This example demonstrates using cuda.bench and cuda.bench.results
to implement simple auto-tuning, demonstrated on selecting of
tile shape hyperparameter for naive stencil kernel implemented
in numba-cuda.
Move NVBench JSON result parsing into cuda.bench.results with explicit
BenchmarkResult, BenchmarkResultDevice, BenchmarkResultSummary,
SubBenchmarkResult, and SubBenchmarkState types. Remove the result reader
from the top-level cuda.bench namespace and require construction through
BenchmarkResult.from_json() or BenchmarkResult.empty().
Preserve bulk sample/frequency parsing and estimator helpers while making
summaries rich objects that retain tag/name/hint/hide/description metadata.
Add nvbench-json-summary to render NVBench JSON output as an NVBench-style
markdown summary table, including axis formatting, device sections, hidden
summary filtering, and summary hint formatting.
Update packaging, type stubs, and tests for the new namespace, renamed
classes, Python 3.10-compatible annotations, and summary-table generation.
This allows for Pythonic way of working with BenchResult
as if it was a dictionary.
```
In [1]: import array, numpy as np, cuda.bench
In [2]: r = cuda.bench.BenchResult("temp_data/axes_run1.json")
In [3]: list(r)
Out[3]:
['simple',
'single_float64_axis',
'copy_sweep_grid_shape',
'copy_type_sweep',
'copy_type_conversion_sweep',
'copy_type_and_block_size_sweep']
In [4]: r["simple"].centers(lambda t: np.percentile(t, [25,75]))
Out[4]: {'Device=0': array([0.00100966, 0.00101299])}
In [5]: r.centers(lambda t: np.percentile(t, [25,75]))["simple"]
Out[5]: {'Device=0': array([0.00100966, 0.00101299])}
In [6]: len(r)
Out[6]: 6
In [7]: "fake" in r
Out[7]: False
```
Add arbitrary BenchResult metadata and explicit parse control, replacing
the previous code/elapsed fields. Make BenchResult subscriptable by
subbenchmark name and make SubBenchResult list-like over its states.
Extend SubBenchState parsing to expose summaries by tag, read paired
sample frequency data, return None for unavailable sample/frequency
files, and validate matching sample/frequency lengths.
Harden parsing for NVBench JSON output with no-axis benchmarks, null
axis_values, skipped states with null summaries, float axis input_string
lookups, and recorded sidecar binary paths.
Expand BenchResult tests to cover metadata, parse=False, sequence-style
access, frequency-aware centers, missing binary data, skipped states,
and mismatched sample/frequency counts.
Example usage:
```
import array, numpy as np, cuda.bench
r = cuda.bench.BenchResult("perf_data/axes_run1.json")
r["copy_sweep_grid_shape"].centers_with_frequencies(
lambda t, f: np.median(np.asarray(t)*np.asarray(f)))
```
* Correct Python API signature of State.get_axis_values_as_strings
The C++ API has default boolean argument color, but Python API
declared no arguments.
Closes#345
* Also exercise invocation of get_axis_values_as_string with keyword argument value
* Remove use of cuda.core.experimental
Fixed relative text alignment in docstrings to fix autodoc warnigns
Renamed cuda.bench.test_cpp_exception and cuda.bench.test_py_exception functions
to start with underscore, signaling that these functions are internal and should
not be documented
Account for test_cpp_exceptions -> _test_cpp_exception, same for *_py_*
Make sure to reset __module__ of reexported symbols to be cuda.bench
* Introduce function colorize to modularize colorization/no-color handling
* Use sns.set_theme instead of deprecated sns.set()
* Use str.format instead of legacy % syntax
* Simplified iteration over list
Use f-string (supported since Python 3.6) instead of str.format for
better readability and performance
Fix GCC16 sfinae incomplete warnings.
GCC16 started requiring that the type `T` used in `std::reference_wrapper<T>` is complete where using `-std=c++17`. Since NVBench has to forward declare some types in header files to break circular dependency, use of incomplete type breaks build due to use of `-Werror` flag due to `-Wsfinae-incomplete` warning emitted by GCC16.
This commit replaced affected uses of `std::reference_wrapper<const nvbench::benchmark_base>` in state.cxx, and `std::reference_wrapper<nvbench::printer_base>` in benchmark_base.cxx with raw pointers.
* Add cuda architectures to build wheel for
* Package scripts in wheel
* Separate cuda major version extraction to fix architecutre selection logic
* Add back statement printing cuda version
* [pre-commit.ci] auto code formatting
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
This loop uses benchmark.run_or_skip to resolve#284 even
for scripts that contain more than one benchmark, or when
a script with a single benchmark is executed when more than
one device is available.
