ParEVO: Synthesizing Code for Irregular Data: High-Performance Parallelism through Agentic Evolution

Parallelizing code for irregular data structures (sparse graphs, unbalanced trees, non-uniform meshes) is notoriously hard, and current LLMs fail catastrophically on such tasks, generating code riddled with race conditions, deadlocks, and poor scaling. We address this with ParEVO, a framework for synthesizing high-performance parallel algorithms for irregular data, built on three contributions: the Parlay-Instruct Corpus of 13,820 tasks generated via a Critic-Refine" pipeline that filters for empirically performant uses of Work-Span primitives; specialized DeepSeek, Qwen, and Gemini models fine-tuned to the semantics of the ParlayLib library; and an Evolutionary Coding Agent (ECA) that repairs the last mile" of correctness using compiler and profiler feedback. On the ParEval benchmark, ParEVO achieves an average 107× speedup and a 13.6× speedup on highly complex irregular graph problems, outperforming commercial models like GPT-5-Thinking and Gemini-3-Pro, while matching expert human-written baselines and reaching up to a 4.1× speedup on kernels such as Maximal Independent Set. This demonstrates that AI-driven agents can effectively navigate the complex landscape of high-performance computing. Source code and datasets are available at https://github.com/WildAlg/ ParEVO (ParEVO, 2026a).