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Csc presentation

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Csc presentation

  1. 1. CSC 2010Brunel University, London August 2010 Almudena Montiel Gonzalez GSI 1
  2. 2. What is CSC?CERN school of ComputingFor postgraduate students and research workersTo give an overview of some computingtechnologies involved in particle physics andsome concepts concerning this kind of physics 2
  3. 3. Organization Physics Base Data Computing Technologies Technologies - Computer Architecture- Intro to physics and Performance Tuningcomputing - Creating Secure- Tools and techniques ROOT Software - Data Technologies- Tools and techniques ROOT - Virtualization- Data Analysis - Networking QoS and Performance 3
  4. 4. Organization Physics Computing Physics Base Data Computing Technologies Technologies- Intro to physics - IntroComputer Architecture - to physics and Performance Tuningcomputing computing Secure - Creating- Tools and techniques- ROOT - ROOTSoftware - Data Technologies - Virtualization- Data Analysis - ToolsNetworking QoS and - and techniques Performance - Data Analysis 3
  5. 5. General introduction to Physics ComputingSoftware and hardware components requiredfor the processing of the experimental data,from the source to the physics analysisThe main goal is data reduction: Very high event rate (40MHz) Event size (>10MB) Large background 4
  6. 6. General introduction to Physics ComputingOnline processing Trigger: Event selection Data acquisition: Interface to detector HW Monitoring Control 5
  7. 7. General introduction to Physics Computing Subdetector at CMS https://cms.web.cern.ch/cms/Resources/Website/Media/Videos/Animations/files/CMS_Slice.gif 6
  8. 8. General introduction to Introduction to Physics Computi CERN School of Computing 2010, Physics Computing CMS L1 trigger example back-to-back opposite sign isolCMS Level 1 trigger muons input rate 40MHz output rate 30-100kHz 2 detector systems: muons/ calorimetersHigh level filter input rate 30-100kHz output rate 100-150Hz CSC 2010 29 Rudi Frühwirth, HEPHY Vienna CSC 2010 Physics Computing General Introduction to Phys 7 197
  9. 9. General introduction to Introduction to Physics Computi CERN School of Computing 2010, Physics Computing CMS L1 trigger example back-to-back opposite sign isolCMS Level 1 trigger muons input rate 40MHz output rate 30-100kHz 2 detector systems: muons/ calorimetersHigh level filter input rate 30-100kHz output rate 100-150Hz CSC 2010 29 Rudi Frühwirth, HEPHY Vienna Raw Data sent to Physics Computing 325MB/s CSC 2010 Tier-0 farm General Introduction to Phys 7 197
  10. 10. General introduction to Physics ComputingOffline processing Calibration Alignment Event Reconstruction Simulation Physics analysis 8
  11. 11. Introduction to Physics ComputingGeneral introduction to CERN School of Computing 2010, Uxbridge Physics Computing Silicon Tracker calibration Incoming particle creates electric Offline processing strips or p charge in g p pixels Calibration Alignment Event Reconstruction Simulation Incoming particle CSC 2010 Physics analysis 45 Rudi Frühwirth, HEPHY ViennaCSC 2010 Physics Computing General Introduction to Physics Computing Lect 8 213
  12. 12. General introduction to Physics ComputingOffline processing Calibration Alignment Event Reconstruction Simulation Physics analysis 8
  13. 13. General introduction to Physics Computing Introduction to Physics Computing CERN School of Computing 2010, Uxbridge Neutral particles (ctd)Offline processing Calibration Alignment Event Reconstruction Simulation Physics analysis CSC 2010 Rudi Frühwirth, HEPHY Vienna 61 8
  14. 14. General introduction to Physics ComputingOffline processing Calibration Alignment Event Reconstruction Simulation Physics analysis 8
  15. 15. ROOTIt is an object-oriented program and librarydeveloped by CERN for particle physics analysis.Developed in 1995, but from 2003 written in C++.What does it provides: Data storage, access and query system. Statistical analysis algorithms. Scientific visualization: 2D, 3D, PDF, LateX Geometrical modeler PROOF parallel query engine 9
  16. 16. ROOTIt is an object-oriented program and librarydeveloped by CERN for particle physics analysis.Developed in 1995, but from 2003 written in C++.What does it provides: Data storage, access and query system. Statistical analysis algorithms. Scientific visualization: 2D, 3D, PDF, LateX Geometrical modeler PoD PROOF parallel query engine 9
  17. 17. ROOT 10
  18. 18. ROOT 10
  19. 19. histograms, functions, parametric e ROOTand to visualize 3D objects (geome 10
  20. 20. ROOT 10
