1  """
  2  Programming Model Comparison: cuTile vs Triton vs Traditional CUDA.
  3  
  4  This module provides a detailed comparison of different GPU programming
  5  approaches for the same GEMM operation.
  6  
  7  Requirements: 5.3
  8  """
  9  
 10  from dataclasses import dataclass
 11  from typing import Any
 12  
 13  
 14  @dataclass
 15  class FeatureComparison:
 16      """Comparison of a specific feature across programming models."""
 17  
 18      feature: str
 19      cutile: str
 20      triton: str
 21      cuda: str
 22      notes: str = ""
 23  
 24  
 25  def compare_programming_models() -> dict[str, Any]:
 26      """
 27      Compare cuTile, Triton, and traditional CUDA programming models.
 28  
 29      Returns a dictionary containing:
 30      - feature_table: List of feature comparisons
 31      - code_examples: Example code for each model
 32      - pros_cons: Advantages and disadvantages
 33      """
 34  
 35      # Feature comparison table
 36      features = [
 37          FeatureComparison(
 38              feature="Abstraction Level",
 39              cutile="Tile (highest)",
 40              triton="Block (medium)",
 41              cuda="Thread (lowest)",
 42              notes="Higher abstraction = easier programming, less control",
 43          ),
 44          FeatureComparison(
 45              feature="Thread Management",
 46              cutile="Automatic",
 47              triton="Semi-automatic",
 48              cuda="Manual",
 49              notes="cuTile compiler handles all thread mapping",
 50          ),
 51          FeatureComparison(
 52              feature="Memory Management",
 53              cutile="Automatic",
 54              triton="Explicit (simplified)",
 55              cuda="Manual",
 56              notes="Shared memory, registers managed by compiler",
 57          ),
 58          FeatureComparison(
 59              feature="Tensor Core Support",
 60              cutile="Automatic",
 61              triton="tl.dot()",
 62              cuda="wmma/mma intrinsics",
 63              notes="cuTile auto-detects when to use Tensor Cores",
 64          ),
 65          FeatureComparison(
 66              feature="Language",
 67              cutile="Python DSL",
 68              triton="Python DSL",
 69              cuda="C++/CUDA",
 70              notes="Python DSLs enable rapid prototyping",
 71          ),
 72          FeatureComparison(
 73              feature="Hardware Support",
 74              cutile="NVIDIA only",
 75              triton="NVIDIA + AMD",
 76              cuda="NVIDIA only",
 77              notes="Triton has broader hardware support",
 78          ),
 79          FeatureComparison(
 80              feature="Maturity",
 81              cutile="Experimental",
 82              triton="Production",
 83              cuda="Mature",
 84              notes="CUDA has 15+ years of development",
 85          ),
 86          FeatureComparison(
 87              feature="Auto-tuning",
 88              cutile="Built-in",
 89              triton="Manual configs",
 90              cuda="Manual",
 91              notes="cuTile aims for automatic optimization",
 92          ),
 93          FeatureComparison(
 94              feature="Debugging",
 95              cutile="Limited",
 96              triton="Moderate",
 97              cuda="Excellent",
 98              notes="CUDA has mature debugging tools",
 99          ),
100          FeatureComparison(
101              feature="Performance Ceiling",
102              cutile="High (theoretical)",
103              triton="High",
104              cuda="Highest",
105              notes="Manual CUDA can achieve peak performance",
106          ),
107      ]
108  
109      # Code examples
110      code_examples = {
111          "cutile": '''
112  # cuTile GEMM (Conceptual)
113  @cutile.kernel
114  def gemm(A: Tile[M, K], B: Tile[K, N], C: Tile[M, N]):
115      """
116      Simple GEMM in cuTile.
117      Compiler handles:
118      - Tiling strategy
119      - Shared memory allocation
120      - Thread mapping
121      - Tensor Core usage
122      """
123      C[:, :] = A @ B
124  ''',
125          "triton": """
126  # Triton GEMM
127  @triton.jit
128  def gemm_kernel(
129      A, B, C,
130      M, N, K,
131      BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr
132  ):
133      # Program ID
134      pid_m = tl.program_id(0)
135      pid_n = tl.program_id(1)
136  
137      # Block offsets
138      offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
139      offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
140      offs_k = tl.arange(0, BLOCK_K)
141  
142      # Pointers
143      a_ptrs = A + offs_m[:, None] * K + offs_k[None, :]
144      b_ptrs = B + offs_k[:, None] * N + offs_n[None, :]
145  
146      # Accumulator
147      acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
148  
149      # Main loop
150      for k in range(0, K, BLOCK_K):
151          a = tl.load(a_ptrs)
152          b = tl.load(b_ptrs)
153          acc += tl.dot(a, b)
154          a_ptrs += BLOCK_K
155          b_ptrs += BLOCK_K * N
156  
157      # Store result
158      c_ptrs = C + offs_m[:, None] * N + offs_n[None, :]
159      tl.store(c_ptrs, acc)
160  """,
161          "cuda": """
162  // Traditional CUDA GEMM (simplified)
163  __global__ void gemm_kernel(
164      const float* A, const float* B, float* C,
165      int M, int N, int K
166  ) {
167      // Shared memory for tiles
168      __shared__ float As[BLOCK_M][BLOCK_K];
169      __shared__ float Bs[BLOCK_K][BLOCK_N];
170  
171      // Thread indices
172      int tx = threadIdx.x, ty = threadIdx.y;
173      int bx = blockIdx.x, by = blockIdx.y;
174  
175      // Global indices
