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Handle legacy nvbench-compare timing summaries

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Derive absolute standard deviation from relative stdev and mean when
nv/cold/time/gpu/stdev/absolute is absent. This lets older JSON files
that only contain mean and relative stdev still construct timing
intervals.

Also allow mean/stdev intervals to be built without min/max summaries,
using min/max only as optional clipping bounds when present. This
restores SAME classification for legacy fixture data instead of treating
those rows as missing-interval AMBG cases.

Update nvbench_compare tests to cover derived stdev handling and the
legacy mean/stdev comparison path.
This commit is contained in:
Oleksandr Pavlyk
2026-06-29 05:29:02 -05:00
parent 5e609dab78
commit 6dae814da5
2 changed files with 69 additions and 35 deletions
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46
python/scripts/nvbench_compare.py
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@@ -990,14 +990,20 @@ def extract_gpu_timing_data(summaries, json_dir=None, float32_reader=read_float3
sample_source = None
frequency_source = None
mean = extract_summary_float(summaries, GPU_TIME_MEAN_TAG)
stdev = extract_summary_float(summaries, GPU_TIME_STDEV_TAG, null_value=math.inf)
stdev_relative = extract_summary_float(
summaries, GPU_TIME_STDEV_RELATIVE_TAG, null_value=math.inf
)
if stdev is None:
stdev = derive_absolute_dispersion(stdev_relative, mean)
return GpuTimingData(
minimum=extract_summary_float(summaries, GPU_TIME_MIN_TAG),
maximum=extract_summary_float(summaries, GPU_TIME_MAX_TAG),
mean=extract_summary_float(summaries, GPU_TIME_MEAN_TAG),
stdev=extract_summary_float(summaries, GPU_TIME_STDEV_TAG, null_value=math.inf),
stdev_relative=extract_summary_float(
summaries, GPU_TIME_STDEV_RELATIVE_TAG, null_value=math.inf
),
mean=mean,
stdev=stdev,
stdev_relative=stdev_relative,
first_quartile=extract_summary_float(summaries, GPU_TIME_Q1_TAG),
median=extract_summary_float(summaries, GPU_TIME_MEDIAN_TAG),
third_quartile=extract_summary_float(summaries, GPU_TIME_Q3_TAG),
@@ -1192,6 +1198,12 @@ def is_nonnegative_finite(value):
return value is not None and math.isfinite(value) and value >= 0.0
def derive_absolute_dispersion(relative_dispersion, center):
if is_nonnegative_finite(relative_dispersion) and is_positive_finite(center):
return relative_dispersion * center
return None
def parse_plot_axis_value(axis_name, axis_value):
try:
value = float(axis_value)
@@ -1240,21 +1252,17 @@ def compute_robust_summary_interval(timing):
def compute_mean_summary_interval(timing):
if (
is_positive_finite(timing.minimum)
and is_positive_finite(timing.maximum)
and is_positive_finite(timing.mean)
and is_nonnegative_finite(timing.stdev)
and timing.minimum <= timing.mean
and timing.mean <= timing.maximum
):
return make_timing_interval(
lower=max(timing.minimum, timing.mean - timing.stdev),
upper=min(timing.maximum, timing.mean + timing.stdev),
center=timing.mean,
)
if not is_positive_finite(timing.mean) or not is_nonnegative_finite(timing.stdev):
return None
return None
lower = max(timing.mean - timing.stdev, timing.mean * 0.001)
upper = timing.mean + timing.stdev
if is_positive_finite(timing.minimum):
lower = max(lower, timing.minimum)
if is_positive_finite(timing.maximum):
upper = min(upper, timing.maximum)
return make_timing_interval(lower=lower, upper=upper, center=timing.mean)
def compute_timing_interval(timing):