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david p anderson space sciences laboratory u c berkeley 22 oct 2009 n.
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BOINC The Year in Review PowerPoint Presentation
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BOINC The Year in Review

BOINC The Year in Review

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BOINC The Year in Review

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  1. David P. Anderson Space Sciences Laboratory U.C. Berkeley 22 Oct 2009 BOINCThe Year in Review

  2. Volunteer computing • Throughput is now 10 PetaFLOPS • mostly Folding@home • Volunteer population is constant • 330K BOINC, 200K F@h • Volunteer computing still unknown in • HPC world • scientific computing world • general public

  3. ExaFLOPS • Current PetaFLOPS breakdown: • Potential: ExaFLOPS by 2010 • 4M GPUs * 1 TFLOPS * 0.25 availability

  4. Projects • No significant new academic projects • but signs of life in Asia • No new umbrella projects • AQUA@home: D-Wave systems • Several hobbyist projects

  5. BOINC funding • Funded into 2011 • New NSF proposal

  6. Facebook apps • Progress thru Processors (Intel/GridRepublic) • Web-only registration process • lots of fans, not so many participants • BOINC Milestones • IBM WCG

  7. Research • Host characterization • Scheduling policy analysis • EmBOINC: project emulator • Distributed applications • Volpex • Apps in VMs • Volunteer motivation study

  8. Fundamental changes • App versions now have dynamically-determined processor usage attributes (#CPUs, #GPUs) • Server can have multiple app versions per (app, platform) pair • Client can have multiple versions per app • An issued job is linked to an app version

  9. Scheduler request • Old (CPU only) • requested # seconds • current queue length • New: for each resource type (CPU, NVIDIA, ...) • requested # seconds • current high-priority queue length • # of idle instances

  10. Schedule reply • Application versions include • resource usage (# CPUs, # GPUs) • FLOPS estimate • Jobs specify an app version • A given reply can include both CPU and GPU jobs for a given application

  11. Client: work fetch policy • When? From which project? How much? • Goals • maintain enough work • minimize scheduler requests • honor resource shares • per-project “debt” CPU 0 CPU 1 CPU 2 CPU 3 max min

  12. Work fetch for GPUs: goals • Queue work separately for different resource types • Resource shares apply to aggregate Example: projects A, B have same resource share A has CPU and GPU jobs, B has only GPU jobs GPU A B CPU A

  13. Work fetch for GPUs • For each resource type • per-project backoff • per-project debt • accumulate only while not backed off • A project’s overall debt is weighted average of resource debts • Get work from project with highest overall debt

  14. Client: job scheduling • GPU job scheduling • client allocates GPUs • GPU prefs • Multi-thread job scheduling • handle a mix of single-, multi-thread jobs • don’t overcommit CPUs

  15. GPU odds and ends • Default install is non-service • Dealing with sporadic usability • e.g. Remote Desktop • Multiple non-identical GPUs • GPUs and anonymous platform

  16. Other client changes • Proxy auto-detection • Exclusive app feature • Don’t write state file on each checkpoint

  17. Screensaver • Screensaver coordinator • configurable • New default screensaver • Intel screensaver

  18. Scheduler/feeder • Handle multiple app versions per platform • Handle requests for multiple resources • app selection • completion estimate, deadline check • Show specific messages to users • “no work because you need driver version N” • Project-customized job check • jobs need different # of GPU processors • Mixed locality and non-locality scheduling

  19. Server • Automated DB update • Protect admin web interface

  20. Manager • Terms of use feature • Show only projects supporting platform • need to extend for GPUs • Advanced view is keyboard navigable • Manager can read cookies (Firefox, IE) • web-only install

  21. Apps • Enhanced wrapper • checkpointing, fraction done • PyMW: master/worker Python system

  22. Community contributions • Pootle-based translation system • projects can use this • Testing • alpha test project • Packaging • Linux client, server packages • Programming • lots of flames, little code

  23. What didn’t get done • Replace runtime system • Installer: deal with “standby after X minutes” • Global shutdown switch

  24. Things on hold • BOINC on mobile devices • Replace Simple GUI

  25. Important things to do • New system for credit and runtime estimation • we have a design! • Keep track of GPU availability separately • Steer computers with GPUs towards projects with GPU apps • Sample CUDA app

  26. BOINC development • Let us know if you want something • If you make changes of general utility: • document them • add them to trunk