diff --git a/projects/composablekernel/tile_engine/ops/gemm_streamk/CMakeLists.txt b/projects/composablekernel/tile_engine/ops/gemm_streamk/CMakeLists.txt
index ae453ea11bda..e1845e3dcf50 100644
--- a/projects/composablekernel/tile_engine/ops/gemm_streamk/CMakeLists.txt
+++ b/projects/composablekernel/tile_engine/ops/gemm_streamk/CMakeLists.txt
@@ -2,8 +2,8 @@
 # SPDX-License-Identifier: MIT
 
 set(GEMM_STREAMK_DATATYPE "fp8;fp16" CACHE STRING "List of datatypes for GEMM (semicolon-separated)")
-set(GEMM_STREAMK_LAYOUT "rcr" CACHE STRING "List of layout for GEMM (semicolon-separated)")
-set(GEMM_CONFIG_FILE "" CACHE STRING "Custom config file name (without path, must be in configs/ folder)")
+set(GEMM_STREAMK_LAYOUT "rcr;rrr;crr;ccr" CACHE STRING "List of layout for GEMM (semicolon-separated)")
+set(GEMM_STREAMK_CONFIG_FILE "" CACHE STRING "Custom config file name (without path, must be in configs/ folder)")
 option(ENABLE_CCACHE_GEMM "Enable ccache for GEMM ops compilation" OFF)
 
 # Store the directory path for use in functions
@@ -116,19 +116,19 @@ function(build_individual_gemm_targets datatype layout)
     
     # Choose config file
     # Priority order:
-    # 1. Environment variable GEMM_CONFIG_FILE
-    # 2. CMake variable GEMM_CONFIG_FILE  
+    # 1. Environment variable GEMM_STREAMK_CONFIG_FILE
+    # 2. CMake variable GEMM_STREAMK_CONFIG_FILE  
     # 3. Default based on layout
     
     # Check environment variable first
-    if(DEFINED ENV{GEMM_CONFIG_FILE} AND NOT "$ENV{GEMM_CONFIG_FILE}" STREQUAL "")
-        set(config_filename "$ENV{GEMM_CONFIG_FILE}")
+    if(DEFINED ENV{GEMM_STREAMK_CONFIG_FILE} AND NOT "$ENV{GEMM_STREAMK_CONFIG_FILE}" STREQUAL "")
+        set(config_filename "$ENV{GEMM_STREAMK_CONFIG_FILE}")
         set(json_blob "${CMAKE_CURRENT_LIST_DIR}/configs/${config_filename}")
         message(STATUS "  Using config from environment variable: ${config_filename}")
-    elseif(NOT "${GEMM_CONFIG_FILE}" STREQUAL "")
+    elseif(NOT "${GEMM_STREAMK_CONFIG_FILE}" STREQUAL "")
         # Use CMake variable if set
-        set(json_blob "${CMAKE_CURRENT_LIST_DIR}/configs/${GEMM_CONFIG_FILE}")
-        message(STATUS "  Using custom config: ${GEMM_CONFIG_FILE}")
+        set(json_blob "${CMAKE_CURRENT_LIST_DIR}/configs/${GEMM_STREAMK_CONFIG_FILE}")
+        message(STATUS "  Using custom config: ${GEMM_STREAMK_CONFIG_FILE}")
     else()
         # Use default config for all layouts
         set(json_blob "${CMAKE_CURRENT_LIST_DIR}/configs/default_config.json")
diff --git a/projects/composablekernel/tile_engine/ops/gemm_streamk/configs/default_config.json b/projects/composablekernel/tile_engine/ops/gemm_streamk/configs/default_config.json
index 07281bdf9aca..96c55715522b 100644
--- a/projects/composablekernel/tile_engine/ops/gemm_streamk/configs/default_config.json
+++ b/projects/composablekernel/tile_engine/ops/gemm_streamk/configs/default_config.json
@@ -98,7 +98,7 @@
         },
         "reduction_strategy": {
             "values": [
-                "atomic"
+                "atomic", "linear", "tree"
             ]
         }
     }
diff --git a/projects/composablekernel/tile_engine/ops/gemm_streamk/gemm_streamk_benchmark.py b/projects/composablekernel/tile_engine/ops/gemm_streamk/gemm_streamk_benchmark.py
new file mode 100644
index 000000000000..cc9025b4b1ce
--- /dev/null
+++ b/projects/composablekernel/tile_engine/ops/gemm_streamk/gemm_streamk_benchmark.py
@@ -0,0 +1,677 @@
+# Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
+# SPDX-License-Identifier: MIT
+
+import sys
+import json
+import subprocess
+import argparse
+import csv
+import time
+from pathlib import Path
+from typing import List, Dict, Tuple, Optional
+
+
+class GemmBenchmark:
+    def __init__(self, build_dir: str, verbose: bool = False):
+        self.build_dir = Path(build_dir)
+        self.verbose = verbose
+        self.results = []
+
+    def discover_kernels(self) -> List[Path]:
+        """Find all benchmark_gemm_streamk_* executables in the build directory"""
+        bin_dir = self.build_dir / "bin"
+        if not bin_dir.exists():
