Working on Python scripts for SpMV benchmark results processing.

#!/usr/bin/python3

import json

import pandas as pd

from pandas.io.json import json_normalize

def slugify(s):

   s = str(s).strip().replace(' ', '_')

   return re.sub(r'(?u)[^-\w.]', '', s)

####

# Parse input file

print( "Parsing input file...." )

with open('sparse-matrix-benchmark.log') as f:

    d = json.load(f)

input_df = json_normalize( d, record_path=['results'] )

#input_df.to_html( "orig-pandas.html" )

####

# Create multiindex for columns

# Get format names - TODO: the first benchmark might not have all of them

matrixName = input_df.iloc[0]['matrix name']

df_matrix = input_df.loc[input_df['matrix name'] == matrixName]

formats = df_matrix.loc[:,'format']

level1 = [ 'Matrix name', 'rows', 'columns' ]

level2 = [ '',            '',     ''        ]

level3 = [ '',            '',     ''        ]

df_data = [[ ' ',' ',' ']]

for format in formats:

   for device in ['CPU','GPU']:

      for data in ['bandwidth' ]: #,'time','speed-up','non-zeros','stddev','stddev/time','diff.max','diff.l2']:

         level1.append( format )

         level2.append( device )

         level3.append( data )

         df_data[ 0 ].append( ' ' )

multiColumns = pd.MultiIndex.from_arrays([ level1, level2, level3 ] )

frames = []

in_idx = 0

out_idx = 0

max_out_idx = 50

print( "Converting data..." )

while in_idx < len(input_df.index) and out_idx < max_out_idx:

   matrixName = input_df.iloc[in_idx]['matrix name']

   df_matrix = input_df.loc[input_df['matrix name'] == matrixName]

   print( out_idx, ":", in_idx, "/", len(input_df.index), ":", matrixName )

   aux_df = pd.DataFrame( df_data, columns = multiColumns, index = [out_idx] )

   for index,row in df_matrix.iterrows():

      aux_df.iloc[0]['Matrix name'] = row['matrix name']

      aux_df.iloc[0]['rows']        = row['rows']

      aux_df.iloc[0]['columns']     = row['columns']

      current_format = row['format']

      current_device = row['device']

      #print( current_format + " / " + current_device )

      aux_df.iloc[0][(current_format,current_device,'bandwidth')]   = row['bandwidth']

      #aux_df.iloc[0][(current_format,current_device,'time')]        = row['time']

      #aux_df.iloc[0][(current_format,current_device,'speed-up')]    = row['speedup']

      #aux_df.iloc[0][(current_format,current_device,'non-zeros')]   = row['non-zeros']

      #aux_df.iloc[0][(current_format,current_device,'stddev')]      = row['stddev']

      #aux_df.iloc[0][(current_format,current_device,'stddev/time')] = row['stddev/time']

      #aux_df.iloc[0][(current_format,current_device,'diff.max')]    = row['CSR Diff.Max']

      #aux_df.iloc[0][(current_format,current_device,'diff.l2')]    = row['CSR Diff.L2']

   frames.append( aux_df )

   out_idx = out_idx + 1

   in_idx = in_idx + len(df_matrix.index)

print( "Merging data into one frame..." )

result = pd.concat( frames )

print( "Setting data types..." )

for format in formats:

   for device in ['CPU','GPU']:

      #df['eps'] = pd.to_numeric(df['eps'], errors='coerce')

      print(result[(format,device,'bandwidth')].toList())

      result[(format,device,'bandwidth')] = pd.to_numeric( result[(format,device,'bandwidth')], errors='coerce' )

      #result[(format,device,'time')].astype('float64')

      #result[(format,device,'speed-up')].astype('float64')

      #result[(format,device,'non-zeros')].astype('int64')

      #result[(format,device,'stddev')].astype('float64')

      #result[(format,device,'stddev/time')].astype('float64')

      #result[(format,device,'diff.max')].astype('float64')

      #result[(format,device,'diff.l2')].astype('float64')

print( "Writting to HTML file..." )

result.to_html( 'output.html' )

#result.sort_values(by=[('cusparse','GPU','bandwidth')],inplace=True,ascending=False)

#for format in formats:

#   cusparse_bw = result[('cusparse','GPU','bandwidth')].toList()

#   format_bw = result[(format,'GPU','bandwidth')].toList()

#

#for format in formats:

#   result.sort_values(by=[(format,'GPU','bandwidth')],inplace=True,ascending=False)

formats_comparison = defaultdict( list )

for format in gpu_comparison_formats:

   df.sort_values(by=[f"{format} Bandwidth"],inplace=True,ascending=False)

   formats_comparison[ format ] = df[format, "GPU", "bandwidth"].tolist();

   formats_comparison[ gpu_comparison_formats[ format ] ] = df[gpu_comparison_formats[ format ], "GPU", "bandwidth"].tolist();

   formats_comparison[ format ] = df[format, "GPU", "bandwidth"].tolist()

   formats_comparison[ gpu_comparison_formats[ format ] ] = df[gpu_comparison_formats[ format ], "GPU", "bandwidth"].tolist()

"""

Writting gnuplot source files