Add tool to plot best kernel performance vs generic kernel

src/algorithms/libs/volk_gnsssdr_module/volk_gnsssdr/apps/plot_best_vs_generic.py

@@ -0,0 +1,52 @@
+#!/usr/bin/env python
+# Copyright (C) 2010-2019 (see AUTHORS file for a list of contributors)
+#
+#  This file is part of GNSS-SDR.
+#
+#  GNSS-SDR is free software: you can redistribute it and/or modify
+#  it under the terms of the GNU General Public License as published by
+#  the Free Software Foundation, either version 3 of the License, or
+#  (at your option) any later version.
+#
+#  GNSS-SDR is distributed in the hope that it will be useful,
+#  but WITHOUT ANY WARRANTY; without even the implied warranty of
+#  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+#  GNU General Public License for more details.
+#
+#  You should have received a copy of the GNU General Public License
+#  along with GNSS-SDR. If not, see <https://www.gnu.org/licenses/>.
+
+# This script is used to compare the generic kernels to the highest performing kernel, for each operation
+# Run:
+#   ./volk_gnsssdr_profile -j volk_gnsssdr_results.json
+# Then run this script under python3
+
+import matplotlib.pyplot as plt
+import numpy as np
+import json
+
+filename = 'volk_gnsssdr_results.json'
+
+operations = []
+metrics = []
+with open(filename) as json_file:
+    data = json.load(json_file)
+    for test in data['volk_gnsssdr_tests']:
+        if ('generic' in test['results']) or ('u_generic' in test['results']): # some dont have a generic kernel
+            operations.append(test['name'][13:]) # remove volk_gnsssdr_ prefix that they all have
+            extension_performance = []
+            for key, val in test['results'].items():
+                if key not in ['generic', 'u_generic']: # exclude generic results, when trying to find fastest time
+                    extension_performance.append(val['time'])
+            try:
+                generic_time = test['results']['generic']['time']
+            except:
+                generic_time = test['results']['u_generic']['time']
+            metrics.append(extension_performance[np.argmin(extension_performance)]/generic_time)
+
+plt.bar(np.arange(len(metrics)), metrics)
+plt.hlines(1.0, -1, len(metrics), colors='r', linestyles='dashed')
+plt.axis([-1, len(metrics), 0, 2])
+plt.xticks(np.arange(len(operations)), operations, rotation=90)
+plt.ylabel('Time taken of fastest kernel relative to generic kernel')
+plt.show()