NCSI: Parallelizing Plasma Calculations

Status	Completed
Mentor Name	Wolfgang Kerzendorf
Mentor's XSEDE Affiliation	Researcher
Mentor Has Been in XSEDE Community	1-2 years
Project Title	Parallelizing Plasma Calculations
Summary	The open-source TARDIS supernova simulation code is written in Python and part of it is accelerated with the Numba just-in-time (JIT) compiler framework. The other part of TARDIS relies extensively on pandas table operations. The project for the spring semester will be to profile the current pandas-based plasma code and research if frameworks like dask could be used to parallelize and speed-up the plasma calculation part. The student will gain insights into code performance analysis, modern frameworks like dask, and open-source science code development (using modern practices such as version control, continuous integration, code review).
Job Description	The student will learn how TARDIS uses pandas - a complex table and database framework - to optimize its performance. The student will attend regular skills meetings and astrophysics meetings to learn new skills and understand how TARDIS applies to scientific research. The student will improve their knowledge of the git version control system, an integral part of the TARDIS software ecosystem and the wider software development community. The student will develop their Python skills to improve the current performance of TARDIS on HPC resources, with a focus on new distributed frameworks like dask. This skill development will be assisted by mentors in the TARDIS community (each one of our student has two mentors). They will test their improvements on the ICER cluster to ensure that the goals of increased performance have been met. An important aspect for us is the documentation of such a project and all results will be made available through (https://tardis-sn.github.io/tardis/) The code will be made freely available as part of the open-source TARDIS repository (BSD3 licensed).
Computational Resources	The host institution has access to a tier 3 XSEDE facility that will be used in the early stages of this project. The PI has one node on the local supercomputer for quick and easy access to such a machine.
Contribution to Community	TARDIS is an open-source code that is already widely used in the computational/theory astrophysics community. This means that all of the results from this project will empower supernova theorists (many of which are part of the XSEDE community). Additionally, new insights into optimally using new parallelization frameworks such as dask, will profit the community as TARDIS is open source. We are moving to increase the complexity of the TARDIS simulation to include more microphysics as well as generating large grids of TARDIS spectra for parameter inference applications. This comes with a commensurate increase in the required computational power. Therefore, the astronomy user community will need TARDIS to be scalable to HPCs. For XSEDE, this will result in a motivated user base that needs HPC resources to perform their research. The optimization of TARDIS for XSEDE resources will enable a higher density of computation for a given amount of HPC resources.
Position Type	Apprentice
Training Plan	The TARDIS collaboration has a rich history of training students via Google Summer of Code and other programs including EMPOWER (2021). This knowledge and skill base means that we have a number of training protocols in place for students. We have twice-weekly skill meetings where mentors in the research group present and discuss a wide range of skills applicable to the TARDIS project. There are also weekly astrophysics meetings to educate members in the scientific applications of TARDIS. For this project, there will be focused mentorship in the pandas framework. The mentor for this project is experienced with HPC usage at both XSEDE facilities and elsewhere.
Student Prerequisites/Conditions/Qualifications	Matthew Bartnik is a third-year undergraduate student who will have had a few months’ experience with TARDIS by the time the EMPOWER project begins in the Spring. He is very proficient in coding with Python and Bash and has experience with using a HPC via Michigan State University’s facilities. He is also well-versed in Pandas, having used it in previous courses, and has demonstrated his capability of utilizing command-line interfaces.
Duration	Semester
Start Date	01/10/2022
End Date	05/13/2022