+            print(f"Error: Binary directory {bin_dir} does not exist")
+            return []
+
+        kernels = list(bin_dir.glob("benchmark_gemm_streamk_*"))
+        if self.verbose:
+            print(f"Found {len(kernels)} kernel executables")
+            for k in kernels:
+                print(f"  - {k.name}")
+        return kernels
+
+    def extract_kernel_info(self, kernel_path: Path) -> Dict[str, str]:
+        """Extract comprehensive kernel information from filename"""
+        name = kernel_path.stem
+
+        # Initialize with basic info
+        info = {
+            "executable": str(kernel_path),
+            "name": name,
+            "data_type": "unknown",
+            "layout": "unknown",
+            "pipeline": "unknown",
+            "scheduler": "unknown",
+            "epilogue": "unknown",
+            "reduction_strategy": "unknown",
+        }
+
+        # Parse the kernel name pattern:
+        # benchmark_gemm_streamk_fp16_rcr_compv3_cshuffle_intrawave_False_False_False_False_False_256x256x32_2x2x1_4x64x16
+        parts = name.split("_")
+
+        if len(parts) >= 3:
+            # Extract data type (4th part after benchmark_gemm_streamk)
+            info["data_type"] = parts[3] if len(parts) > 3 else "unknown"
+
+            # Extract layout (5th part)
+            info["layout"] = parts[4] if len(parts) > 4 else "unknown"
+
+            # Extract pipeline (6th part)
+            info["pipeline"] = parts[5] if len(parts) > 5 else "unknown"
+
+            # Extract epilogue (7th part)
+            info["epilogue"] = parts[6] if len(parts) > 6 else "unknown"
+
+            # Extract scheduler (8th part)
+            info["scheduler"] = parts[7] if len(parts) > 7 else "unknown"
+            
+            # Extract reduction strategy (9th part)
+            info["reduction_strategy"] = parts[8] if len(parts) > 8 else "unknown"
+
+        # Extract detailed configuration from the end of the name
+        config_info = self.parse_detailed_config(name)
+        info.update(config_info)
+
+        # Generate config ID
+        info["config_id"] = self.generate_config_id(info)
+
+        return info
+
+    def parse_detailed_config(self, kernel_name: str) -> Dict:
+        """Parse detailed configuration from kernel name"""
+        config = {
+            "tile_sizes": {"tile_m": 0, "tile_n": 0, "tile_k": 0},
+            "warp_config": {"warp_m": 0, "warp_n": 0, "warp_k": 0},
+            "warp_tile": {"warp_tile_m": 0, "warp_tile_n": 0, "warp_tile_k": 0},
+            "optimization_flags": {
+                "pad_m": False,
+                "pad_n": False,
+                "pad_k": False,
+                "persistent": False,
+            },
+        }
+
+        # Split by underscore and look for patterns
+        parts = kernel_name.split("_")
+
+        # Look for boolean flags (sequence of True/False values)
+        bool_sequence = []
+        for i, part in enumerate(parts):
+            if part in ["True", "False"]:
+                bool_sequence.append(part == "True")
+                # Continue collecting consecutive boolean values
+                j = i + 1
+                while j < len(parts) and parts[j] in ["True", "False"]:
+                    bool_sequence.append(parts[j] == "True")
+                    j += 1
+                break
+
+        # Assign boolean flags if we found them
+        # Order: pad_m, pad_n, pad_k, persistent (4 flags total)
+        if len(bool_sequence) >= 4:
+            config["optimization_flags"]["pad_m"] = bool_sequence[0]
+            config["optimization_flags"]["pad_n"] = bool_sequence[1]
+            config["optimization_flags"]["pad_k"] = bool_sequence[2]
+            config["optimization_flags"]["persistent"] = bool_sequence[3]
+
+        # Look for tile size patterns (e.g., 256x256x32_2x2x1_4x64x16)
+        # The pattern is: tile_sizes_warp_config_warp_tile
+        dimension_groups = []
+        for part in parts:
+            if "x" in part and len(part.split("x")) == 3:
+                try:
+                    dims = [int(x) for x in part.split("x")]
+                    if all(d > 0 for d in dims):
