Working on Python scripts for SpMV benchmark results processing.

src/Benchmarks/scripts/tnl-spmv-benchmark-make-tables-json.py

+#!/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)

src/Benchmarks/scripts/tnl-spmv-benchmark-make-tables.py

@@ -154,8 +154,8 @@ Sort by comparison formats
 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