PyHPC 2014 - 4th Workshop on Python for High Performance and Scientific Computing

Python is an established, general-purpose, high-level programming language with a large following in research and industry for applications in fields including computational fluid dynamics, finance, biomolecular simulation, artificial intelligence, statistics, data analysis, scientific visualization, and systems management. The use of Python in scientific, high performance parallel, big data, and distributed computing roles has been on the rise with the community providing new and innovative solutions while preserving Python’s famously clean syntax, low learning curve, portability, and ease of use.
The workshop will bring together researchers and practitioners from industry, academia, and the wider community 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 (such as language design and performance issues), and to share experience using Python in scientific computing education.
The special focus of this workshop will be on interactive parallel computing with IPython. The interactive shell IPython with its browser-based notebook interface provides an easy-to-use solution to develop, execute, debug, and monitor parallel Python applications. It is a convenient technology to document and distribute the application, too. Since, the use of IPython is rising rapidly in science and education, we encourage you to submit papers describing IPython-based solution for parallel computing, documentation, or education.
Call for Papers
Please submit papers related to Python usage in any of the following topics and application areas as well as on broader topics in business, science, technology, engineering, education, mathematics or multidisciplinary topics.
Interactive parallel computing
High performance computing applications with Python
Performance analysis, profiling, and debugging of Python code
System administration
Integration with other programming languages
Scientific visualization
Problem solving environments and frameworks
Education in scientific computing
Big data and data analytics