000911980 001__ 911980 000911980 005__ 20230310131330.0 000911980 0247_ $$2Handle$$a2128/32853 000911980 037__ $$aFZJ-2022-05210 000911980 041__ $$aEnglish 000911980 1001_ $$0P:(DE-Juel1)190575$$aBaumann, Thomas$$b0$$eCorresponding author 000911980 1112_ $$a14th JLESC Workshop$$cUrbana, IL$$d2022-09-28 - 2022-09-30$$wUSA 000911980 245__ $$aResilience in Spectral Deferred Corrections 000911980 260__ $$c2022 000911980 3367_ $$033$$2EndNote$$aConference Paper 000911980 3367_ $$2BibTeX$$aINPROCEEDINGS 000911980 3367_ $$2DRIVER$$aconferenceObject 000911980 3367_ $$2ORCID$$aCONFERENCE_POSTER 000911980 3367_ $$2DataCite$$aOutput Types/Conference Poster 000911980 3367_ $$0PUB:(DE-HGF)24$$2PUB:(DE-HGF)$$aPoster$$bposter$$mposter$$s1669702294_19269$$xAfter Call 000911980 520__ $$aAdvancement in computational speed is nowadays gained by using more processing units rather than faster ones. Faults in the processing units caused by numerous sources including radiation and aging have been neglected in the past. However, the increasing size of HPC machines makes them more susceptible and it is important to develop a resilience strategy to avoid losing millions of CPU hours. Parallel-in-time methods target the very largest of computers and are hence required to come with algorithm-based fault tolerance. We look here at spectral deferred corrections (SDC), which is a time marching scheme that is at the heart of parallel-in-time methods such as PFASST. Due to its iterative nature, there is ample opportunity to plug in computationally inexpensive fault tolerance schemes, many of which are also easy to implement. We experimentally examine the capability of various strategies to recover from single bit flips both for serial SDC as well as a small-scale parallel-in-time version with diagonal preconditioners. 000911980 536__ $$0G:(DE-HGF)POF4-5112$$a5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x0 000911980 536__ $$0G:(GEPRIS)450829162$$aDFG project 450829162 - Raum-Zeit-parallele Simulation multimodale Energiesystemen (450829162)$$c450829162$$x1 000911980 536__ $$0G:(EU-Grant)955701$$aTIME-X - TIME parallelisation: for eXascale computing and beyond (955701)$$c955701$$fH2020-JTI-EuroHPC-2019-1$$x2 000911980 7001_ $$0P:(DE-HGF)0$$aGötschel, Sebastian$$b1 000911980 7001_ $$0P:(DE-HGF)0$$aLunet, Thibaut$$b2 000911980 7001_ $$0P:(DE-HGF)0$$aRupprecht, Daniel$$b3 000911980 7001_ $$0P:(DE-Juel1)169281$$aSchöbel, Ruth$$b4 000911980 7001_ $$0P:(DE-Juel1)132268$$aSpeck, Robert$$b5 000911980 8564_ $$uhttps://juser.fz-juelich.de/record/911980/files/Poster.pdf$$yOpenAccess 000911980 909CO $$ooai:juser.fz-juelich.de:911980$$pec_fundedresources$$pdriver$$pVDB$$popen_access$$popenaire 000911980 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)190575$$aForschungszentrum Jülich$$b0$$kFZJ 000911980 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a TUHH$$b2 000911980 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a TUHH$$b3 000911980 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)169281$$aForschungszentrum Jülich$$b4$$kFZJ 000911980 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)132268$$aForschungszentrum Jülich$$b5$$kFZJ 000911980 9131_ $$0G:(DE-HGF)POF4-511$$1G:(DE-HGF)POF4-510$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5112$$aDE-HGF$$bKey Technologies$$lEngineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action$$vEnabling Computational- & Data-Intensive Science and Engineering$$x0 000911980 9141_ $$y2022 000911980 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess 000911980 920__ $$lyes 000911980 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x0 000911980 980__ $$aposter 000911980 980__ $$aVDB 000911980 980__ $$aUNRESTRICTED 000911980 980__ $$aI:(DE-Juel1)JSC-20090406 000911980 9801_ $$aFullTexts