Fused operations is the optimization strategy of combining multiple computational steps into a single kernel execution - it cuts launch overhead and avoids materializing intermediate tensors in slow global memory.

What Is Fused operations?

• Definition: Kernel-level or compiler-level merging of consecutive ops such as add, multiply, norm, and activation.
• Primary Effect: Keeps intermediate values in registers or shared memory instead of round-tripping to HBM.
• Typical Patterns: Bias plus activation, residual plus norm, and matmul epilogues with scaling.
• Execution Layer: Implemented via hand-written kernels, compiler passes, or runtime graph optimizers.

Why Fused operations Matters

• Lower Latency: Fewer kernel launches reduce scheduler and synchronization overhead.
• Higher Throughput: Reduced memory traffic improves arithmetic efficiency on bandwidth-bound stages.
• Energy Efficiency: Less redundant data movement lowers per-step power and cost.
• Scalability: Fusion benefits accumulate across repeated layers in deep transformer stacks.
• Production Value: Inference pipelines gain measurable request-per-second improvements.

How It Is Used in Practice

• Hotspot Discovery: Identify chains of small ops that dominate runtime due to launch count.
• Fusion Selection: Merge safe sequences while preserving numerical behavior and gradient correctness.
• Regression Testing: Verify output parity and measure end-to-end latency before broad rollout.

Fused operations are a fundamental GPU performance technique for modern ML systems - minimizing intermediate memory movement is one of the highest-return optimization levers.