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.