Tweak tuning database plot and comparison scripts

benchmarks/scripts/cccl/bench/bench.py

@@ -242,10 +242,10 @@ def device_json(algname):
 
 def get_device_name(device):
     gpu_name = device["name"]
-    bw = device["global_memory_bus_width"]
+    bus_width = device["global_memory_bus_width"]
     sms = device["number_of_sms"]
     ecc = "eccon" if device["ecc_state"] else "eccoff"
-    name = "{} ({}, {}, {})".format(gpu_name, bw, sms, ecc)
+    name = "{} ({}, {}, {})".format(gpu_name, bus_width, sms, ecc)
     return name.replace('NVIDIA ', '')
 
 

benchmarks/scripts/compare.py

@@ -7,6 +7,8 @@
 import numpy as np
 import pandas as pd
 
+from colorama import Fore
+
 
 def get_filenames_map(arr):
     if not arr:
@@ -56,28 +58,21 @@ def alg_dfs(file):
     result = {}
     storage = cccl.bench.StorageBase(file)
     for algname in storage.algnames():
-        subbench_df = None
         for subbench in storage.subbenches(algname):
             df = storage.alg_to_df(algname, subbench)
             df = df.map(lambda x: x if is_finite(x) else np.nan)
             df = df.dropna(subset=['center'], how='all')
-            df = filter_by_type(filter_by_offset_type(filter_by_problem_size(df)))
+            #TODO(bgruber): maybe expose the filters under a -p0, or --short flag
+            #df = filter_by_type(filter_by_offset_type(filter_by_problem_size(df)))
             df['Noise'] = df['samples'].apply(lambda x: np.std(x) / np.mean(x)) * 100
             df['Mean'] = df['samples'].apply(lambda x: np.mean(x))
-            df = df.drop(columns=['samples', 'center', 'bw', 'elapsed'])
-            if subbench_df is None:
-                subbench_df = df
-            else:
-                subbench_df = pd.concat([subbench_df, df])
-        fused_algname = algname + '.' + subbench
-        if fused_algname in result:
-            result[fused_algname] = pd.concat([result[fused_algname], subbench_df])
-        else:
-            result[fused_algname] = subbench_df
+            df = df.drop(columns=['samples', 'center', 'bw', 'elapsed', 'variant'])
+            fused_algname = algname.removeprefix("cub.bench.").removeprefix("thrust.bench.") + '.' + subbench
+            result[fused_algname] = df
 
     for algname in result:
         if result[algname]['cccl'].nunique() != 1:
-            raise ValueError(f"Multiple CCCL versions in one db '{algname}'")
+            print(f"WARNING: Multiple CCCL versions in one db '{algname}'")
         result[algname] = result[algname].drop(columns=['cccl'])
 
     return result
@@ -96,29 +91,53 @@ def parse_args():
     return parser.parse_args()
 
 
+config_count = 0
+pass_count = 0
+faster_count = 0
+slower_count = 0
+
+
+def status(frac_diff, noise_ref, noise_cmp):
+    global config_count
+    global pass_count
+    global faster_count
+    global slower_count
+    config_count += 1
+    min_noise = min(noise_ref, noise_cmp)
+    if abs(frac_diff) <= min_noise:
+        pass_count += 1
+        return Fore.BLUE + "SAME" + Fore.RESET
+    if frac_diff < 0:
+        faster_count += 1
+        return Fore.GREEN + "FAST" + Fore.RESET
+    if frac_diff > 0:
+        slower_count += 1
+        return Fore.RED + "SLOW" + Fore.RESET
+
+
 def compare():
     args = parse_args()
     reference_df = alg_dfs(args.reference)
     compare_df = alg_dfs(args.compare)
-    for alg in reference_df.keys() & compare_df.keys():
+    for alg in sorted(reference_df.keys() & compare_df.keys()):
         print()
         print()
         print(f'# {alg}')
-        merge_columns = [col for col in reference_df[alg].columns if col not in ['Noise', 'Mean']]
+        # use every column except 'Noise', 'Mean', 'ctk', 'gpu' to match runs between reference and comparison file
+        merge_columns = [col for col in reference_df[alg].columns if col not in ['Noise', 'Mean', 'ctk', 'gpu']]
         df = pd.merge(reference_df[alg], compare_df[alg], on=merge_columns, suffixes=('Ref', 'Cmp'))
-        df['Diff'] = df['MeanCmp'] - df['MeanRef']
-        df['FDiff'] = (df['Diff'] / df['MeanRef']) * 100
-
-        for _, row in df[['ctk', 'gpu', 'variant']].drop_duplicates().iterrows():
-            ctk_version = row['ctk']
-            variant = row['variant']
-            gpu = row['gpu']
-            case_df = df[(df['ctk'] == ctk_version) & (df['gpu'] == gpu) & (df['variant'] == variant)]
-            case_df = case_df.drop(columns=['ctk', 'gpu', 'variant'])
-            print()
-            print(f'## CTK {ctk_version} GPU {gpu} ({variant})')
-            print()
-            print(case_df.to_markdown(index=False))
+        df['Abs. Diff'] = df['MeanCmp'] - df['MeanRef']
+        df['Rel. Diff'] = (df['Abs. Diff'] / df['MeanRef']) * 100
+        df['Status'] = list(map(status, df['Rel. Diff'], df['NoiseRef'], df['NoiseCmp']))
+        df = df.drop(columns=['ctkRef', 'ctkCmp', 'gpuRef', 'gpuCmp'])
+        print()
+        print(df.to_markdown(index=False))
+
+    print("# Summary\n")
+    print("- Total Matches: %d" % config_count)
+    print("  - Pass    (diff <= min_noise): %d" % pass_count)
+    print("  - Faster  (diff > min_noise):  %d" % faster_count)
+    print("  - Slower  (diff > min_noise):  %d" % slower_count)
 