  21. 21. Tools and TechniquesSoftware design and modern tools for PhysicsComputing. As individual Testing: Junit, CppUnit Memory related problems - allocation, memory leaks - malloc, MALLOC_CHECK, memprof, ccmalloc, etc. Performance tools: perfAnal. As part of large code projects Controlling and versioning: CVN, SVN Releases and configuration management of systems: CMS 11
  22. 22. Organization Physics Base Data Computing Technologies Technologies - Computer Architecture- Intro to physics and Performance Tuningcomputing - Creating Secure- Tools and techniques Software - Data Techonlogies- ROOT - Virtualization- Data Analysis - Networking QoS and Performance 12
  23. 23. Organization Physics Base Data Computing Base Technologies Technologies Technologies - Computer Architecture- Intro to physicscomputing - Computer Secure Tuning and Performance - Creating Architecture and- Tools and techniques- ROOT Performance Tuning - Data Techonlogies Software - Virtualization- Data Analysis - Creating Secure and - Networking QoS Software - Virtualization Performance - Networking QoS and Performance 12
  24. 24. Computer Architecture Seven dimensions of performance Computer Architecture and Performance Tuning and performance tuning Computer Architecture and Performance Tuningnsions of performance First three dimensions: Superscalar Pipelining p g imensions: Pipelining Pipelining p g Computational width/SIMD Introduction to processor layout. chitecture and Performance Tuning Superscalar performance al width/SIMD dimension is a “pseudo” Next dimension: SIMD widths:is a “pseudo”dimensions of performance 7Hardware multithreading Superscalar Multithreadingn SIMD width Nodes Pipelining p g ast three dimensions: MultithreadingultithreadingLast t ee d e s o s Multiple cores NodesD so seensions: Sockets Multiple sockets Superscalar sdo” Multiple compute nodesSocketskets SIMD width Multicore 19 Multithreading pute nodes= Single Instruction Multiple Data SIMD Sverre Jarp - CERN NodesOverall impact of programming styles and compilers MulticoreData CSC 2010 Base CERN Sverre Jarp - Technologies Computer Architecture and Performance Tuning Lecture 1 & 2 817s Metrics to define application performance: CPI, #branch Computer Architecture and Performance Tuning Lecture 1 & 2 Sockets 817 instructions, mispredicted branches, #SSE instructions, fails cache. Multicore Jarp - CERN Performance monitoring with pfmon and Perfmon2chitecture and Performance Tuning Lecture 1 & 2 17 13
  25. 25. Creating secure softwareProtection, detection, reactionThreats (and solutions) are not only technical:social engineering 14
  26. 26. Network QoS and performance RSVP / NSIS protocols (simplified) Base Technologies / Networking QoS and PerformanceQoS options: Flow RES R R R Flow senderTechnologies / Networking QoS and Performance RESP Receiver Base Diffserv PrincipleNSIS/RSVP R Base Technologies / Networking QoS and Performance MPLS NSIS/RSVP Priority Mark Priority traffic P2 Priority traffic P1 RESERVE control message sent periodically byflowing inserted Create a “circuit” Traffic source before Pkts enter the (called MPLS path) R over th the “QoS core” MPLS path Diffserv Regular traffic Force all traffic with R Simple examination “Marked” destination of mark provides R same priority receiver replies with a RESPONSE control message R packets same Qos requirement MPLS RESPONSE reserve resources on Rthe route back to follow the same DiffServ path MPLS MPLS path if RESERVE not repeated after time-out, resources releasedTCP, UDP and RTP protocols in real-time 37 François Fluckiger – CERN CSC 2010 Base Technologies Networking QoS and PerformanceFrançois Fluckiger – 2 42 Lecture 1 and CERN CSC 2010 Base Technologies Networking QoS and Performance Lecture 1 and 2 28 972 François Fluckiger – CERNstreaming traffic over the Internet 977 CSC 2010 Base Technologies Networking QoS and Performance 963 15
  27. 27. VirtualizationVirtualization refers to technologies designed toprovide a layer of abstraction betweencomputer hardware systems and the softwarerunning on them. 16
  28. 28. Virtualization Memory Resource Virt. mem Network StorageVirtualization Platform OS level Partial Full virtualization Application Paravirtualization HW assisted 17
  29. 29. Virtualization: Introduction to virtualization technology Hypervisor Architecture VirtualizationA technique that all (softwarebased) virtualization solutions Platform virtualizationuse is ring deprivileging: the It p operating system that runs g y hides the physical originally on ring of is computing characteristics 0 a moved to another less privileged ring like platform from the users ring 1. This allows the (hypervisor or l Thi Host softwareVMM to control ll th t t the guest OS access to VMM) creates a simulated resources. computer environment, a It avoids one guestfor itskicking virtual machine, OS guest another out of memory, or a OS. guest OS controlling the hardware directly. 18
