{
  "context": "This criteria evaluates how well the engineer uses PyCUDA's module loading and function attribute querying capabilities to implement a kernel analyzer. The focus is on proper usage of PyCUDA's driver API for retrieving kernel functions and querying their attributes.",
  "type": "weighted_checklist",
  "checklist": [
    {
      "name": "Module loading",
      "description": "Uses pycuda.driver.module_from_file() or equivalent method to load the compiled CUDA module from the file path",
      "max_score": 20
    },
    {
      "name": "Function retrieval",
      "description": "Uses the Module.get_function() method to retrieve kernel functions by name from the loaded module",
      "max_score": 20
    },
    {
      "name": "Register count query",
      "description": "Uses Function.num_regs or equivalent attribute/method to query the register count of a kernel function",
      "max_score": 15
    },
    {
      "name": "Shared memory query",
      "description": "Uses Function.shared_size_bytes or equivalent attribute/method to query the shared memory usage of a kernel function",
      "max_score": 15
    },
    {
      "name": "Max threads query",
      "description": "Uses Function.max_threads_per_block or equivalent attribute/method to query the maximum thread block size for a kernel function",
      "max_score": 15
    },
    {
      "name": "CUDA context initialization",
      "description": "Properly initializes CUDA by calling pycuda.driver.init() and creates/manages a CUDA context (e.g., using pycuda.autoinit or manual context creation)",
      "max_score": 10
    },
    {
      "name": "Error handling",
      "description": "Properly handles PyCUDA exceptions (such as pycuda._driver.Error or LogicError) for missing files and invalid function names",
      "max_score": 5
    }
  ]
}