Eliminate Branches by Melding IR Instructions

2025-12-26

AI Summary

Branch misprediction incurs substantial performance penalties on data-dependent branches, where existing hardware predictors and profile-guided approaches deliver limited gains. This paper proposes an LLVM IR-level compiler optimization that pioneers the application of sequence alignment to branch elimination: it merges semantically similar control-flow paths at the instruction-path level while enforcing semantic correctness via operand-level safety guards—without requiring hardware predicate support. Unlike conventional if-conversion, our approach avoids x86’s restrictions on memory instructions and eliminates high speculative overhead by combining IR-level path alignment, static analysis, and lightweight runtime checks. Evaluated on 102 benchmarks, it achieves a 10.9% geometric mean speedup, with peak improvements up to 32×, and introduces significantly lower static instruction overhead than baseline methods.

Technology Category

• Search and Optimization: Heuristic Search
• Machine Learning: Hardware-aware ML
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Branch mispredictions cause catastrophic performance penalties in modern processors, leading to performance loss. While hardware predictors and profile-guided techniques exist, data-dependent branches with irregular patterns remain challenging. Traditional if-conversion eliminates branches via software predication but faces limitations on architectures like x86. It often fails on paths containing memory instructions or incurs excessive instruction overhead by fully speculating large branch bodies. This paper presents Melding IR Instructions (MERIT), a compiler transformation that eliminates branches by aligning and melding similar operations from divergent paths at the IR instruction level. By observing that divergent paths often perform structurally similar operations with different operands, MERIT adapts sequence alignment to discover merging opportunities and employs safe operand-level guarding to ensure semantic correctness without hardware predication. Implemented as an LLVM pass and evaluated on 102 programs from four benchmark suites, MERIT achieves a geometric mean speedup of 10.9% with peak improvements of 32x compared to hardware branch predictor, demonstrating the effectiveness with reduced static instruction overhead.

Problem

• Eliminates data-dependent branches with irregular patterns
• Reduces performance loss from branch mispredictions in processors
• Avoids limitations of traditional if-conversion on x86 architectures

Innovation

• Melds similar operations from divergent paths at IR level
• Uses sequence alignment to discover branch merging opportunities
• Employs operand-level guarding for correctness without hardware predication