Aims and Scope
While scientific software is an important component in the pursuit of scientific discovery, software development in high-performance computing (HPC) continues to be challenging. The software development process today combines contributions from domain scientists, applied mathematicians, computer scientists, and involves complex programming models. As a result of these diverse contributions software environments have become significantly complicated and expensive.
With this increasing diversity, the complexity of software development increases and it requires a steep learning curve for new developers, resulting in a slower pace of software development. With the continuous integration of scientific applications in complex, deep software stacks (workflows, compilers, runtime libraries, heterogeneous systems) novel techniques and practical tools for assisting the software development in HPC are invaluable. Recent advances in generative AI and large language models, such as GitHub’s Copilot, OpenAI’s GPT, Meta’s Llama, among others, demonstrate already that they can perform important tasks in the HPC and scientific software development, such as verification & validation, generation of optimized code, code translation, porting of applications, etc.
The goal of the AI Assisted Software Development for High-performance Computing (HPC) workshop (AI4Dev) is to create a forum composed of researchers, scientists, application developers, computing centers, and industry staff to discuss ideas on how artificial intelligence can help in the whole process of HPC software development. The workshop will feature contributed papers and invited talks in the area.
Call for Papers
The workshop invites submissions of original research papers. Papers should be no longer than 8 pages (including references) and must be formatted according to the IEEE 2-column conference style.
Papers should be submitted in PDF format via the ICPP Linklings submission system.
We expect papers in the following areas (but not limited to):
- AI and/or Machine Learning (AI/ML) techniques to improve programming productivity
- Performance analysis driven by AI and ML
- Debugging and testing driven by AI/ML
- AI/ML-assisted compiler optimizations and code generation
- Auto-tuning and performance portability using AI/ML
- Code synthesis and generation using automated AI/ML techniques
- AI-assisted code recommendations for code maintainability, performance and correctness
- IDE extensions using ML for improved programming productivity
- AI-assisted software building and deployment
- Mining best programming practices using ML
- Addressing security, privacy, and licensing concerns using AI/ML for software development
Important Dates
- Paper Submission Deadline: June 17, 2025
- Notification of Acceptance: July 08, 2025
- Camera-Ready Deadline: July 22, 2025
- Workshop Date: September 8, 2025
Agenda
TBD
Organizers
Chairs:
- William F Godoy Oak Ridge National Laboratory, USA
- Ignacio Laguna Lawrence Livermore National Laboratory, USA
Committee:
- Harshitha Menon, Lawrence Livermore National Laboratory, USA
- Pedro Valero-Lara, Oak Ridge National Laboratory, USA
- Chris Cummins, Meta, USA
- Pavlos Petoumenos, University of Manchester, UK
- Boyana Norris, University of Oregon, USA
- Riyadh Baghdadi, Massachusetts Institute of Technology, USA
- William Moses, University of Illinois Urbana-Champaign, USA
- Jonathan Ragan-Kelley, Massachusetts Institute of Technology, USA
- Gottschlich, J, Merly AI, USA
- Dong Li, University of California, Merced, USA
- Tarindu Jayatilaka, Princeton University, USA
- Xipeng Shen, North Carolina State University, USA
- Hugh Leather, Meta, USA
- Keren Zhou, OpenAI, USA
- Hui Guan, University of Massachusetts Amherst, USA
- Daya Guo, Sun Yat-Sen University, China
- Nikhil Jain, NVIDIA, USA
- Miltiadis Alamanis, Microsoft, USA
- Charles Sutton, University of Edinburgh, UK
- Dario Garcia Casulla, Barcelona Supercomputing Center, Spain
- Hiroyuki Takizawa, Tohoku University, Japan
- Gokcen Kestor, Barcelona Supercomputing Center, Spain
- Olivier Aumage, INRIA, France
- Diego Andrade Canosa, University of A Coruna, Spain
- Jens Domke, RIKEN Center for Computational Science [R-CSS], Japan