 
 if __name__ == "__main__":

benchmarks/scripts/sol.py

@@ -10,20 +10,6 @@
 import matplotlib.pyplot as plt
 
 
-def get_filenames_map(arr):
-    if not arr:
-        return []
-
-    prefix = arr[0]
-    for string in arr:
-        while not string.startswith(prefix):
-            prefix = prefix[:-1]
-        if not prefix:
-            break
-
-    return {string: string[len(prefix):] for string in arr}
-
-
 def is_finite(x):
     if isinstance(x, float):
         return x != np.inf and x != -np.inf
@@ -60,28 +46,23 @@ def alg_dfs(files):
         storage = cccl.bench.StorageBase(file)
         for algname in storage.algnames():
             for subbench in storage.subbenches(algname):
-                subbench_df = None
                 df = storage.alg_to_df(algname, subbench)
                 df = df.map(lambda x: x if is_finite(x) else np.nan)
                 df = df.dropna(subset=['center'], how='all')
                 df = filter_by_type(filter_by_offset_type(filter_by_problem_size(df)))
                 df = df.filter(items=['ctk', 'cccl', 'gpu', 'variant', 'bw'])
-                df['variant'] = df['variant'].astype(str) + " ({})".format(file)
+                df['variant'] = df['variant'].astype(str)
                 df['bw'] = df['bw'] * 100
-                if subbench_df is None:
-                    subbench_df = df
+                fused_algname = algname.removeprefix("cub.bench.").removeprefix("thrust.bench.") + '.' + subbench
+                if fused_algname in result:
+                    result[fused_algname] = pd.concat([result[fused_algname], df])
                 else:
-                    subbench_df = pd.concat([subbench_df, df])
-            fused_algname = algname + '.' + subbench
-            if fused_algname in result:
-                result[fused_algname] = pd.concat([result[fused_algname], subbench_df])
-            else:
-                result[fused_algname] = subbench_df
+                    result[fused_algname] = df
 
     return result
 
 
-def alg_bws(dfs):
+def alg_bws(dfs, verbose):
     medians = None
     for algname in dfs:
         df = dfs[algname]
@@ -90,8 +71,17 @@ def alg_bws(dfs):
             medians = df
         else:
             medians = pd.concat([medians, df])
-    medians['hue'] = medians['ctk'].astype(str) + ' ' + medians['cccl'].astype(
-        str) + ' ' + medians['gpu'].astype(str) + ' ' + medians['variant']
+    # print more information if it's not unique across all runs or when requested (verbose)
+    medians['hue'] = ''
+    if verbose or medians['cccl'].unique().size > 1:
+        medians['hue'] = medians['hue'] + 'CCCL ' + medians['cccl'].astype(str) + ' '
+    gpuname = medians['gpu'] if verbose else medians['gpu'].astype(str).map(lambda x: x[:x.find('(') - 1])
+    medians['hue'] = medians['hue'] + gpuname + ' '
+    if medians['variant'].unique().size > 1:
+        variant = medians['variant'].astype(str).map(lambda x : (' ' + x if x != 'base' else ''))
+        medians['hue'] = medians['hue'] + variant + ' '
+    if verbose or medians['ctk'].unique().size > 1:
+        medians['hue'] = medians['hue'] + 'CTK ' + medians['ctk'].astype(str)
     return medians.drop(columns=['ctk', 'cccl', 'gpu', 'variant'])
 
 
@@ -106,22 +96,37 @@ def plot_sol(medians, box):
         ax = sns.boxenplot(data=medians, x='alg', y='bw', hue='hue')
     else:
         ax = sns.barplot(data=medians, x='alg', y='bw', hue='hue', errorbar=lambda x: (x.min(), x.max()))
-    for container in ax.containers:
-        ax.bar_label(container, fmt='%.1f')
-    ax.set_xticklabels(ax.get_xticklabels(), rotation=15, rotation_mode='anchor', ha='right')
+        ax.bar_label(ax.containers[0], fmt='%.1f')
+        for container in ax.containers[1:]:
+            labels = [f'{c:.1f}\n({(c/f)*100:.0f}%)' for f, c in zip(ax.containers[0].datavalues, container.datavalues)]
+            ax.bar_label(container, labels=labels)
+
+    ax.legend(title=None)
+    ax.set_xlabel('Algorithm')
+    ax.set_ylabel('Bandwidth (%SOL)')
+    ax.set_xticklabels(ax.get_xticklabels(), rotation=30, rotation_mode='anchor', ha='right')
     plt.show()
 
+def print_speedup(medians):
+    m = medians.groupby(['alg', 'hue'], sort=False).mean()
+    m['speedup'] = (m['bw'] / m.groupby(['alg'])['bw'].transform('first'))
+    print('# Speedups:')
+    print()
+    print(m.drop(columns='bw').sort_values(by='speedup', ascending=False).to_markdown())
 
 def parse_args():
     parser = argparse.ArgumentParser(description="Analyze benchmark results.")
     parser.add_argument('files', type=file_exists, nargs='+', help='At least one file is required.')
     parser.add_argument('--box', action='store_true', help='Plot box instead of bar.')
+    parser.add_argument('-v', action='store_true', help='Verbose legend.')
     return parser.parse_args()
 
 
 def sol():
     args = parse_args()
-    plot_sol(alg_bws(alg_dfs(args.files)), args.box)
+    medians = alg_bws(alg_dfs(args.files), args.v)
+    print_speedup(medians)
+    plot_sol(medians, args.box)
 
 
 if __name__ == "__main__":