+                        dimension_groups.append(dims)
+                except ValueError:
+                    continue
+
+        # Assign dimensions based on order and magnitude
+        if len(dimension_groups) >= 3:
+            # Sort by magnitude to identify: largest=tile_sizes, smallest=warp_config, middle=warp_tile
+            sorted_groups = sorted(dimension_groups, key=lambda x: max(x), reverse=True)
+
+            # Largest dimensions = tile sizes
+            config["tile_sizes"]["tile_m"] = sorted_groups[0][0]
+            config["tile_sizes"]["tile_n"] = sorted_groups[0][1]
+            config["tile_sizes"]["tile_k"] = sorted_groups[0][2]
+
+            # Smallest dimensions = warp config
+            config["warp_config"]["warp_m"] = sorted_groups[2][0]
+            config["warp_config"]["warp_n"] = sorted_groups[2][1]
+            config["warp_config"]["warp_k"] = sorted_groups[2][2]
+
+            # Middle dimensions = warp tile
+            config["warp_tile"]["warp_tile_m"] = sorted_groups[1][0]
+            config["warp_tile"]["warp_tile_n"] = sorted_groups[1][1]
+            config["warp_tile"]["warp_tile_k"] = sorted_groups[1][2]
+        elif len(dimension_groups) == 2:
+            # If only 2 groups, assign based on magnitude
+            sorted_groups = sorted(dimension_groups, key=lambda x: max(x), reverse=True)
+
+            # Larger = tile sizes
+            config["tile_sizes"]["tile_m"] = sorted_groups[0][0]
+            config["tile_sizes"]["tile_n"] = sorted_groups[0][1]
+            config["tile_sizes"]["tile_k"] = sorted_groups[0][2]
+
+            # Smaller = warp config
+            config["warp_config"]["warp_m"] = sorted_groups[1][0]
+            config["warp_config"]["warp_n"] = sorted_groups[1][1]
+            config["warp_config"]["warp_k"] = sorted_groups[1][2]
+        elif len(dimension_groups) == 1:
+            # Only one group - assume it's tile sizes
+            config["tile_sizes"]["tile_m"] = dimension_groups[0][0]
+            config["tile_sizes"]["tile_n"] = dimension_groups[0][1]
+            config["tile_sizes"]["tile_k"] = dimension_groups[0][2]
+
+        return config
+
+    def generate_config_id(self, info: Dict) -> str:
+        """Generate a compact config ID from kernel info"""
+        # Create a compact identifier
+        parts = [
+            info.get("data_type", "unk"),
+            info.get("layout", "unk"),
+            info.get("pipeline", "unk"),
+            info.get("scheduler", "unk"),
+            info.get("reduction_strategy", "unk"),
+        ]
+
+        # Add tile configuration if available
+        tile_sizes = info.get("tile_sizes", {})
+        if tile_sizes.get("tile_m", 0) > 0:
+            tile_str = (
+                f"{tile_sizes['tile_m']}x{tile_sizes['tile_n']}x{tile_sizes['tile_k']}"
+            )
+            parts.append(tile_str)
+
+        # Add warp config if available
+        warp_config = info.get("warp_config", {})
+        if warp_config.get("warp_m", 0) > 0:
+            warp_str = f"w{warp_config['warp_m']}x{warp_config['warp_n']}x{warp_config['warp_k']}"
+            parts.append(warp_str)
+
+        # Add warp tile if available
+        warp_tile = info.get("warp_tile", {})
+        if warp_tile.get("warp_tile_m", 0) > 0:
+            warp_tile_str = f"wt{warp_tile['warp_tile_m']}x{warp_tile['warp_tile_n']}x{warp_tile['warp_tile_k']}"
+            parts.append(warp_tile_str)
+
+        return "_".join(parts)
+
+    def run_kernel(self, kernel_path: Path, params: Dict[str, str]) -> Optional[Dict]:
+        """Run a single kernel with given parameters and save output to individual JSON file"""
+        # Create results directory
+        results_dir = self.build_dir / "results"
+        results_dir.mkdir(exist_ok=True)
+
+        # Generate unique JSON filename for this kernel
+        json_file = results_dir / f"{kernel_path.stem}.json"
+
+        cmd = [str(kernel_path)]
+
+        # Add parameters
+        for key, value in params.items():
+            cmd.append(f"-{key}={value}")
+
+        # Add JSON output flag for clean JSON output
+        cmd.append("-json_output=true")
+
