On Application Design for Manycore Processing Systems in the domain of Neuroscience

Chatzikonstantis, George; Χατζηκωνσταντής, Γεώργιος

dc.contributor.author	Chatzikonstantis, George
dc.contributor.author	Χατζηκωνσταντής, Γεώργιος
dc.date.accessioned	2021-10-07T10:18:15Z
dc.date.available	2021-10-07T10:18:15Z
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/53933
dc.identifier.uri	http://dx.doi.org/10.26240/heal.ntua.21631
dc.rights	Αναφορά Δημιουργού-Όχι Παράγωγα Έργα 3.0 Ελλάδα	*
dc.rights.uri	http://creativecommons.org/licenses/by-nd/3.0/gr/	*
dc.subject	High-performance Computing	en
dc.subject	Computational Neuroscience	en
dc.subject	Multithreading	en
dc.subject	Simulations	en
dc.subject	Cloud Computing	en
dc.subject	Υπολογιστικά Συστήματα Υψηλών Επιδόσεων	el
dc.subject	Υπολογιστική Νευροεπιστήμη	el
dc.subject	Πολυνηματική Επεξεργασία	el
dc.subject	Σύστημα Προσομοίωσης	el
dc.subject	Υπολογιστικό Νέφος	el
dc.title	On Application Design for Manycore Processing Systems in the domain of Neuroscience	en
dc.contributor.department	Microprocessors and Digital Systems Lab	el
heal.type	doctoralThesis
heal.classification	Computer Engineering	en
heal.language	en
heal.access	free
heal.recordProvider	ntua	el
heal.publicationDate	2019-11-04
heal.abstract	In recent years, the rapidly growing field of human neuromodelling has undergone significant changes. Neuroscientists have been taking impressive steps in unveiling the elaborate functionality of the human brain. In doing so, complex mathematical models have been the focus of efforts for describing detailed electrochemical processes that govern the human brain’s behaviour. Such efforts require tremendous computational power in order to render, simulate and analyze in traditional computing systems. As such, the field of computational neuroscience presents an imposing challenge that the realm of high performance computing is tasked with meeting. The evolution of our understanding and mapping of the human brain has been accompanied by a steady increase in the processing power made available in manycore processors. Processors such as Intel’s Xeon Phi line of products have grown to incorporate more advanced computing capability over the years. Due to their nature, they also provide traditional parallel coding tools, which can significantly impact the ease at which applications can be developed, tested and marketed. As a result, manycore CPUs are presented as an attractive alternative to other high-performance computing fabrics, such as GPUs and FPGAs. In this Doctoral thesis, we investigate the impact that manycore processors can have in the domain of computational neuroscience, specifically from the viewpoint of high-detail neuromodelling. By identifying a lack of research efforts in high-performance, largescale, detailed neuronal simulations, the thesis presents the development of a simulator rich in biophysical detail in manycore x86-based processors. Furthermore, the simulator acts as a means to study how manycore processors have evolved in architecture and behaviour, as well as highlight their strengths and drawbacks, in an effort to understand the role that they can play in the landscape of high-performance neuromodelling. This Doctoral thesis presents the complete development effort of the aforementioned simulator. The effort commences with an application specifically designed for the earliest, experimental manycore processors. We meticulously re-configure the simulator and its implementation design in order to take advantage of the continuously evolving architecture of manycore processors. Through this process, the simulator incorporates more modelling options, supports a wider range of simulation parameters and operates on a scalable, modern manycore system. The end product of this thesis is a simulator that constitutes an efficient solution for studying demanding neuronal models, in terms of both performance and energy. Our point of focus and contributions lie in the analysis of manycore processor performance when tasked with demanding neuromodelling workloads. Through the proposed simulator, we highlight how the significant wealth of neuromodelling parameters affects simulation in different manycore processors. As such, we take an important step towards defining proper utilization of high-performance hardware in order to match simulation challenges imposed by the domain of computational neuroscience. Furthermore, significant effort is expended in incorprorating the simulator in a larger, collaborative framework aimed at serving as an online resource for high-performance neuromodelling simulations. The designed framework, named BrainFrame, leverages a heterogeneous ensemble of accelerators, namely manycore processors, FPGAs and GPUs, in order to provide efficient solutions for different modelling and network configurations. We provide a proof of value in the framework by identifying different use cases where a switch in the underlying accelerator hardware yields significant gains in performance, thus reinforcing the value of heterogeneity in high-performance neuromodelling.	en
heal.advisorName	Soudris, Dimitrios
heal.advisorName	Σούντρης, Δημήτριος
heal.committeeMemberName	Soudris, Dimitrios
heal.committeeMemberName	De Zeeuw, Chris I.
heal.committeeMemberName	Strydis, Christos
heal.committeeMemberName	Goumas, Georgios
heal.committeeMemberName	Matsopoulos, George
heal.committeeMemberName	Pneumatikatos, Dionysios
heal.committeeMemberName	Gizopoulos, Dimitris
heal.academicPublisher	Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών	el
heal.academicPublisherID	ntua
heal.numberOfPages	140
heal.fullTextAvailability	false