1. Volunteer Computing with BOINC Dr. David P. Anderson University of California, Berkeley SC10 Nov. 14, 2010

2. Goals Explain volunteer computing Teach how to create a volunteer computing project using BOINC Target audience: High-throughput computing users Technical skills: Basic Linux/Apache sysadmin, familiarity with PHP, SQL and XML, C/C++ (optional)

3. Outline Why use volunteer computing? Basic concepts of BOINC Developing BOINC applications (15 minute break) Deploying a BOINC server Deploying applications Submitting jobs Organizational issues

4. Part 1: Why use volunteer computing?

5. The Consumer Digital Infrastructure 1 billion PCs current GPUs: 1 TeraFLOPS (1,000 ExaFLOPS total) Storage: ~1,000 Exabytes Commodity Internet: 10-1,000 Mbps to home Consumers pay for hardware sysadmin network costs electricity

6. Volunteer computing PC owners donate computing resources to projects (e.g., computational science) Applications run at zero priority while PC in use, and/or while PC is not in use

7. Examples Projectstartwhereareapeak #hosts GIMPS1994math10,000 distributed.net1995cryptography100,000 SETI@home I1999UCBSETI600,000 Folding@home1999Stanfordbiology200,000 United Devices2002commercialbiomedicine200,000 CPDN2003Oxfordclimate change150,000 LHC@home2004CERNphysics60,000 Predictor@home2004Scrippsbiology100,000 WCG2004commercialbiomedicine200,000 Einstein@home2005LIGOastrophysics200,000 SETI@home II2005UCBSETI850,000 Rosetta@home2005U. Washbiology100,000 SIMAP2005T.U. Munichbioinformatics10,000...............

8. Current status ~50 projects 500,000 vounteers 800,000 computers

9. High-throughput computing High-performance computing cluster (MPI) supercomputer cluster (batch) Grid Commercial cloud Volunteer computing single job # processors multiple jobs 10K-1M 1000 100 1

10. Volunteer computing is different You don’t buy resources; you ask for them Resources are:  heterogeneous  sporadically available and connected  untrusted and not private  behind firewalls/NATs/proxies

11. Part 2: Basic concepts of BOINC

12. About BOINC Funded by NSF since 2002 Open-source (LGPL) Based at UC Berkeley Few staff, but lots of volunteers software testing translation documentation support (email lists, message boards, Skype)

13. Volunteers and projects volunteers projects CPDN LHC@home WCG attachments

14. BOINC software overview client apps screensaver GUI scheduler MySQL data server daemons volunteer host project server HTTP

15. BOINC scheduler applications Win32 + NVIDIA Win64 Mac OS X app versions jobs instances Win32 N-core Win32 - HW, SW description - existing workload - per resource type: # of instances requested # of seconds requested - app version descriptions - job descriptions

16. Job replication Job instances may fail or return wrong results Job replication: do 2, see if they agree  “agree” may be fuzzy Homogeneous replication  numerical equivalence of hosts Adaptive replication  reduce replication for hosts that seem trustworthy

17. The job pipeline work generator BOINC validator assimilator

18. The BOINC data model App versions, job inputs, job output can consist of arbitrarily many files Each file has a physical name (unique, immutable); each reference to a file has a “logical name” Files have various attributes (e.g., sticky) Each file can have one or more URLs, and are transferred via HTTP App version files are digitally signed

19. What kinds of jobs can BOINC handle? Pretty much anything you’d run on a Grid Bag of tasks (but IPC support soon) Short/long jobs Data intensive, up to a point Geared towards  Few apps, many jobs (high startup cost per app)  Jobs with high slack time

20. Part 3: Application development for BOINC

21. The BOINC runtime environment processes files

22. Native BOINC applications boinc_init()  create runtime system thread boinc_finish()  write finish file boinc_resolve_filename(logical, physical) boinc_fraction_done(x)

23. Checkpointing bool boinc_time_to_checkpoint()  call when in checkpointable state boinc_checkpoint_done()

24. The BOINC wrapper Can use for legacy apps XML input file lists sub-jobs  executable, input files What it does:  interfaces to BOINC client  copies files to/from slot directory  runs executables  does checkpointing at sub-job level

25. Building app versions Linux  gcc Windows  Visual Studio  minGW (gcc) Mac OS X  xcode

26. Multithread apps boinc_init_parallel() Allows suspend/resume of all threads  Unix: fork/exec  Windows: direct thread control

27. GPU app versions Develop for NVIDIA or ATI, with CUDA, CAL, OpenCL, etc. (BOINC supplies samples) Each version has a “plan class” For each plan class, supply a function that determines  can app run on this host? hardware, driver version, etc.  what resources will it use? #CPUs, #GPUs, GPU RAM, etc.

28. VM apps Develop apps on your favorite OS Create a VirtualBox VM image App version consists of  VM wrapper (supplied by BOINC)  VM image  app executable

29. Part 4: Deploying a BOINC server

30. Hardware options Native Linux host  download/compile BOINC software BOINC server VM (VMware/Debian) BOINC Amazon EC2 image

31. Components of a project Master URL name MySQL database Directory hierarchy A set of daemon processes and cron jobs

32. Processes work generator validator assimilator feeder MySQL DB scheduler transitioner file deleter DB purger clients

33. Project directory hierarchy apps/application files bin/daemon programs cgi-bin/BOINC scheduler and upload GCI config.xmlconfiguration file download/downloadable files html/web site; master URL points here keys/keys for code signing, upload auth log_(hostname)daemon log files project.xmllist of platforms and apps upload/uploaded files

34. BOINC database platform app app_version user host workunit result...

35. Creating a project make_project name creates  directory hierarchy  DB  mods for httpd.conf  crontab entry

36. Project configuration and control config.xml  scheduling and other options  list of daemons  list of periodic tasks project control  bin/start: start daemons, enable scheduler  bin/stop: stop daemons, disable scheduler  bin/status

37. Scaling a BOINC server Components can run on different machines sharing a file system Each component can be distributed MySQL server is typically the bottleneck 1 server machine can issue ~100K jobs/day; 4 machines can issue > 1 million

38. Part 5: Deploying applications

39. Adding an application edit project.xml run bin/xadd multi_thread Test multi-thread apps

40. Adding an application version Create application version directory Sign files on offline computer run bin/update_versions apps/ uppercase/ uppercase_6.14_windows_intelx86__cuda.exe/ uppercase_6.14_windows_intelx86__cuda.exe graphics_app=uppercase_graphics_6.14_windows_intelx86.exe logo.jpg Helvetica.txf

41. Part 6: Submitting jobs

42. Describing job inputs Input template file 0 0 in 1 -cpu_time 60 446797000000000 279248000000000

43. Describing job outputs Output template file 5000000 out

44. Submitting a job Stage input files Submit job create_work –appname A –wu_name B –wu_template C –result_template D cp test_files/12ja04aa `bin/dir_hier_path 12ja04aa`

45. Part 7: Organizational issues

46. Single-scientist projects Need to: Port apps Get publicity interface with public maintain servers Not many research groups have the resources And it creates a lot of competing “brands”

47. Umbrella projects Example: IBM World Community Grid Project publicity web development sysadmin app porting

48. The Berkeley@home model A university has – scientists – a powerful “brand” – PR resources – IT infrastructure – lots of alumni (UCB: 500,000)

49. Hubs nanoHUB: “science portal” for nanoscience – social network + “app store” – sharing of ideas, data, software – computational portal HUBzero: generalization to other areas – currently ~20 hubs Integration of BOINC with HUBzero – each hub has a volunteer computing project