diff --git a/python/perf-kernels/tools/tune_gemm/test_regression.py b/python/perf-kernels/tools/tune_gemm/test_regression.py
index 1d9e6a479fc3..3a180e7ae365 100644
--- a/python/perf-kernels/tools/tune_gemm/test_regression.py
+++ b/python/perf-kernels/tools/tune_gemm/test_regression.py
@@ -4,85 +4,17 @@
 import pytest
 import warnings
 from copy import deepcopy
+import statistics
 
 
-@pytest.mark.parametrize('config', [
-    # M // BLOCK_M * N // BLOCK_N % 304 == 0
-    # 1 workgroup / CU
-    {
-        'M': 4864, 'N': 4096, 'K': 4096, 'rowMajorA': 'T', 'rowMajorB': 'N', 'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 256,
-        'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 4, 'SPLIT_K': 1, 'num_warps': 8, 'num_stages': 0, 'waves_per_eu': 0,
-        'matrix_instr_nonkdim': 16, 'kpack': 2
-    },
-    {
-        'M': 4864, 'N': 4096, 'K': 4160, 'rowMajorA': 'T', 'rowMajorB': 'N', 'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 256,
-        'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 4, 'SPLIT_K': 1, 'num_warps': 8, 'num_stages': 0, 'waves_per_eu': 0,
-        'matrix_instr_nonkdim': 16, 'kpack': 2
-    },
-    {
-        'M': 4864, 'N': 4096, 'K': 4224, 'rowMajorA': 'T', 'rowMajorB': 'N', 'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 256,
-        'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 4, 'SPLIT_K': 1, 'num_warps': 8, 'num_stages': 0, 'waves_per_eu': 0,
-        'matrix_instr_nonkdim': 16, 'kpack': 2
-    },
-    {
-        'M': 4864, 'N': 4096, 'K': 4288, 'rowMajorA': 'T', 'rowMajorB': 'N', 'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 256,
-        'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 4, 'SPLIT_K': 1, 'num_warps': 8, 'num_stages': 0, 'waves_per_eu': 0,
-        'matrix_instr_nonkdim': 16, 'kpack': 2
-    },
-    # 1 workgroup / CU masked loadK
-    {
-        'M': 4864, 'N': 4096, 'K': 4097, 'rowMajorA': 'T', 'rowMajorB': 'N', 'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 256,
-        'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 4, 'SPLIT_K': 1, 'num_warps': 8, 'num_stages': 0, 'waves_per_eu': 0,
-        'matrix_instr_nonkdim': 16, 'kpack': 2
-    },
-    {
-        'M': 4864, 'N': 4096, 'K': 4098, 'rowMajorA': 'T', 'rowMajorB': 'N', 'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 256,
-        'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 4, 'SPLIT_K': 1, 'num_warps': 8, 'num_stages': 0, 'waves_per_eu': 0,
-        'matrix_instr_nonkdim': 16, 'kpack': 2
-    },
-    {
-        'M': 4864, 'N': 4096, 'K': 4100, 'rowMajorA': 'T', 'rowMajorB': 'N', 'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 256,
-        'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 4, 'SPLIT_K': 1, 'num_warps': 8, 'num_stages': 0, 'waves_per_eu': 0,
-        'matrix_instr_nonkdim': 16, 'kpack': 2
-    },
-    {
-        'M': 4864, 'N': 4096, 'K': 4104, 'rowMajorA': 'T', 'rowMajorB': 'N', 'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 256,
-        'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 4, 'SPLIT_K': 1, 'num_warps': 8, 'num_stages': 0, 'waves_per_eu': 0,
-        'matrix_instr_nonkdim': 16, 'kpack': 2
-    },
-    {
-        'M': 4864, 'N': 4096, 'K': 4112, 'rowMajorA': 'T', 'rowMajorB': 'N', 'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 256,
-        'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 4, 'SPLIT_K': 1, 'num_warps': 8, 'num_stages': 0, 'waves_per_eu': 0,
-        'matrix_instr_nonkdim': 16, 'kpack': 2
-    },
-
-    # 2 workgroups / CU
-    {
-        'M': 4864, 'N': 8192, 'K': 4096, 'rowMajorA': 'T', 'rowMajorB': 'N', 'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 256,
-        'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 4, 'SPLIT_K': 1, 'num_warps': 8, 'num_stages': 0, 'waves_per_eu': 0,
-        'matrix_instr_nonkdim': 16, 'kpack': 2
-    },
-    {
-        'M': 4864, 'N': 8192, 'K': 4160, 'rowMajorA': 'T', 'rowMajorB': 'N', 'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 256,
-        'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 4, 'SPLIT_K': 1, 'num_warps': 8, 'num_stages': 0, 'waves_per_eu': 0,
-        'matrix_instr_nonkdim': 16, 'kpack': 2
-    },
-    {
-        'M': 4864, 'N': 8192, 'K': 8192, 'rowMajorA': 'T', 'rowMajorB': 'N', 'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 256,
-        'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 4, 'SPLIT_K': 1, 'num_warps': 8, 'num_stages': 0, 'waves_per_eu': 0,
-        'matrix_instr_nonkdim': 16, 'kpack': 2
-    },
-    {
-        'M': 4864, 'N': 8192, 'K': 8256, 'rowMajorA': 'T', 'rowMajorB': 'N', 'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 256,
-        'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 4, 'SPLIT_K': 1, 'num_warps': 8, 'num_stages': 0, 'waves_per_eu': 0,
-        'matrix_instr_nonkdim': 16, 'kpack': 2
-    },
-], ids=lambda val: f"Config: {val}")
 class TestRegression:
 
     @classmethod
     def setup_class(self):
+        self.slowdown_threshold = 0.97
+
         self.test_results = []
+        self.test_perf_ratios = []
         try:
             with open('gemm-performance-report-reference.yaml', 'r') as ref_file:
                 self.reference_data = yaml.safe_load(ref_file)
@@ -95,18 +27,88 @@ def teardown_class(self):
         with open('gemm-performance-report.yaml', 'w') as out_file:
             yaml.safe_dump(self.test_results, out_file)
 
+    @pytest.mark.parametrize('config', [
+        # M // BLOCK_M * N // BLOCK_N % 304 == 0
+        # 1 workgroup / CU
+        {
+            'M': 4864, 'N': 4096, 'K': 4096, 'rowMajorA': 'T', 'rowMajorB': 'N', 'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N':
+            256, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 4, 'SPLIT_K': 1, 'num_warps': 8, 'num_stages': 0, 'waves_per_eu':
+            0, 'matrix_instr_nonkdim': 16, 'kpack': 2
+        },
+        {
+            'M': 4864, 'N': 4096, 'K': 4160, 'rowMajorA': 'T', 'rowMajorB': 'N', 'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N':
+            256, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 4, 'SPLIT_K': 1, 'num_warps': 8, 'num_stages': 0, 'waves_per_eu':
+            0, 'matrix_instr_nonkdim': 16, 'kpack': 2
+        },
+        {
+            'M': 4864, 'N': 4096, 'K': 4224, 'rowMajorA': 'T', 'rowMajorB': 'N', 'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N':
+            256, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 4, 'SPLIT_K': 1, 'num_warps': 8, 'num_stages': 0, 'waves_per_eu':
+            0, 'matrix_instr_nonkdim': 16, 'kpack': 2
+        },
+        {
+            'M': 4864, 'N': 4096, 'K': 4288, 'rowMajorA': 'T', 'rowMajorB': 'N', 'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N':
+            256, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 4, 'SPLIT_K': 1, 'num_warps': 8, 'num_stages': 0, 'waves_per_eu':
+            0, 'matrix_instr_nonkdim': 16, 'kpack': 2
+        },
+        # 1 workgroup / CU masked loadK
+        {
+            'M': 4864, 'N': 4096, 'K': 4097, 'rowMajorA': 'T', 'rowMajorB': 'N', 'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N':
+            256, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 4, 'SPLIT_K': 1, 'num_warps': 8, 'num_stages': 0, 'waves_per_eu':
+            0, 'matrix_instr_nonkdim': 16, 'kpack': 2
+        },
+        {
+            'M': 4864, 'N': 4096, 'K': 4098, 'rowMajorA': 'T', 'rowMajorB': 'N', 'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N':
+            256, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 4, 'SPLIT_K': 1, 'num_warps': 8, 'num_stages': 0, 'waves_per_eu':
+            0, 'matrix_instr_nonkdim': 16, 'kpack': 2
+        },
+        {
+            'M': 4864, 'N': 4096, 'K': 4100, 'rowMajorA': 'T', 'rowMajorB': 'N', 'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N':
+            256, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 4, 'SPLIT_K': 1, 'num_warps': 8, 'num_stages': 0, 'waves_per_eu':
+            0, 'matrix_instr_nonkdim': 16, 'kpack': 2
+        },
+        {
+            'M': 4864, 'N': 4096, 'K': 4104, 'rowMajorA': 'T', 'rowMajorB': 'N', 'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N':
+            256, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 4, 'SPLIT_K': 1, 'num_warps': 8, 'num_stages': 0, 'waves_per_eu':
+            0, 'matrix_instr_nonkdim': 16, 'kpack': 2
+        },
+        {
+            'M': 4864, 'N': 4096, 'K': 4112, 'rowMajorA': 'T', 'rowMajorB': 'N', 'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N':
+            256, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 4, 'SPLIT_K': 1, 'num_warps': 8, 'num_stages': 0, 'waves_per_eu':
+            0, 'matrix_instr_nonkdim': 16, 'kpack': 2
+        },
+
+        # 2 workgroups / CU
+        {
+            'M': 4864, 'N': 8192, 'K': 4096, 'rowMajorA': 'T', 'rowMajorB': 'N', 'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N':
+            256, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 4, 'SPLIT_K': 1, 'num_warps': 8, 'num_stages': 0, 'waves_per_eu':
+            0, 'matrix_instr_nonkdim': 16, 'kpack': 2
+        },
+        {
+            'M': 4864, 'N': 8192, 'K': 4160, 'rowMajorA': 'T', 'rowMajorB': 'N', 'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N':
+            256, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 4, 'SPLIT_K': 1, 'num_warps': 8, 'num_stages': 0, 'waves_per_eu':
+            0, 'matrix_instr_nonkdim': 16, 'kpack': 2
+        },
+        {
+            'M': 4864, 'N': 8192, 'K': 8192, 'rowMajorA': 'T', 'rowMajorB': 'N', 'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N':
+            256, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 4, 'SPLIT_K': 1, 'num_warps': 8, 'num_stages': 0, 'waves_per_eu':
+            0, 'matrix_instr_nonkdim': 16, 'kpack': 2
+        },
+        {
+            'M': 4864, 'N': 8192, 'K': 8256, 'rowMajorA': 'T', 'rowMajorB': 'N', 'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N':
+            256, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 4, 'SPLIT_K': 1, 'num_warps': 8, 'num_stages': 0, 'waves_per_eu':
+            0, 'matrix_instr_nonkdim': 16, 'kpack': 2
+        },
+    ], ids=lambda val: f"Config: {val}")
     def test_matmul_performance_regression(self, config, record_property):
-        # Get GPU ids
+
         gpus = [0]
         jobs = 1
-
-        # Get element type
         dtype_a = 'fp16'
         dtype_b = 'fp16'
         dtype_c = 'fp16'
-
         rotating_buffer_size = 0
         bias_vector = False
+        bias_size = M if bias_vector else 0
         icache_flush = False
         iters = 200
         benchmark = True
@@ -117,14 +119,11 @@ def test_matmul_performance_regression(self, config, record_property):
 
