NCSI: Performance Evaluation of Calorimeter Clustering Algorithms

Status	Completed
Mentor Name	Alina Lazar
Mentor's XSEDE Affiliation	Plan to apply for Research Allocation
Mentor Has Been in XSEDE Community	1-2 years
Project Title	Performance Evaluation of Calorimeter Clustering Algorithms
Summary	We develop optimized python clustering algorithms to group similar particle trajectories. These algorithms can then be used by the scientists at CERN to predict the traits of the particles in the Large Hadron Collider based on their trajectories. With the ever-growing data from scientific experiments, it is imperative to have automatic ways to analyze that data. Specifically, we analyze clustering algorithms such as DBSCAN, HDBSCAN and CLUE and compare them in terms of accuracy and computing time.
Job Description	The student will be responsible of: - implementing the clustering algorithms using C, C++ and Python. We plan to implement both CPU and GPU versions. - performing in depth evaluation of the methods implemented. - read and summarize related research papers - perform data analysis of the results - prepare posters and research papers.
Computational Resources	To perform a real evaluation of the developed methods, access to a supercomputer is essential. Given the large size of the existing data sets, it is required to run these methods on multiple CPUs and GPUs.
Contribution to Community
Position Type	Apprentice
Training Plan	The student needs to be proficient in C, C++ and Python programming The student needs to be familiar with machine learning algorithms and in particular with clustering algorithm. The student will learn about parallel programming in general and CUDA in particular.
Student Prerequisites/Conditions/Qualifications	The student worked as intern at LBNL under my metorship for two summers, 2008 and 2019.
Duration	Summer
Start Date	06/10/2020
End Date	08/07/2020