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Compute quantiles the same way C++ does

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Use rounded-rank method, rather than NumPy's quantile
This commit is contained in:
Oleksandr Pavlyk
2026-06-26 18:12:46 -05:00
parent ad4a76dbe1
commit 02e2d6f097
2 changed files with 14 additions and 5 deletions
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12
python/scripts/nvbench_compare.py
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@@ -1239,13 +1239,21 @@ def compute_timing_interval_from_samples(samples):
return interval
def percentile_rank(percentile: int, sample_count: int) -> int:
clamped_percentile = min(max(percentile, 0), 100)
return round((clamped_percentile / 100.0) * (sample_count - 1))
def compute_robust_timing_input_from_samples(samples):
values = positive_finite_array(samples)
if values is None:
return None
first_quartile, median, third_quartile = np.quantile(values, [0.25, 0.5, 0.75])
sorted_values = np.sort(values)
first_quartile = sorted_values[percentile_rank(25, len(sorted_values))]
median = sorted_values[percentile_rank(50, len(sorted_values))]
third_quartile = sorted_values[percentile_rank(75, len(sorted_values))]
interval = make_timing_interval(
lower=np.min(values),
lower=sorted_values[0],
upper=third_quartile,
center=median,
)

7
python/test/test_nvbench_compare.py
View File

@@ -801,14 +801,15 @@ def test_compare_gpu_timings_classifies_common_cases(tmp_path, nvbench_compare):
mean=100.0,
stdev=1.0,
stdev_relative=0.01,
sample_values=[4.0, 5.0, 6.0],
sample_values=[4.0, 5.0, 6.0, 100.0],
),
)
assert mixed_robust_summary_and_bulk is not None
assert mixed_robust_summary_and_bulk.ref_time == pytest.approx(10.0)
assert mixed_robust_summary_and_bulk.cmp_time == pytest.approx(5.0)
assert mixed_robust_summary_and_bulk.cmp_time == pytest.approx(6.0)
assert mixed_robust_summary_and_bulk.ref_interval.center == pytest.approx(10.0)
assert mixed_robust_summary_and_bulk.cmp_interval.center == pytest.approx(5.0)
assert mixed_robust_summary_and_bulk.cmp_interval.center == pytest.approx(6.0)
assert mixed_robust_summary_and_bulk.cmp_interval.upper == pytest.approx(6.0)
ref_interval_timing = make_gpu_timing_data(
nvbench_compare,