+        if self.verbose:
+            print(f"Running: {' '.join(cmd)}")
+
+        try:
+            result = subprocess.run(cmd, capture_output=True, text=True, timeout=60)
+
+            if result.returncode != 0:
+                print(f"Error running {kernel_path.name}: {result.stderr}")
+                return None
+
+            # Save raw output to individual JSON file
+            output = result.stdout.strip()
+            if output:
+                with open(json_file, "w") as f:
+                    f.write(output)
+
+                # Parse the JSON file
+                return self.parse_json_file(json_file)
+            else:
+                print(f"No output from {kernel_path.name}")
+                return None
+
+        except subprocess.TimeoutExpired:
+            print(f"Timeout running {kernel_path.name}")
+            return None
+        except Exception as e:
+            print(f"Error running {kernel_path.name}: {e}")
+            return None
+
+    def parse_json_file(self, json_file: Path) -> Optional[Dict]:
+        """Parse JSON data from individual kernel output file"""
+        try:
+            with open(json_file, "r") as f:
+                content = f.read().strip()
+
+            # Parse the JSON directly since executables produce clean JSON
+            data = json.loads(content)
+
+            # Return the complete JSON data as-is, just add some convenience fields
+            result = data.copy()
+            if "perf_result" in data:
+                perf = data["perf_result"]
+                # Add convenience fields for backward compatibility
+                result["time_ms"] = perf.get("latency(ms)", 0)
+                result["tflops"] = perf.get("tflops(TFlops)", 0)
+                result["bandwidth_gb_s"] = perf.get("bandwidth(GB/s)", 0)
+
+            return result
+
+        except json.JSONDecodeError as e:
+            if self.verbose:
+                print(f"Failed to parse JSON from {json_file}: {e}")
+            return None
+        except Exception as e:
+            if self.verbose:
+                print(f"Error reading JSON file {json_file}: {e}")
+            return None
+
+    def benchmark_problem_size(
+        self,
+        kernels: List[Path],
+        m: int,
+        n: int,
+        k: int,
+        verify: int = 0,
+        warmup: int = 50,
+        repeat: int = 100,
+        flush_cache: bool = True,
+        rotating_count: int = 1000,
+    ) -> List[Dict]:
+        """Benchmark all kernels for a specific problem size"""
+        results = []
+
+        params = {
+            "m": m,
+            "n": n,
+            "k": k,
+            "verify": verify,
+            "warmup": warmup,
+            "repeat": repeat,
+            "flush_cache": str(flush_cache).lower(),
+            "rotating_count": rotating_count,
+        }
+
+        print(f"\nBenchmarking M={m}, N={n}, K={k}")
+
+        for kernel_path in kernels:
+            kernel_info = self.extract_kernel_info(kernel_path)
+            result = self.run_kernel(kernel_path, params)
+
+            if result:
+                # Create new structured result format
+                structured_result = {
+                    "name": kernel_info["name"],  # Add name field for compatibility
+                    "config_id": kernel_info["config_id"],
+                    "problem": result.get("problem", {}),
+                    "perf_result": result.get("perf_result", {}),
+                    "config": {
+                        "data_type": kernel_info["data_type"],
+                        "layout": kernel_info["layout"],
+                        "pipeline": kernel_info["pipeline"],
+                        "scheduler": kernel_info["scheduler"],
+                        "epilogue": kernel_info["epilogue"],
+                        "reduction_strategy": kernel_info["reduction_strategy"],
+                        "tile_sizes": kernel_info.get("tile_sizes", {}),
+                        "warp_config": kernel_info.get("warp_config", {}),
+                        "warp_tile": kernel_info.get("warp_tile", {}),
+                        "optimization_flags": kernel_info.get("optimization_flags", {}),
