operation reordering

**Operation reordering** is the **scheduling transformation that changes execution order of independent operations to improve performance** - reordering can reduce critical-path length, improve memory locality, and lower peak resource pressure. **What Is Operation reordering?** - **Definition**: Compiler or runtime rearrangement of semantically independent operations. - **Goals**: Increase parallelism, reduce stalls, and minimize temporary tensor lifetime overlap. - **Constraints**: Only legal when data dependencies and side effects are preserved. - **Effect**: Can improve throughput and memory behavior without altering model outputs. **Why Operation reordering Matters** - **Critical Path Reduction**: Prioritizing unlock-heavy operations can shorten overall step time. - **Memory Peak Control**: Smart ordering avoids simultaneous allocation of large intermediates. - **Parallelism Exposure**: Independent ops can be moved to increase overlap opportunities. - **Backend Efficiency**: Reordered graphs may map better to hardware scheduling behavior. - **Compiler Leverage**: Creates opportunities for further fusion and elimination passes. **How It Is Used in Practice** - **Dependency Graphing**: Build precise data dependency graph before applying reorder transformations. - **Heuristic Selection**: Choose objective such as latency minimization or memory-peak minimization. - **Validation**: Run numerical checks and benchmark to confirm expected improvement. Operation reordering is **a high-impact graph scheduling optimization** - legal dependency-aware rearrangement can materially improve runtime and memory efficiency.