record smoke, standard and huge profile results and output sizes, and add a meaningful regression benchmark.
  • Update benchmark evidence and specifications with the resulting behavior.

The local audit report and complete raw comparison are prepared under spec/process/release-validation/2026-11-05/; they are currently uncommitted. The observations above are included here so this issue is reproducible without access to that worktree.

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