+                    },
+                    "executable": kernel_info["executable"],
+                    # Keep backward compatibility fields
+                    "time_ms": result.get("time_ms", 0),
+                    "tflops": result.get("tflops", 0),
+                    "bandwidth_gb_s": result.get("bandwidth_gb_s", 0),
+                }
+
+                results.append(structured_result)
+
+                if self.verbose:
+                    print(
+                        f"  {kernel_info['config_id']}: {structured_result['tflops']:.2f} TFLOPS, {structured_result['bandwidth_gb_s']:.2f} GB/s, {structured_result['time_ms']:.2f}ms"
+                    )
+
+        return results
+
+    def find_best_kernel(
+        self, results: List[Dict], metric: str = "tflops"
+    ) -> Optional[Dict]:
+        """Find the best performing kernel based on metric"""
+        if not results:
+            return None
+
+        if metric == "tflops":
+            return max(results, key=lambda x: x.get("tflops", 0))
+        elif metric == "time_ms":
+            return min(results, key=lambda x: x.get("time_ms", float("inf")))
+        elif metric == "bandwidth_gb_s":
+            return max(results, key=lambda x: x.get("bandwidth_gb_s", 0))
+        else:
+            raise ValueError(f"Unknown metric: {metric}")
+
+    def benchmark_sweep(
+        self,
+        problem_sizes: List[Tuple[int, int, int]],
+        verify: bool = False,
+        warmup: int = 50,
+        repeat: int = 100,
+        flush_cache: bool = True,
+        rotating_count: int = 1000,
+    ) -> Dict:
+        """Run comprehensive benchmark sweep"""
+        kernels = self.discover_kernels()
+        if not kernels:
+            print("No kernels found!")
+            return {}
+
+        all_results = []
+        best_kernels = {}
+
+        for m, n, k in problem_sizes:
+            results = self.benchmark_problem_size(
+                kernels,
+                m,
+                n,                    k,
+                verify=2 if verify else 0,
+                warmup=warmup,
+                repeat=repeat,
+                flush_cache=flush_cache,
+                rotating_count=rotating_count,
+            )
+
+            all_results.extend(results)
+
+            # Find best kernel for this configuration
+            best = self.find_best_kernel(results)
+            if best:
+                key = f"m{m}_n{n}_k{k}"
+                best_kernels[key] = best
+                print(
+                    f"Best for {key}: {best['name']} ({best['tflops']:.2f} TFLOPS, {best['bandwidth_gb_s']:.2f} GB/s, {best['time_ms']:.2f}ms)"
+                )
+
+        self.results = all_results
+        return best_kernels
+
+    def export_csv(self, filename: str):
+        """Export all results to CSV"""
+        if not self.results:
+            print("No results to export")
+            return
+
+        # Get all unique keys from results
+        all_keys = set()
+        for result in self.results:
+            all_keys.update(result.keys())
+
+        # Sort keys for consistent output
+        fieldnames = sorted(all_keys)
+
+        with open(filename, "w", newline="") as csvfile:
+            writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
+            writer.writeheader()
+            writer.writerows(self.results)
+
+        print(f"Results exported to {filename}")
+
+    def export_best_kernels(self, best_kernels: Dict, filename: str):
+        """Export best kernel selections to file"""
+        with open(filename, "w") as f:
+            f.write("# Best kernel selections\n")
+            f.write(
+                "# Format: problem_size -> kernel_name (TFLOPS, bandwidth, latency)\n\n"
+            )
+
+            for key, kernel in sorted(best_kernels.items()):
+                f.write(
+                    f"{key}: {kernel['name']} ({kernel['tflops']:.2f} TFLOPS, {kernel['bandwidth_gb_s']:.2f} GB/s, {kernel['time_ms']:.2f}ms)\n"
+                )
+
+        print(f"Best kernels exported to {filename}")
+
