feat: add CUDA cuDF convenience API#8624
Performance Regression: -6.55%
⚠️ Unknown Walltime execution environment detected
Using the Walltime instrument on standard Hosted Runners will lead to inconsistent data.
For the most accurate results, we recommend using CodSpeed Macro Runners: bare-metal machines fine-tuned for performance measurement consistency.
⚠️ Different runtime environments detected
Some benchmarks with significant performance changes were compared across different runtime environments,
which may affect the accuracy of the results.
⚡ 2 improved benchmarks
❌ 3 regressed benchmarks
✅ 1590 untouched benchmarks
⏩ 4 skipped benchmarks1
Warning
Please fix the performance issues or acknowledge them on CodSpeed.
Performance Changes
| Mode | Benchmark | BASE |
HEAD |
Efficiency | |
|---|---|---|---|---|---|
| ❌ | Simulation | chunked_varbinview_opt_canonical_into[(1000, 10)] |
169.6 µs | 206.2 µs | -17.75% |
| ❌ | Simulation | chunked_varbinview_into_canonical[(1000, 10)] |
169.3 µs | 205.6 µs | -17.67% |
| ❌ | Simulation | chunked_varbinview_opt_into_canonical[(1000, 10)] |
183.4 µs | 219.7 µs | -16.52% |
| ⚡ | Simulation | chunked_varbinview_into_canonical[(100, 100)] |
306.4 µs | 272.1 µs | +12.61% |
| ⚡ | Simulation | bitwise_not_vortex_buffer_mut[128] |
273.6 ns | 244.4 ns | +11.93% |
Tip
Investigate this regression by commenting @codspeedbot fix this regression on this PR, or directly use the CodSpeed MCP with your agent.
Comparing ad/pycudf4 (bbfb83a) with develop (0a45777)
Footnotes
-
4 benchmarks were skipped, so the baseline results were used instead. If they were deleted from the codebase, click here and archive them to remove them from the performance reports. ↩