Update

Author: JewelRoam

graph_net/analysis.py

@@ -11,7 +11,7 @@
 def parse_filename(filename):
     """
     Parses the model name and compiler name from a JSON filename.
-    Assumes filename format: <model_name>_<compiler_name>.json
+    According to filename format of graph_net.torch.test_compiler: <model_name>_<compiler_name>.json
     """
     parts = os.path.splitext(filename)[0].split("_")
     if len(parts) < 2:
@@ -24,7 +24,7 @@ def parse_filename(filename):
 def read_all_speedups(benchmark_path):
     """
     Recursively finds all .json files in a given path, extracts the speedup values,
-    and organizes them by compiler and category (subdirectory).
+    and organizes them by compiler and category (library).
     """
     data_by_compiler_category = defaultdict(lambda: defaultdict(list))
     all_compilers = set()

graph_net/torch/test_compiler.py

@@ -105,8 +105,10 @@ def measure_performance(model_call, args, compiler):
     compiler.synchronize()
 
     if "cuda" in args.device:
-        # Acknowledgement: We evaluate the performance on both end-to-end and GPU-only timings, with reference to
-        # methods only based on CUDA events from KernelBench in https://github.com/ScalingIntelligence/KernelBench.
+        """
+        Acknowledgement: We evaluate the performance on both end-to-end and GPU-only timings,
+        With reference to methods only based on CUDA events from KernelBench in https://github.com/ScalingIntelligence/KernelBench
+        """
 
         device = torch.device(args.device)
         hardware_name = torch.cuda.get_device_name(device)