fused operations

**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.