Build multi architecture cuda wheels (#302)

* 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

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

python/scripts/nvbench_histogram.py

@@ -0,0 +1,127 @@
+#!/usr/bin/env python
+
+import argparse
+import os
+import sys
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import seaborn as sns
+
+try:
+    from nvbench_json import reader
+except ImportError:
+    from scripts.nvbench_json import reader
+
+
+def parse_files():
+    help_text = "%(prog)s [nvbench.out.json | dir/] ..."
+    parser = argparse.ArgumentParser(prog="nvbench_histogram", usage=help_text)
+
+    args, files_or_dirs = parser.parse_known_args()
+
+    filenames = []
+    for file_or_dir in files_or_dirs:
+        if os.path.isdir(file_or_dir):
+            for f in os.listdir(file_or_dir):
+                if os.path.splitext(f)[1] != ".json":
+                    continue
+                filename = os.path.join(file_or_dir, f)
+                if os.path.isfile(filename) and os.path.getsize(filename) > 0:
+                    filenames.append(filename)
+        else:
+            filenames.append(file_or_dir)
+
+    filenames.sort()
+
+    if not filenames:
+        parser.print_help()
+        exit(0)
+
+    return filenames
+
+
+def extract_filename(summary):
+    summary_data = summary["data"]
+    value_data = next(filter(lambda v: v["name"] == "filename", summary_data))
+    assert value_data["type"] == "string"
+    return value_data["value"]
+
+
+def extract_size(summary):
+    summary_data = summary["data"]
+    value_data = next(filter(lambda v: v["name"] == "size", summary_data))
+    assert value_data["type"] == "int64"
+    return int(value_data["value"])
+
+
+def parse_samples_meta(filename, state):
+    summaries = state["summaries"]
+    if not summaries:
+        return None, None
+
+    summary = next(
+        filter(lambda s: s["tag"] == "nv/json/bin:nv/cold/sample_times", summaries),
+        None,
+    )
+    if not summary:
+        return None, None
+
+    sample_filename = extract_filename(summary)
+
+    # If not absolute, the path is relative to the associated .json file:
+    if not os.path.isabs(sample_filename):
+        sample_filename = os.path.join(os.path.dirname(filename), sample_filename)
+
+    sample_count = extract_size(summary)
+    return sample_count, sample_filename
+
+
+def parse_samples(filename, state):
+    sample_count, samples_filename = parse_samples_meta(filename, state)
+    if not sample_count or not samples_filename:
+        return []
+
+    with open(samples_filename, "rb") as f:
+        samples = np.fromfile(f, "<f4")
+
+    assert sample_count == len(samples)
+    return samples
+
+
+def to_df(data):
+    return pd.DataFrame.from_dict(dict([(k, pd.Series(v)) for k, v in data.items()]))
+
+
+def parse_json(filename):
+    json_root = reader.read_file(filename)
+
+    samples_data = {}
+
+    for bench in json_root["benchmarks"]:
+        print("Benchmark: {}".format(bench["name"]))
+        for state in bench["states"]:
+            print("State: {}".format(state["name"]))
+
+            samples = parse_samples(filename, state)
+            if len(samples) == 0:
+                continue
+
+            samples_data["{} {}".format(bench["name"], state["name"])] = samples
+
+    return to_df(samples_data)
+
+
+def main():
+    filenames = parse_files()
+
+    dfs = [parse_json(filename) for filename in filenames]
+    df = pd.concat(dfs, ignore_index=True)
+
+    sns.displot(df, rug=True, kind="kde", fill=True)
+    plt.show()
+
+
+if __name__ == "__main__":
+    sys.exit(main())