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CAPre: Code-Analysis based Prefetching for Persistent object stores
dc.contributor.author | Touma, Rizkallah |
dc.contributor.author | Queralt Calafat, Anna |
dc.contributor.author | Cortés, Toni |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors |
dc.contributor.other | Barcelona Supercomputing Center |
dc.date.accessioned | 2020-05-07T12:22:19Z |
dc.date.available | 2021-11-12T01:32:50Z |
dc.date.issued | 2019-11-12 |
dc.identifier.citation | Touma, R.; Queralt, A.; Cortés, T.. CAPre: Code-Analysis based Prefetching for Persistent object stores. "Future generation computer systems", Octubre 2020, vol. 111, p. 491-506. |
dc.identifier.issn | 0167-739X |
dc.identifier.other | https://arxiv.org/abs/2005.11259 |
dc.identifier.uri | http://hdl.handle.net/2117/186696 |
dc.description.abstract | Data prefetching aims to improve access times to data storage systems by predicting data records that are likely to be accessed by subsequent requests and retrieving them into a memory cache before they are needed. In the case of Persistent Object Stores, previous approaches to prefetching have been based on predictions made through analysis of the store’s schema, which generates rigid predictions, or monitoring access patterns to the store while applications are executed, which introduces memory and/or computation overhead. In this paper, we present CAPre, a novel prefetching system for Persistent Object Stores based on static code analysis of object-oriented applications. CAPre generates the predictions at compile-time and does not introduce any overhead to the application execution. Moreover, CAPre is able to predict large amounts of objects that will be accessed in the near future, thus enabling the object store to perform parallel prefetching if the objects are distributed, in a much more aggressive way than in schema-based prediction algorithms. We integrate CAPre into a distributed Persistent Object Store and run a series of experiments that show that it can reduce the execution time of applications from 9% to over 50%, depending on the nature of the application and its persistent data model. |
dc.description.sponsorship | This work has been supported by the European Union’s Horizon 2020 research and innovation program under the BigStorage European Training Network (ETN) (grant H2020-MSCA-ITN-2014- 642963), the Spanish Ministry of Science and Innovation (contract TIN2015-65316) and the Generalitat de Catalunya, Spain (contract 2014-SGR-1051). |
dc.format.extent | 16 p. |
dc.language.iso | eng |
dc.publisher | Elsevier |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International |
dc.rights | ©2019 Elsevier |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Arquitectura de computadors |
dc.subject.lcsh | Object-oriented programming (Computer science) |
dc.subject.lcsh | Cache memory |
dc.subject.other | Persistent object stores |
dc.subject.other | Static code analysis |
dc.subject.other | Data prefetching |
dc.subject.other | Parallel prefetching |
dc.subject.other | Object-oriented programming languages |
dc.title | CAPre: Code-Analysis based Prefetching for Persistent object stores |
dc.type | Article |
dc.subject.lemac | Programació orientada a l'objecte (Informàtica) |
dc.subject.lemac | Memòria cau |
dc.contributor.group | Universitat Politècnica de Catalunya. CAP - Grup de Computació d'Altes Prestacions |
dc.identifier.doi | 10.1016/j.future.2019.10.023 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S0167739X19314293 |
dc.rights.access | Open Access |
local.identifier.drac | 28099836 |
dc.description.version | Postprint (author's final draft) |
dc.relation.projectid | info:eu-repo/grantAgreement/MINECO//TIN2015-65316-P/ES/COMPUTACION DE ALTAS PRESTACIONES VII/ |
dc.relation.projectid | info:eu-repo/grantAgreement/AGAUR/V PRI/2014 SGR 1051 |
dc.relation.projectid | info:eu-repo/grantAgreement/EC/H2020/642963/EU/BigStorage: Storage-based Convergence between HPC and Cloud to handle Big Data/BigStorage |
local.citation.author | Touma, R.; Queralt, A.; Cortés, T. |
local.citation.publicationName | Future generation computer systems |
local.citation.volume | 111 |
local.citation.startingPage | 491 |
local.citation.endingPage | 506 |
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