The high–level programming language Python is well established with a<br>large community in academia and industry. It is a general–purpose<br>language adopted by many scientific applications. Examples are<br>computational fluid dynamics, bio molecular simulation, machine<br>learning, finance, or scientific visualization. Scientists, engineers,<br>and educators use Python for data science, high–performance computing,<br>and distributed computing. Traditionally, system administrators use<br>Python for system management and automating administration tasks.<br>Python is extremely easy to learn due to its very clean syntax and<br>great readability. Therefore developers love Python as it facilitates<br>writing sustainable and maintainable software systems. For the same<br>reasons, Python is well suited for education at all levels.<br>The workshop will bring together researchers and practitioners using<br>Python in all aspects of high performance and scientific computing.<br>The goal is to present Python applications from mathematics, science,<br>and engineering, to discuss general topics regarding the use of<br>Python, and to share experiences using Python in scientific computing<br>education.<br>The overarching theme of the workshop is productivity vs. performance<br>in HPC and scientific programming. While Python is extremely strong in<br>supporting human productivity as well reproducible science, it still<br>lacks in computational performance compared to ‘traditional’ HPC<br>languages such as Fortran or C. For the workshop, we encourage authors<br>to submit novel research in improving performance of Python<br>applications as well as research on productivity of development with<br>Python.<br>CALL FOR PAPERS<br>Please submit papers related to Python usage in any of the following<br>topics and application areas as well as on broader topics in business,<br>science, technology, engineering, or education:<br>* Big Data and Data Science with Python<br>* Hybrid programming and integration with other programming languages<br>* Comparison of Python with other dynamic languages for HPC<br>* Python for multi–core processors and quantum computers<br>* Interactive HPC applications using Jupyter<br>* High performance computing applications with Python<br>* Performance analysis, profiling, and debugging of Python code<br>* Administration of large HPC systems with Python<br>* Scientific and interactive visualization with Python<br>* Problem solving environments and frameworks<br>* Diversity and education in HPC and scientific computing<br>
Abbrevation
PyHPC
City
Denver
Country
United States
Deadline Paper
Start Date
End Date
Abstract