extras/benchmark/plot_cc.py

0001 # SPDX-PackageName = "traccc, a part of the ACTS project"
0002 # SPDX-FileCopyrightText: CERN
0003 # SPDX-License-Identifier: MPL-2.0
0004
0005 import matplotlib.pyplot
0006 import pandas
0007 import argparse
0008 import logging
0009 import pathlib
0010 from datetime import datetime
0011
0012 log = logging.getLogger("traccc_plot")
0013
0014
0015 def main():
0016     parser = argparse.ArgumentParser()
0017
0018     parser.add_argument(
0019         "input",
0020         type=pathlib.Path,
0021         help="input CSV database",
0022     )
0023
0024     parser.add_argument(
0025         "output",
0026         type=pathlib.Path,
0027         help="output plot file",
0028     )
0029
0030     parser.add_argument(
0031         "--threshold",
0032         type=float,
0033         help="value in seconds under which to hide kernels",
0034         default=0.001,
0035     )
0036
0037     args = parser.parse_args()
0038
0039     logging.basicConfig(
0040         level=logging.INFO,
0041         format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
0042     )
0043
0044     log.info('Loading CSV database "%s"', args.input)
0045     df = pandas.read_csv(args.input)
0046
0047     valid_kernels = set()
0048
0049     ccs = []
0050
0051     log.info("Filtering kernels with a threshold of %f seconds", args.threshold)
0052
0053     for x in df.iloc:
0054         if x["rec_throughput"] >= args.threshold:
0055             valid_kernels.add(x["kernel"])
0056         if x["cc"] not in ccs:
0057             ccs.append(x["cc"])
0058
0059     log.info(
0060         "Plotting %d kernels over %d Compute Capabilities", len(valid_kernels), len(ccs)
0061     )
0062
0063     sorted_valid_kernels = sorted(list(valid_kernels))
0064
0065     px = 1 / matplotlib.pyplot.rcParams["figure.dpi"]
0066
0067     f, a = matplotlib.pyplot.subplots(1, 1, figsize=(806 * px, 600 * px))
0068
0069     xrange = list(range(len(ccs)))
0070
0071     for x in sorted_valid_kernels:
0072         x_data = []
0073         y_data = []
0074         y_error = []
0075
0076         for y in df.iloc:
0077             if y["kernel"] == x:
0078                 x_data.append(ccs.index(y["cc"]))
0079                 y_data.append(y["rec_throughput"] * 1000)
0080                 y_error.append(y["rec_throughput_dev"] * 1000)
0081
0082         a.errorbar(
0083             x_data, y_data, yerr=y_error, label=x, marker="x", capsize=2, elinewidth=1
0084         )
0085
0086     a.set_xticks(
0087         xrange,
0088         ccs,
0089     )
0090     a.set_xlabel("Compute Capability (PTX Target)")
0091     a.set_ylabel("Reciprocal throughput [ms]")
0092     a.legend(prop={"family": "monospace"})
0093     a.grid(color="lightgray", linewidth=0.5)
0094     f.tight_layout()
0095
0096     log.info('Saving plot to "%s"', args.output)
0097
0098     f.savefig(args.output)
0099
0100
0101 if __name__ == "__main__":
0102     main()