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dc.contributor.authorDominguez-Sanchez, Emilio-
dc.contributor.authorRos, Alberto-
dc.contributor.otherFacultades, Departamentos, Servicios y Escuelas::Departamentos de la UMU::Ingeniería y Tecnología de Computadoreses
dc.date.accessioned2023-06-28T11:19:44Z-
dc.date.available2023-06-28T11:19:44Z-
dc.date.created2023-
dc.date.issued2023-
dc.identifier.citation2023 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS) pp.:71-80es
dc.identifier.isbn979-8-3503-9739-0-
dc.identifier.urihttp://hdl.handle.net/10201/132447-
dc.description© 2023. IEEE. This document is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0 This document is the accepted version of a published work that appeared in final form in 2023 IEEE International Symposium on Performance Analysis of Systems and Software To access the final work, see DOI: 10.1109/ISPASS57527.2023-
dc.description.abstractBranch predictors are the hardware logic that tries to guess the outcome of a branch instruction before its execution. Currently, researchers make use of simulation tools to measure the accuracy of their predictors against hundreds of program traces. However, these simulations require multiple hours of computation time. This makes the prototyping slow and limits the ability of the researcher to test different strategies. Besides, current simulators are built as frameworks instead of libraries,in the sense that they call the user code and not the other way around. As a result, the user has no control of the program execution and they cannot optimize it for the experiment at hand. In this paper we present Modular Branch Prediction Library (MBPlib), an open-source C++ library that solves the aforementioned issues. MBPlib runs over 18.4 × faster than the current fastest framework, and its trace format uses 6.5 × less disk space. MBPlib also makes development easier by providing utilities that are typically used as subcomponents in most branch prediction designs. Moreover, the library features one of the largest collections of example implementations, including traditional as well as state-of-the-art predictors. MBPlib will allow researchers to significantly reduce the time needed for evaluation. Furthermore, by giving the option of obtaining results within seconds, as well as by means of the broad collection of examples, written in a modern and uniform code style, MBPlib can significantly decrease the barrier to entry into the field. Thus, we believe that MBPlib is also a great tool for computer architecture classes.es
dc.formatapplication/pdfes
dc.format.extent10es
dc.languageenges
dc.publisherIEEE Computer Societyes
dc.relationEuropean Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (ECHO: Extending Coherence for Hardware-Driven Optimizations in Multicore Architectures, grant agreement No 819134, Consolidator Grant, 2018).es
dc.relation.ispartofInternational Symposium on Performance Analysis of Systems and Software (ISPASS)es
dc.rightsinfo:eu-repo/semantics/openAccesses
dc.rightsAtribución 4.0 Internacional*
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectBranch-predictiones
dc.subjectSimulationes
dc.subjectLibraryes
dc.titleMBPlib: Modular Branch Prediction Libraryes
dc.typeinfo:eu-repo/semantics/articlees
dc.identifier.doi10.1109/ISPASS57527.2023.00016-
Aparece en las colecciones:Artículos: Ingeniería y Tecnología de Computadores

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