Commit 340107a2 authored by Tomáš Oberhuber's avatar Tomáš Oberhuber Committed by Jakub Klinkovský
Browse files

Improving script for SpMV benchmark results processinb.

parent 52ee50b2

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

+28 −15
Original line number Diff line number Diff line
@@ -31,6 +31,7 @@ def slugify(s): 
def latexFormatName( name ):

   name = name.replace('<','')

   name = name.replace('>','')

   name = name.replace( 'Light  Automatic ', '')

   return name



####
@@ -68,7 +69,7 @@ def get_multiindex( input_df, formats ): 
         level3.append( 'speed-up')

         level4.append( 'non-symmetric' )

         df_data[ 0 ].append( ' ' )

      if format == 'CSR< Light > Automatic':

      if format == 'CSR< Light > Automatic' or format == 'CSR< Light > Automatic Light':

         level1.append( format )

         level2.append( 'GPU' )

         level3.append( 'speed-up')
@@ -93,6 +94,7 @@ def convert_data_frame( input_df, multicolumns, df_data, begin_idx = 0, end_idx 
   #max_out_idx = max_rows

   if end_idx == -1:

      end_idx = len(input_df.index)

   best_count = 0

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

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

      df_matrix = input_df.loc[input_df['matrix name'] == matrixName]
@@ -118,6 +120,7 @@ def convert_data_frame( input_df, multicolumns, df_data, begin_idx = 0, end_idx 
             not 'cusparse' in current_format and

             not 'LightSpMV' in current_format and

             not 'Hybrid' in current_format and

             current_format != 'CSR< Light > Automatic' and

             bw > best_bw ):

            best_bw = bw

            best_format = current_format
@@ -135,6 +138,7 @@ def convert_data_frame( input_df, multicolumns, df_data, begin_idx = 0, end_idx 
         aux_df.iloc[0][('TNL Best','GPU','format','')] = best_format

      else:

         aux_df.iloc[0][('TNL Best','GPU','format','')] = 'cusparse'

      best_count += 1

      if out_idx >= begin_idx:

         frames.append( aux_df )

      out_idx = out_idx + 1
@@ -173,8 +177,9 @@ def compute_cusparse_speedup( df, formats ): 


####

# Compute speedup of Light CSR

def compute_csr_light_speedup( df ):

def compute_csr_light_speedup( df, formats ):

   for light in [ 'CSR< Light > Automatic', 'CSR< Light > Automatic Light']:

      if light in formats:

         csr_light_bdw_list = df[(light,'GPU','bandwidth')]

         light_spmv_bdw_list = df[('LightSpMV Vector','GPU','bandwidth')]
@@ -223,7 +228,7 @@ def compute_symmetric_speedup( df, formats ): 


def compute_speedup( df, formats ):

   compute_cusparse_speedup( df, formats )

   #compute_csr_light_speedup( df )

   compute_csr_light_speedup( df, formats )

   compute_binary_speedup( df, formats )

   compute_symmetric_speedup( df, formats )
@@ -487,7 +492,7 @@ def cusparse_speedup_comparison( df, formats, head_size=10 ): 
   profiles = {}

   for format in formats:

      if not format in ['cusparse','CSR']:

         print( f"Writing comparison of speed-up of {format} compared to Cusparse" )

         print( f"Writing comparison of speed-up of {format} ({latexFormatName(format)}) compared to Cusparse" )

         df['tmp'] = df[(format, 'GPU','bandwidth')]

         filtered_df=df.dropna(subset=[('tmp','','','')])

         filtered_df.sort_values(by=[(format,'GPU','speed-up','cusparse')],inplace=True,ascending=False)
@@ -752,11 +757,15 @@ input_df = json_normalize( d, record_path=['results'] ) 
#input_df.to_html( "orig-pandas.html" )



formats = list(set( input_df['format'].values.tolist() )) # list of all formats in the benchmark results

formats.remove('CSR< Light > Automatic')

formats.remove('Binary CSR< Light > Automatic')

formats.remove('Symmetric CSR< Light > Automatic')

formats.remove('Symmetric Binary CSR< Light > Automatic')

formats.append('TNL Best')

multicolumns, df_data = get_multiindex( input_df, formats )



print( "Converting data..." )

result = convert_data_frame( input_df, multicolumns, df_data, 0, 200 )

result = convert_data_frame( input_df, multicolumns, df_data, 0, 2000 )

compute_speedup( result, formats )



result.replace( to_replace=' ',value=np.nan,inplace=True)
@@ -779,6 +788,8 @@ def processDf( df, formats, head_size = 10 ): 
   csr_light_speedup_comparison( df, head_size )



   best = df[('TNL Best','GPU','format')].tolist()

   best_formats = list(set(best))

   sum = 0

   for format in formats:

      if( not 'Binary' in format and

          not 'Symmetric' in format and
@@ -787,7 +798,9 @@ def processDf( df, formats, head_size = 10 ): 
          not 'TNL Best' in format ):

         cases = best.count(format)

         print( f'{format} is best in {cases} cases.')



         sum += cases

   print( f'Total is {sum}.' )

   print( f'Best formats {best_formats}.')

head_size = 25

if not os.path.exists( 'general' ):

   os.mkdir( 'general' )