000123848 001__ 123848
000123848 005__ 20240319081009.0
000123848 0247_ $$2doi$$a10.1109/ACCESS.2022.3230068
000123848 0248_ $$2sideral$$a132419
000123848 037__ $$aART-2022-132419
000123848 041__ $$aeng
000123848 100__ $$0(orcid)0000-0003-1550-735X$$aSegarra, J.$$uUniversidad de Zaragoza
000123848 245__ $$aImproving the configuration of the predictable ACDC data cache for real-time systems
000123848 260__ $$c2022
000123848 5060_ $$aAccess copy available to the general public$$fUnrestricted
000123848 5203_ $$aIn real-time systems, analyzing the worst-case execution time (WCET) of a task in the presence of data caches is hard. The ACDC is a data cache that provides predictability, facilitating WCET analysis. It works by granting data cache replacement permission to specific load/store instructions. Nonetheless, knowing how to select these instructions to minimize the WCET, i.e., configuring the ACDC, is not trivial. In this paper, we propose four new methods to configure the ACDC, and compare them with existing methods. Unlike those in previous studies, our proposed methods provide specific ACDC configurations for the different phases of a given task, instead of a single ACDC configuration per task. We evaluate the WCET bounds obtained when using different ACDC configuration methods on the TACLeBench benchmark suite. Our results show that the most complex benchmarks work better with multiple-content configurations, which indicates that realistic tasks may also benefit from this kind of configuration. The methods proposed in this study improve the WCET in more than 60% of cases, with an average WCET improvement of nearly 5% and up to 50% in some cases.
000123848 536__ $$9info:eu-repo/grantAgreement/ES/AEI/PID2019-105660RB-C21-AEI-10.13039-501100011033$$9info:eu-repo/grantAgreement/ES/AEI/PID2020-117713RB-I00$$9info:eu-repo/grantAgreement/ES/DGA-ESF/T58-20R
000123848 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000123848 590__ $$a3.9$$b2022
000123848 592__ $$a0.926$$b2022
000123848 591__ $$aCOMPUTER SCIENCE, INFORMATION SYSTEMS$$b73 / 158 = 0.462$$c2022$$dQ2$$eT2
000123848 591__ $$aTELECOMMUNICATIONS$$b41 / 88 = 0.466$$c2022$$dQ2$$eT2
000123848 591__ $$aENGINEERING, ELECTRICAL & ELECTRONIC$$b100 / 274 = 0.365$$c2022$$dQ2$$eT2
000123848 593__ $$aComputer Science (miscellaneous)$$c2022$$dQ1
000123848 593__ $$aMaterials Science (miscellaneous)$$c2022$$dQ1
000123848 593__ $$aEngineering (miscellaneous)$$c2022$$dQ1
000123848 594__ $$a9.0$$b2022
000123848 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000123848 700__ $$aMarti-Campoy, A.
000123848 7102_ $$15007$$2035$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Arquit.Tecnología Comput.
000123848 773__ $$g10 (2022), 132708-132724$$pIEEE Access$$tIEEE Access$$x2169-3536
000123848 8564_ $$s1712079$$uhttps://zaguan.unizar.es/record/123848/files/texto_completo.pdf$$yVersión publicada
000123848 8564_ $$s2694579$$uhttps://zaguan.unizar.es/record/123848/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000123848 909CO $$ooai:zaguan.unizar.es:123848$$particulos$$pdriver
000123848 951__ $$a2024-03-18-15:00:01
000123848 980__ $$aARTICLE