Memory management in hybrid DRAM/NVM systems using LSTMs

Σταυρακάκης, Κωνσταντίνος; Stavrakakis, Konstantinos

dc.contributor.author	Σταυρακάκης, Κωνσταντίνος	el
dc.contributor.author	Stavrakakis, Konstantinos	en
dc.date.accessioned	2022-06-14T09:27:33Z
dc.date.available	2022-06-14T09:27:33Z
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/55274
dc.identifier.uri	http://dx.doi.org/10.26240/heal.ntua.22972
dc.rights	Αναφορά Δημιουργού 3.0 Ελλάδα	*
dc.rights.uri	http://creativecommons.org/licenses/by/3.0/gr/	*
dc.subject	Μηχανική μάθηση	el
dc.subject	DRAM	en
dc.subject	Page scheduling	en
dc.subject	Non volatile memory	en
dc.subject	K-Means	en
dc.subject	Επαναληπτικά νευρωνικά δίκτυα	el
dc.subject	Long short term memory networks	en
dc.title	Memory management in hybrid DRAM/NVM systems using LSTMs	en
heal.type	bachelorThesis
heal.classification	Αρχιτεκτονική υπολογιστών	el
heal.classification	Μηχανική μάθηση	el
heal.language	en
heal.access	free
heal.recordProvider	ntua	el
heal.publicationDate	2022-02-18
heal.abstract	Heterogeneous memory technologies have been widely used in effort to address the ever-increasing demands of modern applications for larger main memory capacity. The new technologies showcase vastly greater differences in terms of capacity, latencies and bandwidth. This heterogeneity along with the the greater irregularity of emerging workloads, render state-of-the-art memory management solutions insufficient; thus calling for more intelligent methods. In this diploma Thesis, we design and evaluate a scheduler which intelligently places application data, on a Page granularity, across hybrid memory components using Artificial Neural Networks. The proposed Scheduler combines intelligent page placement decisions leveraging LSTM networks with existing history-based data tiering methods. The scheduler focuses the machine learning on a page subset whose timely movement will reveal most application performance improvement, while being mindful of computation resources. K-Means address space clustering is also utilized to augment the eviction policy used by the proposed scheduler in order to provide application performance boost. That boost is on average 10% according to our evaluation process. Our performance evaluation also indicates that the proposed Scheduler significantly reduces the performance gap between existing solutions and an oracle scheduler with a priori knowledge of the page access patterns, while being a potential candidate for designing low-power oriented Hybrid Memory Systems as well.	en
heal.advisorName	Σούντρης, Δημήτριος	el
heal.committeeMemberName	Σούντρης, Δημήτριος	el
heal.committeeMemberName	Τσανάκας, Παναγιώτης	el
heal.committeeMemberName	Γκούμας, Γεώργιος	el
heal.academicPublisher	Εθνικό Μετσόβιο Πολυτεχνείο. Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών. Τομέας Τεχνολογίας Πληροφορικής και Υπολογιστών	el
heal.academicPublisherID	ntua
heal.numberOfPages	115 σ.	el
heal.fullTextAvailability	false