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Call for Papers - 7th Workshop on Python for High-Performance and Scientific Computing

Introduction

The high-level programming language Python is well established with a large community in academia and industry. It is a general-purpose language adopted by many scientific applications. Examples are computational fluid dynamics, bio molecular simulation, machine learning, finance, or scientific visualization. Scientists, engineers, and educators use Python for data science, high-performance computing, and distributed computing. Traditionally, system administrators use Python for system management and automating administration tasks. Python is extremely easy to learn due to its very clean syntax and great readability. Therefore developers love Python as it facilitates writing sustainable and maintainable software systems. For the same reasons, Python is well suited for education at all levels.

The workshop will bring together researchers and practitioners using Python in all aspects of high performance and scientific computing. The goal is to present Python applications from mathematics, science, and engineering, to discuss general topics regarding the use of Python, and to share experiences using Python in scientific computing education.

Call for Papers

The overarching theme of the workshop is productivity vs. performance in HPC and scientific programming. While Python is extremely strong in supporting human productivity as well reproducible science, it still lacks in computational performance compared to ‘traditional’ HPC languages such as Fortran or C. For the workshop, we encourage authors to submit novel research in improving performance of Python applications as well as research on productivity of development with Python.

• Big Data and Data Science with Python
• Hybrid programming and integration with other programming languages
• Comparison of Python with other dynamic languages for HPC
• Python for multi-core processors and quantum computers
• Interactive HPC applications using Jupyter
• High performance computing applications with Python
• Performance analysis, profiling, and debugging of Python code
• Administration of large HPC systems with Python
• Scientific and interactive visualization with Python
• Problem solving environments and frameworks
• Diversity and education in HPC and scientific computing

Submissions

We invite you to submit a paper of up to 10 pages (including references) via the submission site (https://easychair.org/conferences/?conf=pyhpc2017). All papers will be published in cooperation with SIGHPC through ACM Digital Library and IEEE Xplore.

The formatting instructions are available here: http://www.ieee.org/conferences_events/conferences/publishing/templates.html

You can also use the template online on Overleaf.

Important Dates

Full paper submission: September 1, 2017
Notification of acceptance: September 18, 2017
Camera-ready papers: October 9, 2017
Workshop: November 12, 2017 (concurrent with SC17)

Program Committee

• Achim Basermann, German Aerospace Center, Germany
• Yung-Yu Chen, Synopsys, Inc., Taiwan
• Cyrus Harrison, Lawrence Livermore National Laboratory, USA
• Konrad Hinsen, Centre de Biophysique Moléculaire, CNRS Orléans, France
• Michael Klemm, Intel, Inc., Germany
• Guy K. Kloss, Qrious Ltd., New Zealand
• Maurice Ling, Nanyang Technological University, Singapore
• Sergey Maidanov, Intel, Inc., USA
• Karen Ng, Intel, Inc., USA
• Shilpika, Argonne National Laboratory, USA
• Mike Müller, Python Academy, Germany
• Massimo Di Pierro, DePaul University, USA
• Matthew Turk, Columbia University, USA
• Jake VanderPlas, University of Washington, USA

Workshop Co-Chairs

• Andreas Schreiber, German Aerospace Center (DLR), Germany
• William Scullin, Argonne National Laboratory, USA
• Bill Spotz, Sandia National Laboratories, USA
• Rollin Thomas, Lawrence Berkeley National Laboratory, USA