         M, N, K, col_a, col_b, runConfig = tune_gemm.process_item(deepcopy(config))
 
-        # Before tuning starts, clear cache and previously generated kernel files
         tune_gemm.run_bash_command("rm -rf ~/.triton/cache")
         tune_gemm.run_bash_command(f"rm -rf {tune_gemm.get_filename_myKernels()}")
 
-        # Append new configs into the tuning space
         tune_gemm.generate_matmul_kernels([runConfig])
 
-        bias_size = M if bias_vector else 0
         minTime, bestConfig, compile_time, profile_time, post_time = tune_gemm.tune_gemm_config(
             M, N, K, col_a, col_b, dtype_a, dtype_b, dtype_c, init_type, [runConfig], benchmark, jobs, iters,
             skipWarmup=skipWarmup, num_threads=num_threads, gpus=gpus, verbose=verbose_level,
@@ -134,11 +133,13 @@ def test_matmul_performance_regression(self, config, record_property):
         perf_tflops = lambda us: 2 * M * N * K * 1e-12 / (us * 1e-6)
         tri_tflops = perf_tflops(minTime)
 
+        record_property("TFlops", f"{tri_tflops:.2f}")
+        record_property("MinTime", f"{minTime:.2f}")
+
         # Add to global results
         self.test_results.append({'config': config, 'tflops': float(tri_tflops)})
 
         # Look for reference run
-
         reference_run = None
         for run in self.reference_data:
             if run['config'] == config:
@@ -147,9 +148,18 @@ def test_matmul_performance_regression(self, config, record_property):
 
         if reference_run is not None:
             performance_ratio = tri_tflops / reference_run['tflops']
-            slowdown_threshold = 0.97
+            self.test_perf_ratios.append(performance_ratio)
             regression_percent = (100.0 * (1.0 - performance_ratio))
             record_property("Performance difference (lower is better)", f"{regression_percent:.2f}%")
-            assert performance_ratio > slowdown_threshold, f'Performance regressed by {regression_percent:.2f}% (threshold={((1.0 - slowdown_threshold) * 100.0 ):.2f}%)'
+            assert performance_ratio > self.slowdown_threshold, f'Performance regressed by {regression_percent:.2f}% (threshold={((1.0 - self.slowdown_threshold) * 100.0 ):.2f}%)'
         else:
             pytest.skip("No performance reference found!")
+
+    def test_overall_performance_difference(self, record_property):
+        if len(self.test_perf_ratios) < 2:
+            pytest.skip("Overall results will be tested if test count > 2")
+        perf_diff_mean = statistics.mean(self.test_perf_ratios)
+        regression_percent = (100.0 * (1.0 - perf_diff_mean))
+
+        record_property("Overall performance difference (mean)", f"{regression_percent:.2f}%")
+        assert perf_diff_mean > self.slowdown_threshold, f'Performance regressed by {regression_percent:.2f}% (threshold={((1.0 - self.slowdown_threshold) * 100.0 ):.2f}%)'