Behind the scene III Cloud computing
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1 Behind the scene III Cloud computing Athens, M. Dolenc / R. Klinc
2 Why we do it? Engineering in the cloud is a combina3on of cloud based services and rich interac3ve applica3ons allowing engineers to create, explore, and discover be;er designs (faster). P. Williams and S. Cox, (June 2009), Engineering in the Cloud: An Engineering SoIware + Services Architecture Forged in Turbulent Times, h;p://msdn.microsoi.com/en- us/architecture/aa894305
3 Cloud compu<ng landscape and benefits! Access data/services at any place, from any device and at any 3me! Lower cost of entry! Reliability, scalability, security and sustainability! Minimise infrastructure risk! Reduce run 3me and response 3me! Increased pace of innova3on
4 Cloud deployment models! Public! Private! Hybrid! Community! Special purpose NorthBridge and GigaOM, 2013 Cloud Survey, h;p:// future- cloud- compu3ng- survey- reveals- business- driving- cloud- adop3on- everything- service- era- it
5 ISES Cloud Testbed local data LOCAL local results REMOTE external/remote data external/remote results Hardware specs! IntelR XeonR Processor (2.26 GHz), 8/16 GB RAM Web browser! 152 CPU cores! Fiber- Channel disk array 5 TB nd Navigator ISES Cloud API AWS resources ISES resources Service Framework Parametric studies Parallel MPI applications ISES app ISES app ISES app SoIware! Ubuntu Server LTS! OpenStack cloud infrastructure, HTCondor, MPI enabled! General purpose soiware: MATLAB, BLAS, LINPACK,! ISES specific applica3ons
6 ISES Cloud Test- bed Hardware specs! IntelR XeonR Processor (2.26 GHz), 8/16 GB RAM! 152 CPU cores! Fiber- Channel disk array 5 TB SoIware! Ubuntu Server LTS! OpenStack cloud infrastructure, HTCondor, MPI enabled! General purpose soiware: MATLAB, BLAS, LINPACK,! ISES specific applica3ons
7 ISES Cloud API Requirements! Use- cases! Workflow API! RESTful! Security issues! General usage instruc3ons ISES Cloud API documenta3on, h;p://ises.eu- project.info/api- docs
8 ISES Cloud API Requirements! Use- cases! Workflow API! RESTful! Security issues! General usage instruc3ons ISES Cloud API documenta3on, h;p://ises.eu- project.info/api- docs
9 ISES use of the cloud Stochas3c analysis! Genera3ng large parametric studies! Parametric studies execute one applica3on many 3mes with different sets of input parameters! High- throughput compu3ng (HTC) environment CFD analysis! Computa3onal fluid dynamics! Time consuming! Parallel applica3ons, high- performance compu3ng (HPC) environment
10 HPC vs HTC High- performance compu<ng (HPC) High- throughput compu<ng (HTC) FLOPS environment problem protocols / solu3ons very important, single compu3ng unit sta3c, iden3cal HW units single large problem Message Passing Interface (MPI) not that important, many compu3ng units dynamic, heterogeneous HW and OS parallel independent jobs, parametric study HTCondor, Torque, SETI@Home
11 Benchmarking method! The Mavrica Kindergarten BIM model! Average of three simula3on runs! Test- bed hardware used for local and distributed simula3ons! Wine 1.6 used for execu3ng Windows based applica3ons
12 Benchmarking context Price! Small and small- to- medium (SME) enterprises, engineering industries Flexibility! Workflows, services, systems and subsystems, U3lisa3on rate! Total Cost of Ownership (TCO), energy consump3on, Scalability! Scalability of HW and SW
13 BIM conversion to simula<on data! Slower cloud- based analysis compared to local one small overhead for using remote/ distributed resources.! Other benefits: SaaS, storage/ backup, No. of cases Local 1 CPU Condor 1 0:01:12 0:01:39 ISES Cloud API documenta3on, h;p://ises.eu- project.info/api- docs
14 Passive simula<on! Slower cloud- based analysis compared to local one small overhead for using remote/ distributed resources.! Other benefits: SaaS, storage/ backup, No. of cases Local 1 CPU Condor 1 0:33:47 0:34:24 ISES Cloud API documenta3on, h;p://ises.eu- project.info/api- docs
15 Sensi<vity analysis Applica3on: Grandlund Riuska Notes: Executed in Wine 1.6 environment
16 Sensi<vity analysis Applica3on: Therakles, TUD- IBK Notes: Na3ve Linux applica3on
17 Detailed CFD analysis Sofis3k CFD! Parallel CFD analysis tool for 3D unsteady, incompressible, turbulent, buoyancy- driven flows.! Complementary tools (geometrical modeller, mesh genera3on tools, post- processing tools, etc.) Parallel processing of CFD solver! MPI protocol (MPICH2, OpenMPI)! 64- bit Linux 16 CPUs 96 CPUs ~ 1 week ~ 1 day! Synchroniza3on - restricted paralleliza3on! Small number of large messages
18 Lessons learned Should a SME invest in local cloud/cluster?! It depends on the problem.! No easy answer, depends on ini3al investment cost, technical exper3se, Can we es3mate the price of using public cloud?! Sure, for example Amazon WS - h;p://calculator.s3.amazonaws.com/index.html Can public cloud be used for running high- performance applica3ons?! No, not really. High speed connec3ons (FiberChannel) between computers are needed. How about parametric studies e.g. high- throughput compu3ng?! Sure, for HTC the number of available CPUs is far more important then fast connec3ons or even the speed of (virtual) CPUs.! Large example: Novar3s / 39 years > 9 days / CPUs, 4k USD
19 Thank You! Ques<ons? Presenter: Matevž Dolenc, University of Ljubljana
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