Call for Submissions: 9th Workshop on Python for High-Performance and Scientific Computing

Posted by j.laird on 16 June 2020 - 9:30am

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Call for paper and lightning talk submissions to the 9th Workshop on Python for High-Performance and Scientific Computing. 

PyHPC workshop at SC20

Python remains one of the fastest-growing programming languages with large communities of users in academia and industry. Its high-level syntax lowers the barrier to entry and improves productivity, making it the “go-to” language for data science, machine learning, whilst also remaining increasingly popular in high-performance and distributed computing.

PyHPC returns to Supercomputing to bring researchers, developers and Python practitioners to share their experiences using Python across a broad spectrum of disciplines and applications. The goal of the workshop is to provide a platform for the community to present novel Python applications from a wide range of disciplines, to enable topical discussions regarding the use of Python, and to share experiences using Python in scientific computing and education.

In bringing the community together, the workshop aims to help address the needs of the community and to help the community shape future directions in high-performance and scientific computing.

General Submission Guidance

Authors are encouraged to submit novel research on the broad use of Python in high-performance and scientific computing primarily, but also in data science, machine learning as well as broader topics in science, technology, engineering, education, mathematics or multidisciplinary topics.

Either a paper or lightning talk related to Python usage in any of the following topics and application areas, including but not limited to:

  • High-Performance Computing, Big Data, Machine learning, and Data Science with Python
  • Hybrid programming and integration with other programming languages
  • Python compared to other languages for HPC and Data Science
  • Python for emerging computing paradigms (e.g., quantum computing, neuromorphic computing, Probabilistic and stochastic computing
  • Interactivity and reproducibility in HPC using Python
  • Performance analysis, profiling, and debugging
  • Administration of large HPC systems
  • Scientific and interactive visualization
  • Problem solving environments and frameworks
  • Diversity, inclusivity and education in HPC and scientific computing

Full details can be found at: