Evaluating and Improving Framework-based Parallel Code Completion with Large Language Models
This program is tentative and subject to change.
Modern computing architectures (e.g., multi-core CPUs, GPUs, distributed systems) rely on parallel code implemented via frameworks such as OpenMP, MPI, and CUDA. While large language models (LLMs) have shown strong performance in general code generation, they struggle with the structured reasoning required for parallel programming, such as handling concurrency, synchronization, and framework-specific semantics. In practical parallel code development, a common workflow begins with sequential code and incrementally introduces parallel directive codes. We formalize this process as the task of \textbf{framework-based parallel code completion} (FPCC), which involves three subtasks: identifying insertion points, selecting parallel frameworks, and completing parallel directive codes.
To support this task, we construct a high-quality dataset of 16,615 framework-based parallel code pairs across six widely used frameworks, labeled with directive points, parallel frameworks, and the code of parallel directives. Empirical results show that six popular LLMs perform poorly on FPCC, particularly struggling with identifying insertion points and completing correct directive codes.
To address these limitations, we propose HPCL, a curriculum-based fine-tuning framework that progressively improves model capabilities in insertion point identification, parallel framework selection, and parallel directive code completion. Our approach achieves substantial improvements, yielding an 17.82% increase in EM and a 5.43% improvement in DIR scores over LLM-based baselines. Finally, expert-guided error analysis reveals common failure patterns and suggests future directions in retrieval-augmented completion and consistency-aware training.
This program is tentative and subject to change.
Tue 18 NovDisplayed time zone: Seoul change
11:00 - 12:30 | |||
11:00 10mTalk | Coverage-Based Harmfulness Testing for LLM Code Transformation Research Papers Honghao Tan Concordia University, Haibo Wang Concordia University, Diany Pressato Concordia University, Yisen Xu Software PErformance, Analysis, and Reliability (SPEAR) lab, Concordia University, Montreal, Canada, Shin Hwei Tan Concordia University | ||
11:10 10mTalk | RealisticCodeBench: Towards More Realistic Evaluation of Large Language Models for Code Generation Research Papers Xiao Yu Zhejiang University, Haoxuan Chen Wuhan University of Technology, Lei Liu Xi’an Jiaotong University, Xing Hu Zhejiang University, Jacky Keung City University of Hong Kong, Xin Xia Zhejiang University | ||
11:20 10mTalk | Code-DiTing: Automatic Evaluation of Code Generation without References or Test Cases Research Papers Guang Yang , Yu Zhou Nanjing University of Aeronautics and Astronautics, Xiang Chen Nantong University, Wei Zheng Northwestern Polytechnical University, Xing Hu Zhejiang University, Xin Zhou Singapore Management University, Singapore, David Lo Singapore Management University, Taolue Chen Birkbeck, University of London Pre-print | ||
11:30 10mTalk | An Agent-based Evaluation Framework for Complex Code Generation Research Papers Xinchen Wang Harbin Institute of Technology, Pengfei Gao ByteDance, Chao Peng ByteDance, Ruida Hu Harbin Institute of Technology, Shenzhen, Cuiyun Gao Harbin Institute of Technology, Shenzhen | ||
11:40 10mTalk | PseudoFix: Refactoring Distorted Structures in Decompiled C Pseudocode Research Papers Gangyang Li University of Science and Technology of China, Xiuwei Shang University of Science and Technology of China, Shaoyin Cheng University of Science and Technology of China, junqi zhang University of Science and Technology of China, Li Hu , Xu Zhu University of Science and Technology of China, Weiming Zhang University of Science and Technology of China, Nenghai Yu School of Cyber Security, University of Science and Technology of China | ||
11:50 10mTalk | Evaluating and Improving Framework-based Parallel Code Completion with Large Language Models Research Papers Ke Liu , Qinglin Wang Shandong Normal University, Xiang Chen Nantong University, Guang Yang , YiGui Feng National University of Defense Technology, Gencheng Liu National University of Defense Technology, Jie Liu Institute of Software, Chinese Academy of Sciences | ||
12:00 10mTalk | Variational Prefix Tuning for diverse and accurate code summarization using pre-trained language models Journal-First Track Junda Zhao Department of Mechanical and Industrial Engineering, University of Toronto, Yuliang Song Department of Mechanical and Industrial Engineering, University of Toronto, Eldan Cohen Department of Mechanical and Industrial Engineering, University of Toronto | ||
12:10 10mTalk | Effective Code Membership Inference for Code Completion Models via Adversarial Prompts Research Papers Yuan Jiang Harbin Institute of Technology, Zehao Li Harbin Institute of Technology, Shan Huang East China Normal University, Christoph Treude Singapore Management University, Xiaohong Su Harbin Institute of Technology, Tiantian Wang Harbin Institute of Technology | ||
12:20 10mTalk | LongCodeZip: Compress Long Context for Code Language Models Research Papers Yuling Shi Shanghai Jiao Tong University, Yichun Qian Stanford University, Hongyu Zhang Chongqing University, Beijun Shen Shanghai Jiao Tong University, Xiaodong Gu Shanghai Jiao Tong University Pre-print Media Attached | ||