Design and implement a high-performance fused softmax + top-k kernel in CUDA (or CUDA-like pseudocode).

Requirements:
- Input: logits [B, T, V]
- Output:
    - top-k indices per (B, T)
    - top-k probabilities (after softmax)

Constraints:
1. Do NOT materialize the full softmax matrix in global memory.
2. Must be numerically stable (log-sum-exp).
3. Minimize global memory reads/writes.
4. Use shared memory where appropriate.
5. Handle large V (e.g., 50k+) efficiently.

Deliver:
- Kernel pseudocode or CUDA code
- Memory access pattern explanation
- Warp-level optimization strategy
- Complexity analysis (bandwidth vs compute bound)
- Comparison to naive implementation