code: def static_persistent_warp_specialized_matmul_kernel(  #
      a_ptr,
      b_ptr,
      c_ptr,  #
      M,
      N,
      K,  #
      stride_am,
      stride_ak,  #
      stride_bk,
      stride_bn,  #
      stride_cm,
      stride_cn,  #
      BLOCK_M: tl.constexpr,
      BLOCK_N: tl.constexpr,
      BLOCK_K: tl.constexpr,  #
      NUM_SMS: tl.constexpr,  #
  ):
      start_tile = tl.program_id(axis=0)
      m_tiles = tl.cdiv(M, BLOCK_M)
      n_tiles = tl.cdiv(N, BLOCK_N)
      num_tiles = m_tiles * n_tiles
      offs_k = tl.arange(0, BLOCK_K)
  
      for tile_id in range(start_tile, num_tiles, NUM_SMS):
          pid_m = tile_id // n_tiles
          pid_n = tile_id % n_tiles
          accumulator = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
          offs_am = (pid_m * BLOCK_M + tl.arange(0, BLOCK_M)) % M
          offs_bn = (pid_n * BLOCK_N + tl.arange(0, BLOCK_N)) % N
          a_ptrs = a_ptr + (
              offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak
          )
          b_ptrs = b_ptr + (
              offs_k[:, None] * stride_bk + offs_bn[None, :] * stride_bn
          )
  
          for k in range(0, K, BLOCK_K):
              a = tl.load(a_ptrs)
              b = tl.load(b_ptrs)
              accumulator += tl.dot(a, b)
              a_ptrs += BLOCK_K * stride_ak
              b_ptrs += BLOCK_K * stride_bk
  
          offs_cm = tl.arange(0, BLOCK_M) + pid_m * BLOCK_M
          offs_cn = tl.arange(0, BLOCK_N) + pid_n * BLOCK_N
  
          c_ptrs = (
              c_ptr
              + offs_cm[:, None] * stride_cm
              + offs_cn[None, :] * stride_cn
          )
          tl.store(c_ptrs, accumulator)
  
imports: import itertools
  import pytest
  import torch
  from torch.testing import assert_close
  import triton
  import triton.language
  from triton.runtime import driver
label_desc: Use image node_id 8 for calling a global function with example usage: static_persistent_warp_specialized_matmul_kernel(a_ptr, b_ptr, c_ptr, M, N, K, stride_am, stride_ak, stride_bk, stride_bn, stride_cm, stride_cn, BLOCK_M, BLOCK_N, BLOCK_K, NUM_SMS) without return types