{
  "context": "This evaluation assesses how well the engineer uses CuPy's CUDA graph capabilities to optimize a repeated matrix computation pipeline. The focus is on proper use of graph capture and replay APIs to reduce kernel launch overhead.",
  "type": "weighted_checklist",
  "checklist": [
    {
      "name": "Stream Creation",
      "description": "Creates a CUDA stream using cupy.cuda.Stream() to enable graph capture functionality",
      "max_score": 10
    },
    {
      "name": "Graph Capture",
      "description": "Uses stream.begin_capture() to start capturing operations into a CUDA graph",
      "max_score": 20
    },
    {
      "name": "Operation Recording",
      "description": "Records the complete sequence of operations (matrix multiply, addition, squaring) within the capture context to build the graph",
      "max_score": 15
    },
    {
      "name": "Graph Finalization",
      "description": "Calls stream.end_capture() to finalize the CUDA graph and obtain a graph object",
      "max_score": 20
    },
    {
      "name": "Graph Replay",
      "description": "Uses graph.launch() inside a loop to replay the captured graph for multiple iterations instead of re-executing operations",
      "max_score": 25
    },
    {
      "name": "Computation Correctness",
      "description": "Implements the correct sequence of operations (a @ b, + c, ** 2, update a) that produces mathematically correct results",
      "max_score": 10
    }
  ]
}