Vectorization is a powerful optimization technique that significantly boosts the performance of high performance computing applications operating on large data arrays. Despite decades of research on auto-vectorization, compilers frequently miss opportunities to vectorize code. On the other hand, writing vectorized code manually using compiler intrinsics is still a complex, error-prone task that demands deep knowledge of specific architecture and compilers. In this paper, we evaluate the potential of large-language models (LLMs) to generate vectorized (Single Instruction Multiple Data) code from scalar programs that process individual array elements. We propose a novel finite-state-machine multi-agents based approach that harnesses LLMs and test-based feedback to generate vectorized code. Our findings indicate that LLMs are capable of producing high-performance vectorized code with run-time speedup ranging from 1.1x to 9.4x as compared to the state-of-the-art compilers such as Intel Compiler, GCC, and Clang. To verify the correctness of vectorized code, we use Alive2, a leading bounded translation validation tool for LLVM IR. We describe a few domain-specific techniques to improve the scalability of Alive2 on our benchmark dataset. Overall, our approach is able to verify 38.2% of vectorizations as correct on the TSVC benchmark dataset.

CGO 2021 included two separate categories of papers—main conference papers and tool & practical experience papers—as it did in the previous year. However, this year the acceptance criterion of tool papers required the validation of the tool from the Artifact Evaluation Committee.

Static analyses compute properties of programs that are true in all executions, and compilers use these properties to justify optimizations such as dead code elimination. Each static analysis in a compiler should be as precise as possible while remaining sound and being sufficiently fast. Unsound static analyses typically lead to miscompilations, whereas imprecisions typically lead to missed optimizations. Neither kind of bug is easy to track down.