add netmark analysis

Signed-off-by: vsoch <[email protected]>

Author: vsoch

analysis/netmark/1-run-analysis.py

@@ -0,0 +1,122 @@
+#!/usr/bin/env python3
+
+import argparse
+import os
+import sys
+import re
+import pandas
+
+import matplotlib.pylab as plt
+import seaborn as sns
+
+here = os.path.dirname(os.path.abspath(__file__))
+analysis_root = os.path.dirname(here)
+root = os.path.dirname(analysis_root)
+sys.path.insert(0, analysis_root)
+
+import performance_study as ps
+
+sns.set_theme(style="whitegrid", palette="muted")
+
+# These are files I found erroneous - no result, or incomplete result
+# Details included with each, and more exploration is likely needed to quantify
+# error types
+errors = []
+error_regex = "(%s)" % "|".join(errors)
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        description="Run analysis",
+        formatter_class=argparse.RawTextHelpFormatter,
+    )
+    parser.add_argument(
+        "--root",
+        help="root directory with experiments",
+        default=os.path.join(root, "experiments"),
+    )
+    parser.add_argument(
+        "--non-anon",
+        help="Generate non-anon",
+        action="store_true",
+        default=False,
+    )
+    parser.add_argument(
+        "--out",
+        help="directory to save parsed results",
+        default=os.path.join(here, "data"),
+    )
+    return parser
+
+
+def main():
+    """
+    Find application result files to parse.
+    """
+    parser = get_parser()
+    args, _ = parser.parse_known_args()
+
+    # Output images and data
+    outdir = os.path.abspath(args.out)
+    indir = os.path.abspath(args.root)
+
+    # We absolutely want on premises results here
+    if not os.path.exists(outdir):
+        os.makedirs(outdir)
+
+    # Find input files (skip anything with test)
+    files = ps.find_inputs(indir, "RTT.csv")
+    if not files:
+        raise ValueError(f"There are no input files in {indir}")
+
+    parse_data(indir, outdir, files)
+
+
+def parse_data(indir, outdir, files):
+    """
+    Parse filepaths for environment, etc., and results files for data.
+    """
+    # metrics here will be figures of merit, and seconds runtime
+    p = ps.ProblemSizeParser("netmark")
+
+    # For flux we can save jobspecs and other event data
+    data = {}
+    img_outdir = os.path.join(outdir, "img")
+    if not os.path.exists(img_outdir):
+        os.makedirs(img_outdir)
+
+    # It's important to just parse raw data once, and then use intermediate
+    for filename in files:
+        exp = ps.ExperimentNameParser(filename, indir)
+        if exp.prefix not in data:
+            data[exp.prefix] = []
+        exp.show()
+
+        hosts_file = os.path.join(os.path.dirname(filename), "hosts.csv")
+        hosts = list(pandas.read_csv(hosts_file))
+        df = pandas.read_csv(filename, header=None, names=hosts)
+        df.index = hosts
+
+        plt.figure(figsize=(20, 18))
+        sns.heatmap(df, vmin=0, vmax=70, cmap="crest")
+        plt.title(
+            f"                 Netmark Latency Google Kubernetes Size {exp.size}",
+            fontsize=16,
+        )
+        plt.tight_layout()
+        plt.savefig(os.path.join(img_outdir, f"netmark-size-{exp.size}-heatmap.svg"))
+        plt.savefig(os.path.join(img_outdir, f"netmark-size-{exp.size}-heatmap.png"))
+
+        plt.close()
+        plt.clf()
+        sns.clustermap(df, cmap="crest")
+        plt.savefig(os.path.join(img_outdir, f"netmark-size-{exp.size}-cluster.svg"))
+        plt.savefig(os.path.join(img_outdir, f"netmark-size-{exp.size}-cluster.png"))
+        plt.close()
+        plt.clf()
+
+    print("Done parsing netmark results!")
+
+
+if __name__ == "__main__":
+    main()