117 lines
4.3 KiB
Python
Executable File
117 lines
4.3 KiB
Python
Executable File
#!/usr/bin/env python
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import sys, argparse
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import csv
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import matplotlib.pyplot as plt
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import numpy as np
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from collections import OrderedDict
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def parse_args():
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parser = argparse.ArgumentParser()
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parser.add_argument("csv_files", nargs=1, default=None, help="CSV files with level runtimes")
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parser.add_argument("-f", default=None, help="Output filename")
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parser.add_argument("--min_fwd_width", type=int, default=None, help="Minimum forward width for parllel")
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parser.add_argument("--min_bck_width", type=int, default=None, help="Minimum backward width for parllel")
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args = parser.parse_args()
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return args
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def main():
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args = parse_args()
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data = {}
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for filename in args.csv_files:
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with open(filename) as f:
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data[filename] = {}
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csv_reader = csv.DictReader(f)
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for field in csv_reader.fieldnames:
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data[filename][field] = []
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for row in csv_reader:
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for field in csv_reader.fieldnames:
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data[filename][field].append(float(row[field]))
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for filename, series in data.iteritems():
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print "File: ", filename
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#for series_name, data_values in series.iteritems():
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#print "\tSeries: ", series_name
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#print "\tValuse: ", data_values
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series = OrderedDict()
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analyzer_colors = OrderedDict()
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analyzer_colors['Serial']= 'r'
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analyzer_colors['Dynamic']= 'b'
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analyzer_colors['Levelized']= 'g'
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analyzer_colors['No Dep']= 'y'
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for analyzer, color in analyzer_colors.iteritems():
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series['%s' % analyzer] = {'label': '%s' % analyzer, 'linestyle': "-", 'color': color}
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series['%s SIMD_AUTO' % analyzer] = {'label': '%s SIMD_AUTO' % analyzer, 'linestyle': "--", 'color': color}
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series['%s SIMD_INTRINSICS' % analyzer] = {'label': '%s SIMD_INTR' % analyzer, 'linestyle': "-.", 'color': color}
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#Plot results
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fig, ax = plt.subplots(1)
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for series_name, series_info in series.iteritems():
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ax.plot(data[args.csv_files[0]]["Time Vector Width"], data[args.csv_files[0]][series_name], linestyle=series_info['linestyle'], color=series_info['color'], label=series_info['label'], linewidth=2)
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ax.legend(loc='best', prop={'size': 13})
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ax.set_ylabel("Time (sec)")
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ax.set_xlabel("Time Vector Width")
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ax.set_title("Run-time Scaling With Time Vector Width")
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ax.set_xlim(left=1, right=max(data[args.csv_files[0]]["Time Vector Width"]))
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##Cummulative Times
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#for series_name in derived_series.keys():
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#if series_name.startswith("Cumm Time"):
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#ax[0].plot(data[args.csv_files[0]]["Level"], derived_series[series_name], label=series_name[len("Cumm Time"):])
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#ax[0].legend(loc='best')
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#ax[0].set_ylabel("Cummulative\nTime (sec)")
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#ax[0].set_title("Per Level Performance Characteristics")
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#ax[0].set_ylim(bottom=0)
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##Per-level Speed-up
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#for traversal in traversals:
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#series_name = "Avg Level Speed-up %s" % traversal
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#ax[1].plot(data[args.csv_files[0]]["Level"], derived_series[series_name], label=traversal)
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#ax[1].legend(loc='best')
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##ax[1].set_yscale('log')
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#ax[1].set_ylabel("Level Speed-Up\n(N_AVG %d)" % N_AVG)
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#ax[1].set_ylim(bottom=0)
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#for traversal in traversals:
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#series_name = "Cumm Speed-Up %s" % traversal
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#ax[2].plot(data[args.csv_files[0]]["Level"], derived_series[series_name], label=traversal)
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#ax[2].legend(loc='best')
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#ax[2].set_ylabel("Cummulative\nSpeed-Up")
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#ax[2].set_ylim(bottom=0)
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#ax[3].plot(data[args.csv_files[0]]["Level"], data[args.csv_files[0]]["Width"], label="width")
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#if args.min_fwd_width:
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#ax[3].plot(data[args.csv_files[0]]["Level"], [args.min_fwd_width for x in xrange(len(data[args.csv_files[0]]["Level"]))], label="Min // Fwd")
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#if args.min_fwd_width:
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#ax[3].plot(data[args.csv_files[0]]["Level"], [args.min_bck_width for x in xrange(len(data[args.csv_files[0]]["Level"]))], label="Min // Bck")
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#ax[3].set_yscale('log')
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#ax[3].set_xlabel("Level")
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#ax[3].set_ylabel("Level Width")
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##ax[3].set_xlim(right=len(data[args.csv_files[0]]["Level"]))
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#ax[3].legend(loc='best')
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plt.tight_layout()
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if args.f:
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plt.savefig(args.f, dpi=300)
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else:
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plt.show()
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if __name__ == "__main__":
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main()
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