MBPlib: Modular Branch Prediction Library

Dominguez-Sanchez, Emilio; Ros, Alberto

Publication:
MBPlib: Modular Branch Prediction Library

dc.contributor.author	Dominguez-Sanchez, Emilio
dc.contributor.author	Ros, Alberto
dc.contributor.department	Ingeniería y Tecnología de Computadores
dc.date.accessioned	2023-06-28T11:19:44Z
dc.date.available	2023-06-28T11:19:44Z
dc.date.created	2023
dc.date.issued	2023
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.abstract	Branch 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.format	application/pdf	es
dc.format.extent	10	es
dc.identifier.citation	2023 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS) pp.:71-80
dc.identifier.doi	10.1109/ISPASS57527.2023.00016
dc.identifier.eissn	979-8-3503-9739-0
dc.identifier.uri	http://hdl.handle.net/10201/132447
dc.language	eng	es
dc.publisher	IEEE Computer Society	es
dc.relation	European 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.ispartof	International Symposium on Performance Analysis of Systems and Software (ISPASS)	es
dc.rights	info:eu-repo/semantics/openAccess	es
dc.rights	Atribución 4.0 Internacional	*
dc.rights	Attribution-NonCommercial-NoDerivatives 4.0 Internacional	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/4.0/	*
dc.subject	Branch-prediction	es
dc.subject	Simulation	es
dc.subject	Library	es
dc.title	MBPlib: Modular Branch Prediction Library	es
dc.type	info:eu-repo/semantics/article	es
dspace.entity.type	Publication	es