Can Large Language Models Recover Semantic Optimization Opportunities That Compilers Miss?
A new study shows large language models can identify missed optimization opportunities in C/C++ code, introducing the SeGaBench benchmark with 120 test cases.
- LLMs can be used to recover hidden semantics that enable compiler optimizations.
- SeGaBench provides 120 curated cases for evaluating this capability.
- The approach produces contract‑preserving artifacts validated for correctness.
- Integrating LLMs with compilers could unlock performance gains missed by current tools.
Optimizing compilers often overlook profitable transformations because the necessary semantics are not present in the program's intermediate representation. The authors investigate whether large language models can infer these missing semantics from heterogeneous C/C++ contexts and generate validated, contract-preserving artifacts.
To assess this capability, they create SeGaBench, an executable benchmark comprising 100 synthetic and 20 real-world cases. Each case embeds hidden enabling semantics, provides an oracle artifact, and includes correctness and semantic validators.
The benchmark covers low‑level assumptions, data‑structure invariants, and high‑level semantic lifting, offering a comprehensive testbed for future research on LLM‑driven code optimization.
The work highlights a new direction where AI models assist traditional compiler pipelines, potentially improving performance and energy efficiency in software systems.
Offers a new method to enhance code performance beyond traditional compiler limits.
Potentially reduces runtime costs by enabling deeper optimizations.
Signals emerging market for AI‑augmented compilation tools.
Provides a benchmark for research projects on AI‑assisted code transformation.
Shows how AI can improve fundamental software engineering processes.
- semantic optimization
- Transformations that rely on program meaning rather than just syntax.
- contract-preserving artifact
- A generated code change that maintains the original program's functional contracts.
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