[2603.02510] ParEVO: Synthesizing Code for Irregular Data: High-Performance Parallelism through Agentic Evolution

Computer Science > Machine Learning arXiv:2603.02510 (cs) [Submitted on 3 Mar 2026] Title:ParEVO: Synthesizing Code for Irregular Data: High-Performance Parallelism through Agentic Evolution Authors:Liu Yang, Zeyu Nie, Andrew Liu, Felix Zou, Deniz Altinbüken, Amir Yazdanbakhsh, Quanquan C. Liu View a PDF of the paper titled ParEVO: Synthesizing Code for Irregular Data: High-Performance Parallelism through Agentic Evolution, by Liu Yang and 6 other authors View PDF Abstract:The transition from sequential to parallel computing is essential for modern high-performance applications but is hindered by the steep learning curve of concurrent programming. This challenge is magnified for irregular data structures (such as sparse graphs, unbalanced trees, and non-uniform meshes) where static scheduling fails and data dependencies are unpredictable. Current Large Language Models (LLMs) often fail catastrophically on these tasks, generating code plagued by subtle race conditions, deadlocks, and sub-optimal scaling. We bridge this gap with ParEVO, a framework designed to synthesize high-performance parallel algorithms for irregular data. Our contributions include: (1) The Parlay-Instruct Corpus, a curated dataset of 13,820 tasks synthesized via a "Critic-Refine" pipeline that explicitly filters for empirically performant algorithms that effectively utilize Work-Span parallel primitives; (2) specialized DeepSeek, Qwen, and Gemini models fine-tuned to align probabilistic generation with ...