| |
 |
| Paper: |
Python Code Parallelization, Challenges and Alternatives |
| Volume: |
521, Astronomical Data Analysis Software and Systems XXVI |
| Page: |
515 |
| Authors: |
Gonzalez, J.; Taylor, J.; Castro, S.; Kern, J.; Knudstrup, J.; Zampieri, S.; Manning, A.; Bhatnagar, S.; Davis, L.; Golap, K.; Jacobs, J.; Nakazato, T.; Petry, D.; Pokorny, M.; Rao, U.; Robnett, J.; Schiebel, D.; Sugimoto, K.; Tsutsumi, T.; Wells, A.; Williams, S. J. |
| Abstract: |
In the last few years the development of Python code for science and data reduction purposes has gained significant popularity. ESO itself uses a Python-based archiving system for VLT and ALMA data. Also the data reduction suite for ALMA data is python-based. Rapid development is fostered by a big community and a wide range of already available packages. However Python enforces locking mechanisms, to ensure thread safety, that effectively reduce the capacity of Python to use only one core. In this context a number of alternatives have been developed by the community to emulate actual multi-threading and make parallel processing easier to use from Python, preserving interactivity. |
|
|
|
|
|