+    def export_json(self, filename: str, best_kernels: Dict = None):
+        """Export all results and best kernels to JSON with comprehensive metadata"""
+        from datetime import datetime
+
+        # Calculate comprehensive summary statistics for all metrics
+        successful_results = [r for r in self.results if r.get("tflops", 0) > 0]
+
+        tflops_values = [r.get("tflops", 0) for r in successful_results]
+        bandwidth_values = [r.get("bandwidth_gb_s", 0) for r in successful_results]
+        latency_values = [
+            r.get("time_ms", 0) for r in successful_results if r.get("time_ms", 0) > 0
+        ]
+
+        # Performance breakdown by kernel type
+        pipeline_stats = {}
+        scheduler_stats = {}
+        data_type_stats = {}
+
+        for result in successful_results:
+            # Get config info from the new structure
+            config = result.get("config", {})
+
+            # Pipeline statistics
+            pipeline = config.get("pipeline", "unknown")
+            if pipeline not in pipeline_stats:
+                pipeline_stats[pipeline] = {
+                    "count": 0,
+                    "avg_tflops": 0,
+                    "best_tflops": 0,
+                }
+            pipeline_stats[pipeline]["count"] += 1
+            pipeline_stats[pipeline]["best_tflops"] = max(
+                pipeline_stats[pipeline]["best_tflops"], result.get("tflops", 0)
+            )
+
+            # Scheduler statistics
+            scheduler = config.get("scheduler", "unknown")
+            if scheduler not in scheduler_stats:
+                scheduler_stats[scheduler] = {
+                    "count": 0,
+                    "avg_tflops": 0,
+                    "best_tflops": 0,
+                }
+            scheduler_stats[scheduler]["count"] += 1
+            scheduler_stats[scheduler]["best_tflops"] = max(
+                scheduler_stats[scheduler]["best_tflops"], result.get("tflops", 0)
+            )
+
+            # Data type statistics
+            data_type = config.get("data_type", "unknown")
+            if data_type not in data_type_stats:
+                data_type_stats[data_type] = {
+                    "count": 0,
+                    "avg_tflops": 0,
+                    "best_tflops": 0,
+                }
+            data_type_stats[data_type]["count"] += 1
+            data_type_stats[data_type]["best_tflops"] = max(
+                data_type_stats[data_type]["best_tflops"], result.get("tflops", 0)
+            )
+
+        # Calculate averages for breakdown stats
+        for stats_dict, field_name in [
+            (pipeline_stats, "pipeline"),
+            (scheduler_stats, "scheduler"),
+            (data_type_stats, "data_type"),
+        ]:
+            for key in stats_dict:
+                relevant_results = [
+                    r
+                    for r in successful_results
+                    if r.get("config", {}).get(field_name, "unknown") == key
+                ]
+                if relevant_results:
+                    stats_dict[key]["avg_tflops"] = sum(
+                        r.get("tflops", 0) for r in relevant_results
+                    ) / len(relevant_results)
+
+        output_data = {
+            "benchmark_metadata": {
+                "timestamp": datetime.now().isoformat(),
+                "total_kernels_tested": len(self.results),
+                "unique_kernels": len(
+                    set(r.get("name", "unknown") for r in self.results)
+                ),
+                "successful_runs": len(successful_results),
+                "failed_runs": len(self.results) - len(successful_results),
+            },
+            "performance_summary": {
+                "tflops_stats": {
+                    "best": max(tflops_values, default=0),
+                    "average": sum(tflops_values) / len(tflops_values)
+                    if tflops_values
+                    else 0,
+                    "min": min(tflops_values, default=0),
+                    "median": sorted(tflops_values)[len(tflops_values) // 2]
+                    if tflops_values
+                    else 0,
+                },