  30. 30. VirtualizationPartial virtualizationThe machine simulates only some parts ofthe host hardware environment.Does not allow any “guest” operatingsystem to work. 19
  31. 31. VirtualizationFull virtualization 20
  32. 32. VirtualizationParavirtualization 21
  33. 33. VirtualizationWhy? Server consolidation Isolated sandboxes per user. Running untrusted applications will not risk the entire box Provisioning with no need of up-front purchase 22
  34. 34. Virtualization...Why? Disaster recovery: the restarting and relocating of a VM is faster Developing: being able to run on different platforms Easier management, it is easier to automate, easier to scale the number of VMs up and down 23
  35. 35. VirtualizationUse Cases Software testing: ETICS Software development: CernVM Volunteering computing: BOINC 24
  36. 36. VirtualizationUse Case: Cloud Computing Get services on demand over the network Service: Software, Platform or Infrastructure 25
  37. 37. Virtualization: Application of the virtualization technology VirtualizationRethinking Application Deployment Use Case: CernVM Virtual Machine Application mphasis in the ‘Application’ Virtual appliance Libraries The application dictates the platform and not the contrary Runs on any virtualization platform and Tools provides consistent and effortless pplication (e.g. of experiment SW installation simulation) is Databases undled with its libraries, services OS nd bitsConfiguration of a CernVM image for a of OS Self-contained, self-describing, deployment ready specific experiment such as ALICE or LHCb and run some experiment specificWhat makes the Application ready to run in any target application xecution environment? e.g. Traditional, Grid, Cloud26
  38. 38. and group to ‘alice’ (we will need this for the next p Virtualization 27
  39. 39. Organization Physics Base Data Computing Technologies Technologies - Computer Architecture- Intro to physics and Performance Tuningcomputing - Creating Secure- Tools and techniques Software - Data Technologies- ROOT - Virtualization- Data Analysis - Networking QoS and Performance 28
  40. 40. Organization Physics Data Technologies Base Data Computing Technologies Technologies - Computer Architecture- Intro to physics and Performance Tuningcomputing - Creating Secure- Tools and techniques Software - Data Technologies- ROOT - Data Technologies - Virtualization- Data Analysis - Networking QoS and Performance 28
  41. 41. Data technologies Storage Technologies Physical and logical connectivity Complexity Hardware Components CPU, disk, memory,Storage Technologies PC, disk server motherboard O Network, Storage devices Cluster, Interconnects Local fabric RAID Wide area network World Wide G Cluster Man File Systems (local, 5 Bernd Panzer-Steindel - CERN network and cluster) CSC 2010 Data Technologies Storage Techn 1019 And many other concepts.. 29
  42. 42. Data technologies Storage Technologies Physical and logical connectivity Complexity Hardware Components CPU, disk, memory,Storage Technologies PC, disk server motherboard O Network, Storage devices Cluster, Interconnects Local fabric RAID Wide area network World Wide G Cluster Man File Systems (local, 5 Bernd Panzer-Steindel - CERN network and cluster) CSC 2010 Data Technologies Storage Techn 1019 And many other concepts.. 29
  43. 43. Data technologies Storage Technologies Physical and logical connectivity Complexity Hardware Components CPU, disk, memory,Storage Technologies PC, disk server motherboard O Network, Storage devices Cluster, Interconnects Local fabric RAID Wide area network World Wide G Cluster Man File Systems (local,systems I Cluster file Storage Technologies 5 Bernd Panzer-Steindel - CERN network and cluster) Aggregation of local file systems and Server nodes Clients CSC 2010 Data Technologies Storage Techn 1019 Meta-data server is the new important component Mapping of files to locations And many other Data base implementation (Oracle, MySQL, ….) Data base Control data flow between the clients and the concepts.. Meta-data server Data flow directly between clients and disk server Server S Two types of implementations : 1. Device driver implementation via the virtual file system the application accesses the data via a file system syntax th li ti th d t i fil t t mount point, looks like a local file system, same commands (ls, rm, mkdir, etc.) 2. Translation of application IO commands ( p , read, write, seek, close) via pp (open, , , , ) special IO library linked into the executable. Special commands for ls/rm/mkdir … 42 Bernd Panzer-Steindel - CERN 29
  44. 44. If you are interested:http://www-linux.gsi.de/~amontiel/CSC2010.pdf.gz 30
  45. 45. Beyond the lectures
  46. 46. Beyond the lectures
  47. 47. Any questions? 32

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