Beam Longitudinal Dynamics Simulation Code Acceleration with GPUs

Τυπάλδος, Γεώργιος Αναστάσιος; Typaldos, Georgios Anastasios

dc.contributor.author	Τυπάλδος, Γεώργιος Αναστάσιος	el
dc.contributor.author	Typaldos, Georgios Anastasios	en
dc.date.accessioned	2023-05-31T11:14:19Z
dc.date.available	2023-05-31T11:14:19Z
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/57783
dc.identifier.uri	http://dx.doi.org/10.26240/heal.ntua.25480
dc.rights	Αναφορά Δημιουργού - Παρόμοια Διανομή 3.0 Ελλάδα	*
dc.rights.uri	http://creativecommons.org/licenses/by-sa/3.0/gr/	*
dc.subject	Κάρτες γραφικών	el
dc.subject	Παράλληλος Προγραμματισμός	el
dc.subject	Υπολογιστική υψηλών επιδόσεων	el
dc.subject	Προγραμματισμός σε CUDA	el
dc.subject	Διαμήκης δυναμική δέσμης	el
dc.subject	Beam Longitudinal Dynamics	en
dc.subject	CUDA	en
dc.subject	GPU	en
dc.subject	High Performance Computing	en
dc.title	Beam Longitudinal Dynamics Simulation Code Acceleration with GPUs	en
heal.type	bachelorThesis
heal.classification	High Performance Computing	en
heal.language	el
heal.language	en
heal.access	free
heal.recordProvider	ntua	el
heal.publicationDate	2023-03-21
heal.abstract	The Beam Longitudinal Dynamics (BLonD) suite is an open-source software package for the simulation of the longitudinal motion of particles in synchrotrons. It has been developed at CERN since 2014 and features a modular structure that allows the user to combine a variety of physics phenomena according to the study requirements. This thesis’s scope is upgrading the BLonD suite by modifying the GPU implementation to host the CuPy Python library rather than the PyCUDA library for GPU acceleration, as it provides a NumPy-like interface and low-level CUDA functionalities. This results in software simplicity, thus a better user experience, and performance enhancements, which achieve significant execution speedup. Various hardware structures and optimization techniques, such as GPU memory hierarchy and thread-coarsening, are tested for additional performance gain. A custom Python roofline model tool is also developed and utilized to assess the efficiency of main kernels. The BLonD-CuPy implementation is evaluated using three NVIDIA GPU models and compared against a multithreaded AMD CPU implementation executed on 16 cores. The CuPy GPU version significantly surpasses the CPU and the previous PyCUDA version’s performance. It achieves up to 80 CPU speedup for intensive configurations and powerful GPU models, versus a respective 75 PyCUDA speedup, while minimizing the required CUDA lines of code from 2600 to 350.	en
heal.advisorName	Σούντρης, Δημήτριος	el
heal.committeeMemberName	Σούντρης, Δημήτριος	el
heal.committeeMemberName	Τσανάκας, Παναγιώτης	el
heal.committeeMemberName	Ξύδης, Σωτήριος	el
heal.academicPublisher	Εθνικό Μετσόβιο Πολυτεχνείο. Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών. Τομέας Τεχνολογίας Πληροφορικής και Υπολογιστών. Εργαστήριο Μικροϋπολογιστών και Ψηφιακών Συστημάτων VLSI	el
heal.academicPublisherID	ntua
heal.numberOfPages	72 σ.	el
heal.fullTextAvailability	false