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Accelerating Distributed Machine Learning by Smart Parameter Server

Accelerating Distributed Machine Learning by Smart Parameter Server. Jinkun Geng , Dan Li and Shuai Wang. Background. Distributed machine learning becomes the common practice, because of: 1. The explosive growth of data size. Background.

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Accelerating Distributed Machine Learning by Smart Parameter Server

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  1. AcceleratingDistributedMachineLearningbySmartParameterServer JinkunGeng, Dan Li and Shuai Wang

  2. Background • Distributedmachinelearningbecomesthecommonpractice,becauseof: • 1.Theexplosivegrowthofdatasize

  3. Background • Distributedmachinelearningbecomesthecommonpractice,becauseof: • 2.Theincreasingcomplexityoftrainingmodel ImageNetCompetition: <10(Hinton, 2012), 22 (Google, 2014), 152 (Microsoft, 2015), 1207 (SenseTime, 2016)

  4. Background • ParameterServer(PS)-basedarchitectureiswidelysupportedbymainstreamDMLsystems.

  5. Background • However,thepowerofPSarchitecturehasnotbeenfullyexploited. • 1.Communicationredundancy • 2.Stragglerproblem

  6. Background • Adeeperinsight… • 1.Worker-centricdesignislessefficient • 2.PScanbemoreintelligent(i.e.SmartPS) SmartPS

  7. Background • TomakePSmoreintelligent… • Dependency-Aware • Straggler-Assistant

  8. ASimpleModelofParameters

  9. WorkflowofPS-basedDML

  10. WorkflowofPS-basedDML

  11. WorkflowofPS-basedDML

  12. DesignStrategies • TomakePSmoreintelligent… • 1.Selectiveupdate() • 2.Proactivepush() • 3.Prioritizedtransmission() • 4.Unnecessarypushblockage()

  13. Strategy1:SelectiveUpdate

  14. Strategy1:SelectiveUpdate

  15. Strategy1:SelectiveUpdate

  16. Strategy1:SelectiveUpdate

  17. Strategy2:Proactive Push

  18. Strategy3:Straggler-Assistant

  19. Strategy3:Straggler-Assistant

  20. Strategy3:Straggler-Assistant

  21. Strategy4:Blocking Unnecessary Pushes

  22. Evaluation • ExperimentSetting： • 17Nodeswithdifferentperformanceconfigurations:1PS+16Worker • 2Benchmarks: • MatrixFactorizationandPageRank • 5Baselines： • BSP， ASP，SSP（slack=1）， SSP（slack=2），SSP （slack=3）

  23. Evaluation MFBenchmark: Withacommonthreshold,SmartPSreducesthetrainingtimeby68.1%~90.3%comparedwiththebaselines.

  24. Evaluation PRBenchmark: Withacommonthreshold,SmartPSreducesthetrainingtimeby65.7%~84.9%comparedwiththebaselines.

  25. FurtherDiscussion • Comparisontosomerecentworks: Bothleveragetheknowledgeofparameterdependency 2.Bothleverageprioritizedtransmission forDMLacceleration

  26. FurtherDiscussion • Comparisontosomerecentworks:

  27. OngoingWork • AdeeperinsightintoPS-basedarch… • FunctionofPS: • 1.ParameterDistribution • 2.ParameterAggregation • FunctionofWorker: • 1.ParameterRefinement ->DataAccessControl ->DataOperation ->DataOperation

  28. OngoingWork ParameterDistribution ParameterAggregation ParameterRefinement

  29. OngoingWork DataAccessControl DataOperation DataOperation

  30. OngoingWork DataAccessControl Token Token Token DataOperation

  31. NextGenerationofSmartPS • ParameterServer->TokenServer • 1.Decoupledata(access)controlanddataoperation • 2.Alight-weightandsmartTokenServerinsteadofParameterServer. TokenServer ParameterServer

  32. Thanks! NASPResearchGroup https://nasp.cs.tsinghua.edu.cn/ https://www.gengjinkun.com/

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