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

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 3 CPU 2 CPU 1 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 CPU A BA

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