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scalable dynamic multi-resource allocation in multicore systems PDF

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SCALABLE DYNAMIC MULTI-RESOURCE ALLOCATION IN MULTICORE SYSTEMS ADissertation PresentedtotheFacultyoftheGraduateSchool ofCornellUniversity inPartialFulfillmentoftheRequirementsfortheDegreeof DoctorofPhilosophy by XiaodongWang January2017 (cid:13)c 2017XiaodongWang ALLRIGHTSRESERVED SCALABLEDYNAMICMULTI-RESOURCEALLOCATIONINMULTICORE SYSTEMS XiaodongWang,Ph.D. CornellUniversity2017 Designing chip multiprocessors (CMPs) that scale to more than a handful of cores is an important goal for the upcoming technology generations. A chal- lenge to scalability is the fact that these cores will inevitably share hardware resources, whether it be on-chip storage, memory bandwidth, the chip’s power budget, etc. Efficiently allocating those shared resources across cores is criti- cal to optimize CMP executions. Techniques proposed in the literature often rely on global, centralized mechanisms that seek to maximize system through- put. Globaloptimizationmayhurtscalability: asmorecoresareintegratedona die, the search space grows exponentially, making it harder to achieve optimal or even acceptable operating points at run-time without incurring significant overheads. Inthisthesis,wepresentXChange[103],aframeworkforscalableresourceal- locationinlarge-scaleCMPs. Inspiredbymarketmechanisms,whicharewidely used in real life to allocate social resources at scale, we propose to address the resource allocation problem in large-scale CMPs as a purely dynamic, largely distributedmarketframework. Eachsharedresourceisassignedavirtualprice, whichchangesovertimetoreflectitssupply-demandrelationship. Coresinthe CMP act as market players: they seek to maximize their own utilities by bid- dingforsharedresourceswithintheirbudgets. Becauseeachcoreworkslargely independently, the resource allocation becomes a scalable, mostly distributed decision-making process. Cores in the system are able to dynamically monitor and learn their own resource-performance relationship and bid accordingly— nopriorknowledgeoftheworkloadcharacteristicsisassumed. We show our market-based resource allocation mechanism delivers supe- rior system efficiency and fairness against existing proposals. This approach is purely empirical, however, and thus it does not provide any guarantees on the loss of efficiency and fairness. It is well known, for example, that market mechanisms in equilibrium can sometimes be highly inefficient—this is known as Tragedy of Commons [44]. Therefore, we study the theoretic properties of our market-basedapproach,andestablishaboundonthelossofefficiencyandfair- ness in the market-based resource allocation, by introducing two new metrics, marketutilityrange(MUR)andmarketbudgetrange(MBR).Further,guidedby such theoretic foundations, we propose ReBudget [104], a budget re-assignment technique that is able to systematically trade off efficiency and fairness in an adjustablemanner. In the process of formulating our XChange framework, we propose novel mechanisms to support fine-grain resource management. Specifically, we pro- pose SWAP [102], a scalable and fine-grain cache management technique that seamlessly combines set and way partitioning. By cooperatively managing cache ways and sets, SWAP (“Set and WAy Partitioning”) can successfully pro- videhundredsoffine-grainedcachepartitionsforthemanycoreera. Weimple- ment SWAP as a user-space management thread on Cavium’s ThunderX, a real server-grade48-coreprocessor,andweshowthatSWAPsignificantlyimproves system throughput by twice as much speedup as what we can obtain by using only ThunderX’s way partitioning mechanism. This was a collaboration with Caviumengineers. BIOGRAPHICALSKETCH Xiaodong Wang attended Shanghai Jiao Tong University as a undergraduate student in the department of Electronic Engineering, where he earned a B.Eng degree in2011. Hemet Prof. Hsien-Hsin Sean Leethere, who motivated him to participate in an exchange program at Georgia Institute of Technology in 2010. During his time at Georgia Tech, he’s inspired by doing research with Sean in the field of computer architecture and 3D memories, and therefore he decided to join the graduate program at Cornell University, pursuing a PhD degree in Computer Engineering. Through his years at Cornell, he has the opportunity to work with Prof. Jose´ Mart´ınez as his research advisor, and focus on the scal- ability challenge of resource allocation in large-scale chip-multiprocessors. His paper was nominated to be the best paper candidate in HPCA 2015, and he is the winner of Cavium Octeon Trophy in 2014, and Cavium ThunderX Trophy in2015-2016. iii Dedicatedtomyparents,mywife,mydearfriends,andeveryoneelsewho believedinmealongtheway. iv ACKNOWLEDGEMENTS Completing my PhD degree is one of the most challenging tasks in my life so far. Looking back over the past six years of my PhD journey, I feel extremely gratefultohavesomanypeoplehelpmealongtheway. Whatmakesmeproud is not only the projects that I’ve done, but also the moments that I share with my family, my mentors, my friends, and my colleagues, that I’ll always cherish fortherestofmylife. I should start by thanking my parents. Words simply cannot express my gratitude to their sacrifices. My father not only works tirelessly to support me financially, but also advises me on any issues that I encounter. He encourages me to think out of the box and make my own decisions, but he also pulls me back to the right track whenever necessarily. My mother dedicated her life for taking care of me. I wasn’t a strong kid, both physically and mentally. She taught me to be a fighter, which is the characteristic that I attribute most of my success to. I am truly proud of my parents, and I hope I’ve made you proud of metoo. I want to thank my wife, Jia. Living and studying in a place thousands of milesawayfromyourfamilyandchildhoodfriendsisneveraneasyjob. Iamso luckytomeetyou,Jia,hereatCornell. Becauseofyou,myapartmentislongera “sleep area”, but a warm home that we enjoy family life. PhD is a long