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OOPSLA2026顶会

Efficient Extraction for Effectful E-graphs

Oliver Flatt, Anjali Pal, Yihong Zhang, Ryan Tjoa, Kirsten Graham, Alex Fischman, Chandrakana Nandi, Eli Rosenthal, Zachary Tatlock, Haobin Ni

2026年份
1被引次数

摘要

E-graphs have enabled recent advances in program optimization, synthesis, and verification, yet remain difficult to apply to effectful programs whose memory and I/O operations must respect execution order. Existing effect-aware extraction algorithms rely on integer linear programming (ILP) and dominate total runtime.

We introduce statewalk DP, a new extraction algorithm that enforces effect ordering efficiently without external solvers. We prove that finding any effect-safe extraction is NP-complete, but show that statewalk DP is tractable in statewalk width, a parameter that measures the complexity of dataflow interactions among effects. In practice, statewalk width generally remains small, enabling statewalk DP to achieve order-of-magnitude speedups over ILP extraction while producing programs comparable to LLVM across our benchmarks. We implement the algorithm in eggcc, a prototype e-graph-based compiler for imperative Bril programs, and demonstrate that effect-aware extraction is no longer a bottleneck. CCS Concepts: • Software and its engineering → Compilers; Automated static analysis.

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