{
  "context": "This evaluation assesses how well the engineer uses PyCUDA's argument handler system (In, Out, InOut) for automatic memory marshaling between host and device. The focus is on utilizing prepared calls with argument handlers to eliminate manual memory allocation and transfer code.",
  "type": "weighted_checklist",
  "checklist": [
    {
      "name": "Kernel compilation",
      "description": "Uses pycuda.compiler.SourceModule to compile a CUDA kernel that performs the fused multiply-add operation (result = a * b + c).",
      "max_score": 15
    },
    {
      "name": "Prepared call usage",
      "description": "Uses prepared_call() or prepared_async_call() method on the kernel function rather than direct kernel invocation. This is essential for using argument handlers.",
      "max_score": 25
    },
    {
      "name": "In handler usage",
      "description": "Uses pycuda.driver.In class (or InOut) to wrap input arrays (a, b, c) for automatic host-to-device transfer without manual memory allocation.",
      "max_score": 25
    },
    {
      "name": "Out handler usage",
      "description": "Uses pycuda.driver.Out class (or InOut) to wrap the output array for automatic device-to-host transfer without manual memory operations.",
      "max_score": 25
    },
    {
      "name": "Grid/block configuration",
      "description": "Properly configures block and grid dimensions for kernel execution based on array size.",
      "max_score": 10
    }
  ]
}