journey, andIcannotbelievethatIwillsurvivewithoutyou. I’msohappytohavegone throughmyPhDcareerwithyou,andtocontinueourlife-longjourney. My PhD work would not have been possible without my advisor, Prof. Jose´ Mart´ınez. There are numerous of times in my PhD that I become frustrated, either by experimental infrastructures, simulation results, paper reviews, etc. He always motivates me to get out of the swamp, and to be keen and excited v again about my research projects. Whenever I met with any difficulties, I know he’s always there and ready to help. We have brainstormed and debated about countless of ideas, and worked together days and nights for a dozen of dead- lines. I learned from him not only technically, but also in the aspect of a human beingandagoodresearcher. Inaddition,heisneverrefrainedfromofferingme his most frank opinions, whether it’s technical, professional, or personal. I’m deeplygratefulforallhiseffortandgreatcontributions. I would like to thank my committee members, Prof. David Albonesi and Prof. Emin Sirer, for their insightful suggestions and advices. Whenever I have any questions or need a second opinion of my ideas, they’re the ones that I can always rely on, and they’re always willing to spend their time talking to me no matterhowbusytheyare. I would also like to thank Prof. E´va Tardos. I wasn’t a person that is partic- ular interested in math and theories. She showed me the beauty of them, and howtheequationscanhelpguidingpragmaticapproaches. MyReBudgetwork wouldn’thavebeenmadepossiblewithoutherselflesssupport. Special thanks to my undergraduate advisor, Prof. Hsien-Hsin Sean Lee. When I was considering doing a PhD, I was clueless about what research di- rection I should pursue. When Prof. Lee came to my undergraduate institu- tion, and gave us a talk about computer architecture and 3D architecture, it openedupanewworldtome,andIimmediatelydecidedthatitisthefieldthat I am dedicated to. During my one-semester exchange study, Prof. Lee not only taught me the computer architecture basics, but also led me into the world of research. HetrulybuiltthefoundationofmyPhDcareer. I want to thank my co-authors, Jeff Setter, and Shuang Chen, for their great contributions to the SWAP work in this thesis. Jeff and I first came up with vi the SWAP idea, and we built the first prototype together with three months of hard working. Shuang joined the team one year later, and we work tirelessly to perfect this project. I am greatly honored to have the opportunity to work with bothofyou,andIbelieveyouwillbeverysuccessfulinyourcareer. MyfellowCSLcolleagueshavebeenfantasticinhelpingmecompletingthis arduous PhD journey. Saugata Ghose is my mentor. His enthusiasm and pas- sion in research and teaching has a great impact on me, and I admire him as if he’s my elder brother. I’m also thankful to my lab mates, Janani Mukundan, Maia Kelner, and Skand Hurkat, for being with me in the same office and sup- porting me throughout my PhD. Special thanks to Abhinandan Majumdar and Shreesha Srinath, for our special friendship of joining CSL at the same time. I surely believe that both of you will be tremendously successful in anything you’ll do. I also have been fortunate to be friends with Yao Wang, Tao Chen, RobertKarmazin,JonanthanTse,andalltheotherCSLstudents. vii TABLEOFCONTENTS BiographicalSketch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii Dedication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v TableofContents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii ListofTables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi ListofFigures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xii 1 Introduction 1 1.1 TheScalabilityChallengeofResourceAllocationinCMPs . . . . 1 1.1.1 AMarket-basedApproach . . . . . . . . . . . . . . . . . . 3 1.1.2 TheoreticGuaranteesinSystemEfficiencyandFairness . . 4 1.2 Fine-GrainCacheManagement . . . . . . . . . . . . . . . . . . . . 5 2 XChange: ScalableResourceAllocationinCMPs 7 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2 MotivationofOurApproach . . . . . . . . . . . . . . . . . . . . . 9 2.3 Market-BasedFramework . . . . . . . . . . . . . . . . . . . . . . . 12 2.3.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.4 Mechanism: MarketParticipants . . . . . . . . . . . . . . . . . . . 17 2.4.1 UtilityModel . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.4.2 BiddingStrategy . . . . . . . . . . . . . . . . . . . . . . . . 23 2.4.3 DesignIssues . . . . . . . . . . . . . . . . . . . . . . . . . . 25 2.5 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 2.5.1 Hardware . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 2.5.2 Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 2.6 ExperimentalMethodology . . . . . . . . . . . . . . . . . . . . . . 33 2.6.1 ArchitecturalModel . . . . . . . . . . . . . . . . . . . . . . 33 2.7 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 2.7.1 XChangevs.Unmanaged . . . . . . . . . . . . . . . . . . . 39 2.7.2 XChangevs.EqualShare . . . . . . . . . . . . . . . . . . . . 40 2.7.3 XChangevs.GHC,REF . . . . . . . . . . . . . . . . . . . . 41 2.7.4 OverallEffectofWealthRedistribution . . . . . . . . . . . 42 2.8 First-orderModelValidation . . . . . . . . . . . . . . . . . . . . . . 43 2.9 Scalability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 2.10 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 3 ReBudget: Trading Off Efficiency vs. Fairness in Market-Based Multi- coreResourceAllocationviaRuntimeBudgetReassignment 47 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 3.2 MarketFramework . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 3.2.1 MarketEquilibrium . . . . . . . . . . . . . . . . . . . . . . 53 3.2.2 Efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 viii

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We show our market-based resource allocation mechanism delivers supe- seems to be no fundamental reason why the utility profiles could not be de- leave these and other possible options as future work. [18] Alper Buyuktosunoglu Canturk Isci, Chen-Yong Cher, Pradip Bose, and. Margaret
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