176      int row = bx * BLOCK_M + ty;
177      int col = by * BLOCK_N + tx;
178  
179      float acc = 0.0f;
180  
181      // Loop over K tiles
182      for (int k = 0; k < K; k += BLOCK_K) {
183          // Cooperative load to shared memory
184          if (row < M && k + tx < K)
185              As[ty][tx] = A[row * K + k + tx];
186          if (k + ty < K && col < N)
187              Bs[ty][tx] = B[(k + ty) * N + col];
188  
189          __syncthreads();
190  
191          // Compute partial product
192          for (int i = 0; i < BLOCK_K; i++)
193              acc += As[ty][i] * Bs[i][tx];
194  
195          __syncthreads();
196      }
197  
198      // Store result
199      if (row < M && col < N)
200          C[row * N + col] = acc;
201  }
202  """,
203      }
204  
205      # Pros and cons
206      pros_cons = {
207          "cutile": {
208              "pros": [
209                  "Highest level of abstraction",
210                  "Automatic optimization",
211                  "Minimal code required",
212                  "Built-in auto-tuning",
213                  "Future-proof (compiler improvements)",
214              ],
215              "cons": [
216                  "Experimental/early stage",
217                  "Limited control over optimization",
218                  "NVIDIA-only",
219                  "Limited debugging tools",
220                  "May not achieve peak performance",
221              ],
222          },
223          "triton": {
224              "pros": [
225                  "Good balance of abstraction and control",
226                  "Production-ready",
227                  "Multi-vendor support (NVIDIA + AMD)",
228                  "Active community",
229                  "Good performance",
230              ],
231              "cons": [
232                  "Still requires understanding of GPU architecture",
233                  "Manual configuration for best performance",
234                  "Less mature than CUDA",
235                  "Some operations not supported",
236              ],
237          },
238          "cuda": {
239              "pros": [
240                  "Maximum control and flexibility",
241                  "Highest possible performance",
242                  "Mature ecosystem",
243                  "Excellent debugging tools",
244                  "Comprehensive documentation",
245              ],
246              "cons": [
247                  "Steep learning curve",
248                  "Verbose code",
249                  "Manual optimization required",
250                  "NVIDIA-only",
251                  "Time-consuming development",
252              ],
253          },
254      }
255  
256      return {"feature_table": features, "code_examples": code_examples, "pros_cons": pros_cons}
257  
258  
259  def print_comparison_table(features: list[FeatureComparison]) -> None:
260      """Print a formatted comparison table."""
261      print("\n" + "=" * 100)
262      print("Feature Comparison: cuTile vs Triton vs CUDA")
263      print("=" * 100)
264  
265      # Header
266      print(f"{'Feature':<25} | {'cuTile':<20} | {'Triton':<20} | {'CUDA':<20}")
267      print("-" * 100)
268  
269      for f in features:
270          print(f"{f.feature:<25} | {f.cutile:<20} | {f.triton:<20} | {f.cuda:<20}")
271          if f.notes:
272              print(f"  Note: {f.notes}")
273  
274      print("=" * 100)
275  
276  
277  def print_code_examples(examples: dict[str, str]) -> None:
278      """Print code examples for each model."""
279      print("\n" + "=" * 80)
280      print("Code Examples: GEMM Implementation")
281      print("=" * 80)
282  
283      for model, code in examples.items():
284          print(f"\n--- {model.upper()} ---")
285          print(code)
286  
287  
288  def print_pros_cons(pros_cons: dict[str, dict[str, list[str]]]) -> None:
289      """Print pros and cons for each model."""
290      print("\n" + "=" * 80)
291      print("Pros and Cons Analysis")
292      print("=" * 80)
293  
294      for model, analysis in pros_cons.items():
295          print(f"\n{model.upper()}")
296          print("-" * 40)
297  
298          print("Pros:")
299          for pro in analysis["pros"]:
300              print(f"  ✓ {pro}")
301  
302          print("Cons:")
303          for con in analysis["cons"]:
304              print(f"  ✗ {con}")
305  
306  
307  def main() -> None:
308      """Run the comparison analysis."""
309      print("=" * 80)
310      print("GPU Programming Model Comparison")
311      print("cuTile vs Triton vs Traditional CUDA")
312      print("=" * 80)
313  
314      comparison = compare_programming_models()
315  
316      print_comparison_table(comparison["feature_table"])
317      print_code_examples(comparison["code_examples"])
318      print_pros_cons(comparison["pros_cons"])
319  
320      # Recommendations
321      print("\n" + "=" * 80)
322      print("Recommendations")
323      print("=" * 80)
324      print("""
325  When to use each:
326  
327  1. cuTile (Future)
328     - Rapid prototyping
329     - When automatic optimization is sufficient
330     - For teams without GPU expertise
331     - When code maintainability is priority
332  
333  2. Triton (Now)
334     - Production ML workloads
335     - When you need good performance with reasonable effort
336     - Cross-vendor deployment (NVIDIA + AMD)
337     - Custom operators for PyTorch/JAX
338  
339  3. Traditional CUDA (Always)
340     - Maximum performance requirements
341     - Complex memory access patterns
342     - When you need full control
343     - Debugging and profiling critical code
344     - Learning GPU architecture
345  """)
346  
347  
348  if __name__ == "__main__":
349      main()