+                "bandwidth_stats": {
+                    "best_gb_s": max(bandwidth_values, default=0),
+                    "average_gb_s": sum(bandwidth_values) / len(bandwidth_values)
+                    if bandwidth_values
+                    else 0,
+                    "min_gb_s": min(bandwidth_values, default=0),
+                    "median_gb_s": sorted(bandwidth_values)[len(bandwidth_values) // 2]
+                    if bandwidth_values
+                    else 0,
+                },
+                "latency_stats": {
+                    "best_ms": min(latency_values, default=0),
+                    "average_ms": sum(latency_values) / len(latency_values)
+                    if latency_values
+                    else 0,
+                    "max_ms": max(latency_values, default=0),
+                    "median_ms": sorted(latency_values)[len(latency_values) // 2]
+                    if latency_values
+                    else 0,
+                },
+                "kernel_type_breakdown": {
+                    "by_pipeline": pipeline_stats,
+                    "by_scheduler": scheduler_stats,
+                    "by_data_type": data_type_stats,
+                },
+                "total_problem_configurations": len(best_kernels)
+                if best_kernels
+                else 0,
+            },
+            "kernel_results": self.results,
+            "best_kernels_by_problem": best_kernels or {},
+        }
+
+        with open(filename, "w") as f:
+            json.dump(output_data, f, indent=2)
+
+        print(f"JSON results exported to {filename}")
+        print(f"  - Total kernels: {len(self.results)}")
+        print(f"  - Successful runs: {len(successful_results)}")
+        print(f"  - Best TFLOPS: {max(tflops_values, default=0):.2f}")
+        print(f"  - Best bandwidth: {max(bandwidth_values, default=0):.2f} GB/s")
+        print(f"  - Best latency: {min(latency_values, default=0):.2f}ms")
+
+
+def main():
+    parser = argparse.ArgumentParser(description="GEMM Kernel Benchmarking Tool")
+    parser.add_argument(
+        "build_dir", help="Build directory containing kernel executables"
+    )
+    parser.add_argument(
+        "--problem-sizes",
+        nargs="+",
+        default=["1024,1024,1024", "2048,2048,2048", "4096,4096,4096"],
+        help="Problem sizes as M,N,K tuples",
+    )
+    parser.add_argument(
+        "--split-k", nargs="+", type=int, default=[1], help="Split-K values to test"
+    )
+    parser.add_argument("--verify", action="store_true", help="Enable verification")
+    parser.add_argument(
+        "--csv", default="gemm_benchmark_results.csv", help="CSV output filename"
+    )
+    parser.add_argument(
+        "--best", default="best_kernels.txt", help="Best kernels output filename"
+    )
+    parser.add_argument("--verbose", action="store_true", help="Verbose output")
+    parser.add_argument(
+        "--warmup",
+        type=int,
+        default=50,
+        help="Number of warmup iterations (default: 50)",
+    )
+    parser.add_argument(
+        "--repeat",
+        type=int,
+        default=100,
+        help="Number of benchmark iterations (default: 100)",
+    )
+    parser.add_argument(
+        "--flush-cache",
+        action="store_true",
+        default=True,
+        help="Enable cache flushing (default: True)",
+    )
+    parser.add_argument(
+        "--rotating-count",
+        type=int,
+        default=1000,
+        help="Number of iterations to rotate cache (default: 1000)",
+    )
+    parser.add_argument("--json", help="JSON output filename (optional)")
+
+    args = parser.parse_args()
+
+    # Parse problem sizes
+    problem_sizes = []
+    for size_str in args.problem_sizes:
+        try:
+            m, n, k = map(int, size_str.split(","))
+            problem_sizes.append((m, n, k))
+        except ValueError:
+            print(f"Invalid problem size: {size_str}")
+            return 1
+
+    # Create benchmark instance
+    benchmark = GemmBenchmark(args.build_dir, verbose=args.verbose)
+
+    # Run benchmark sweep
+    print("Starting GEMM kernel benchmark sweep...")
