{
  "context": "This evaluation assesses the engineer's ability to use PyCUDA's pagelocked (pinned) host memory features for optimized GPU data transfers. The focus is on proper usage of pagelocked memory allocation functions and their integration with device memory transfer operations.",
  "type": "weighted_checklist",
  "checklist": [
    {
      "name": "Pagelocked Buffer Creation",
      "description": "Uses pycuda.driver.pagelocked_empty(), pycuda.driver.pagelocked_zeros(), or similar pagelocked allocation functions to create optimized host buffers instead of regular NumPy arrays",
      "max_score": 30
    },
    {
      "name": "Device Memory Allocation",
      "description": "Uses pycuda.driver.mem_alloc() or similar functions to allocate device memory for storing transferred data",
      "max_score": 15
    },
    {
      "name": "Host-to-Device Transfer",
      "description": "Uses pycuda.driver.memcpy_htod() or similar transfer functions to copy data from pagelocked host memory to device memory",
      "max_score": 20
    },
    {
      "name": "Device-to-Host Transfer",
      "description": "Uses pycuda.driver.memcpy_dtoh() or similar transfer functions to copy data from device memory back to pagelocked host memory",
      "max_score": 20
    },
    {
      "name": "Context Management",
      "description": "Properly initializes CUDA context using pycuda.autoinit or pycuda.driver.init() with device and context creation",
      "max_score": 10
    },
    {
      "name": "Timing Implementation",
      "description": "Uses appropriate timing mechanisms (pycuda.driver.Event for GPU timing or Python time functions for overall timing) to measure transfer performance",
      "max_score": 5
    }
  ]
}