CompilerAutovectorisierung

CompilerAutove, short for Compiler Auto-Vectorization Engine, is a theoretical framework proposed to automate vectorization and related optimizations within modern compilers. It envisions a cohesive set of analysis and transformation passes that can convert scalar code into SIMD-enabled code while preserving program semantics. Although not tied to a concrete implementation, the concept informs discussions of how compilers might better exploit data parallelism across architectures.

The core idea combines aggressive loop vectorization with enhanced alias analysis, polyhedral modeling, and runtime checks to preserve correctness in the presence of complex memory access patterns. It supports common SIMD targets such as x86 AVX, ARM NEON, and RISC-V vector extensions, and it is designed to work with front ends for C, C++, and Fortran. Key techniques include loop unswitching, dependency analysis improvements, reduction detection, and selective use of gather-scatter patterns. Some proposals also explore machine-learning assisted decision models to predict vectorizable regions and choose vector widths.

Architecturally, CompilerAutove is imagined as a backend framework that plugs into existing compiler infrastructures (for example LLVM or GCC-like IRs). It would provide both declarative annotations for portability and pass pipelines that can be swapped or extended. Runtime guards and fallback paths would be used to maintain correctness on uncertain analyses.

Status and reception: as a hypothetical construct, CompilerAutove has influenced discussions on automatic vectorization, emphasizing the trade-offs between compile-time cost, portability, and performance. Real-world adoption depends on advances in alias analysis, cost modeling, and maintainable backend integrations. Some researchers view it as a useful north star for future compiler design rather than a concrete project.