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.