AI-SEPS

The 6th ACM SIGPLAN International Workshop on AI-Inspired and Empirical Methods for Software Engineering on Parallel Computing Systems (AI-SEPS) (Co-located with SPLASH 2019 as an ACM SIGPLAN-approved workshop)

The purpose of this workshop is to provide a stable forum for researchers and practitioners dealing with compelling challenges and issues of the software development life cycle on modern parallel platforms and HPC systems. The increased complexity of parallel applications on modern parallel platforms (e.g. multicore/manycore, distributed/hybrid systems) requires more insight into engineering of parallel software for targeting the underlying parallel systems. Rapidly emerging artificial intelligence-related technologies and machine learning, and their application to software engineering and parallel computing systems will be promising approaches to tackle these issues as well as approaches using traditional empirical and experimental methods. The workshop AI-Inspired and Empirical Methods for Software Engineering and Parallel Computing Systems (AI-SEPS) emphasizes on this trend for rapidly growing research interests on AI-inspired software engineering techniques for performance. We aim to advance the state of the art in all aspects of techniques on software engineering and parallel computing systems such as requirements engineering and software specification; design and implementation; program analysis; performance analysis, profiling and tuning; testing and debugging.

Accepted Papers

Call for Papers

The 6th ACM SIGPLAN International Workshop on AI-Inspired and Empirical Methods for Software Engineering on Parallel Computing Systems (AI-SEPS) (Co-located with SPLASH 2019 as an ACM SIGPLAN-approved workshop)

20-25 October 2019 Athens, Greece https://conf.researchr.org/home/seps-2019

General Scope:

The purpose of this workshop is to provide a stable forum for researchers and practitioners dealing with compelling challenges and issues of the software development life cycle on modern parallel platforms and HPC systems. The increased complexity of parallel applications on modern parallel platforms (e.g. multicore/manycore, distributed/hybrid systems) requires more insight into engineering of parallel software for targeting the underlying parallel systems. Rapidly emerging artificial intelligence-related technologies and machine learning, and their application to software engineering and parallel computing systems will be promising approaches to tackle these issues as well as approaches using traditional empirical and experimental methods. The workshop "AI-Inspired and Empirical Methods for Software Engineering and Parallel Computing Systems (AI-SEPS) ” emphasizes on this trend for rapidly growing research interests on AI-inspired software engineering techniques for performance. We aim to advance the state of the art in all aspects of techniques on software engineering and parallel computing systems such as requirements engineering and software specification; design and implementation; program analysis; performance analysis, profiling and tuning; testing and debugging.

Specific topics of interest include, but are not limited to:

• AI and machine learning for parallel programming and high-performance computing
• Software analytics for parallel programs
• Tools and environments for all aspects of engineering parallel software and their enhancement through AI-related technologies and machine learning
• High-performance deep learning
• Design of parallel programs and parallel design patterns
• Software development process and requirement engineering of parallel software
• Parallel software architectures
• Performance modeling techniques on parallel systems
• Profiling and event trace analysis
• Refactoring and reengineering
• Performance analysis and auto-tuning
• Machine learning for performance analysis and auto-tuning
• Energy-efficient parallel computing
• Testing and debugging of parallel applications
• Case studies and experience reports

The workshop welcomes the following types of submissions:

• “Work in progress” - abstract submissions (max. 800 words)
  • Position papers (max. 2 pages) and short papers (max. 4 pages) including:
    • Industrial and practical experiences
    • Tool presentations/demonstration
    • Early results & novel ideas without a comprehensive/extensive evaluation
    • Preliminary and exploratory work with unconventional approaches or wild and crazy ideas
  • Original, unpublished regular papers on current research (max. 10 pages)

The format of the workshop will be a full-day, SIGPLAN-approved workshop. Especially, we encourage work in progress or early-stage works as abstract submissions, which could be accepted for the presentation at the workshop without including in the proceedings publication. A concise and factual abstract is required. The abstract should state briefly the purpose of the research, the major results and conclusions (max. 800 words). Also presentation of the position papers on ongoing research are central in AI-SEPS 2019, and could be accepted for the formal proceeding publications in the ACM Digital Library by the peer-review process (in addition to short papers and long papers).

Submission: Papers and abstracts submitted to AI-SEPS 2019 must not have been published or simultaneously submitted anywhere else. Contributions should be submitted electronically in PDF format.

Submissions should use the ACM SIGPLAN Conference Format. You can find template for LaTeX or Word on ACM SIGPLAN website.

All of paper submissions must be done using the submission site: https://ai-seps19.hotcrp.com/

Publication: All accepted papers will be published in as formal proceedings in the ACM Digital Library.

Registration: Authors of accepted papers are expected to register and present their papers at the Workshop.

Contact SEPS 2019 Organizing Committee seps2019@googlegroups.com with any questions or concerns.