+    start_time = time.time()
+
+    best_kernels = benchmark.benchmark_sweep(
+        problem_sizes=problem_sizes,
+        verify=args.verify,
+        warmup=args.warmup,
+        repeat=args.repeat,
+        flush_cache=args.flush_cache,
+        rotating_count=args.rotating_count,
+    )
+
+    elapsed_time = time.time() - start_time
+    print(f"\nBenchmark completed in {elapsed_time:.2f} seconds")
+
+    # Export results
+    benchmark.export_csv(args.csv)
+    benchmark.export_best_kernels(best_kernels, args.best)
+
+    # Export JSON if requested
+    if args.json:
+        benchmark.export_json(args.json, best_kernels)
+
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/projects/composablekernel/tile_engine/ops/gemm_streamk/gemm_streamk_instance_builder.py b/projects/composablekernel/tile_engine/ops/gemm_streamk/gemm_streamk_instance_builder.py
index 5c87d6f50c39..9e5ded5c98d3 100644
--- a/projects/composablekernel/tile_engine/ops/gemm_streamk/gemm_streamk_instance_builder.py
+++ b/projects/composablekernel/tile_engine/ops/gemm_streamk/gemm_streamk_instance_builder.py
@@ -436,9 +436,9 @@ def _generate_kernel_instance(self, tile_config, trait_combo, is_header=True):
     static constexpr ck_tile::index_t WarpTileK = {tile_config["warp_tile_k"]};
 
     // Traits
-    static constexpr bool kPadM = {"true" if pad_m == "true" else "false"};
-    static constexpr bool kPadN = {"true" if pad_n == "true" else "false"};
-    static constexpr bool kPadK = {"true" if pad_k == "true" else "false"};
+    static constexpr bool kPadM = {"true" if pad_m in [True, "true"] else "false"};
+    static constexpr bool kPadN = {"true" if pad_n in [True, "true"] else "false"};
+    static constexpr bool kPadK = {"true" if pad_k in [True, "true"] else "false"};
     static constexpr bool Preshuffle = false;
 
     static constexpr bool DoubleSmemBuffer = {"true" if pipeline == "compv4" else "false"};
@@ -447,8 +447,7 @@ def _generate_kernel_instance(self, tile_config, trait_combo, is_header=True):
     static constexpr bool NumWaveGroup       = 1;
 
     static constexpr bool TransposeC = false;
-    static constexpr bool UsePersistentKernel = {"true" if str(persistent).lower() == "true" else "false"};
-    static constexpr bool UseStructuredSparsity = false;
+    static constexpr bool UsePersistentKernel = {"true" if persistent in [True, "true"] else "false"};
     static constexpr ck_tile::index_t NumWaveGroups = 1;
     static constexpr ck_tile::StreamKReductionStrategy reduction_strategy = {reduction_strategy_map.get(reduction_strategy, "ck_tile::StreamKReductionStrategy::Linear")};
 
@@ -697,11 +696,11 @@ def write_kernel_list(self):
                     pipeline,
                     epilogue,
                     scheduler,
+                    reduction_strategy,
                     pad_m,
                     pad_n,
                     pad_k,
                     persistent,
-                    reduction_strategy,
                 ) = trait_combo
 
                 # Create kernel name with proper boolean capitalization
@@ -873,10 +872,10 @@ def main():
             trait_parts[1],  # epilogue
             trait_parts[2],  # scheduler
             trait_parts[3],  # reduction_strategy
-            trait_parts[4] == "false",  # pad_m
-            trait_parts[5] == "false",  # pad_n
-            trait_parts[6] == "false",  # pad_k
-            trait_parts[7],  # persistent
+            trait_parts[4] == "True",  # pad_m
+            trait_parts[5] == "True",  # pad_n
+            trait_parts[6] == "True",  # pad_k
+            trait_parts[7] == "True",  # persistent
         )
 
         # Generate the kernel