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Charged Particle Track Reconstruction Algorithms for Massively Parallel Systems
SessionPoster Reception
DescriptionThe reconstruction of the trajectories of charged particles through detector experiments is a core computational task in the domain of high-energy physics. Upcoming upgrades to accelerators such as the Large Hadron Collider as well as to experiments like ATLAS threaten to render existing CPU-based approaches to track reconstruction insufficient, and the use of massively parallel systems - GPGPUs in particular - is an important opportunity to meet future data processing requirements. In my thesis, I investigate the feasibility of GPGPU-based track reconstruction from performance engineering perspective: I focus on structured analysis of application performance, the development of statistical and analytical models of performance, methods for mitigating the challenges of GPGPU programming, and the design and implementation of novel track reconstruction algorithms. The key contributions of my thesis include the development of novel algorithms for hit clustering, seed finding, and combinatorial Kalman filtering, key parts of the track reconstruction process. These algorithms suffer from significant load imbalance and thread divergence, and I have developed a novel statistical method for estimating the performance effects of this, as well as to guide optimization through thread refinement and coarsening. I have developed a method for the automated design space exploration of data storage methods for magnetic fields, which play a crucial role in track reconstruction. Furthermore, I have developed an evolutionary method for finding layouts for multi-dimensional arrays in hierarchical memory systems. My thesis will be concluded by a comprehensive study of the performance of track reconstruction, as guided by the aforementioned research.
Event Type
ACM Student Research Competition: Graduate Poster
ACM Student Research Competition: Undergraduate Poster
Doctoral Showcase
Posters
Research Posters
Scientific Visualization & Data Analytics Showcase
TimeTuesday, 14 November 20235:15pm - 7pm MST
LocationDEF Concourse
Accelerators
Applications
TP