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It is shown that a small, reusable set of optimization patterns---loop flattening, $128$-bit vectorized loads, splitting fused kernels, and profile-guided launch configuration---recovers CUDA-class performance without changing the public API, and argued that these patterns constitute a practical recipe for performance-portable, vendor-agnostic HEP analysis kernels.