BOINCThe Year in Review

David P. Anderson

Space Sciences LaboratoryU.C. Berkeley

22 Oct 2009

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

ExaFLOPS

• Current PetaFLOPS breakdown:

• Potential: ExaFLOPS by 2010– 4M GPUs * 1 TFLOPS * 0.25 availability

Processor type0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5 4.6

2.42.2

1.2

NVIDIACPUPS3 (Cell)ATI

Projects

• No significant new academic projects

– but signs of life in Asia

• No new umbrella projects

• AQUA@home: D-Wave systems

• Several hobbyist projects

BOINC funding

• Funded into 2011

• New NSF proposal

Facebook apps

• Progress thru Processors (Intel/GridRepublic)

– Web-only registration process

– lots of fans, not so many participants

• BOINC Milestones

• IBM WCG

Research

• Host characterization

• Scheduling policy analysis

– EmBOINC: project emulator

• Distributed applications

– Volpex

• Apps in VMs

• Volunteer motivation study

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

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

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

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

maxmin

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

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

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

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

Other client changes

• Proxy auto-detection

• Exclusive app feature

• Don’t write state file on each checkpoint

Screensaver

• Screensaver coordinator

– configurable

• New default screensaver

• Intel screensaver

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

Server

• Automated DB update

• Protect admin web interface

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

Apps

• Enhanced wrapper

– checkpointing, fraction done

• PyMW: master/worker Python system

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

What didn’t get done

• Replace runtime system

• Installer: deal with “standby after X minutes”

• Global shutdown switch

Things on hold

• BOINC on mobile devices

• Replace Simple GUI

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

BOINC development

• Let us know if you want something

• If you make changes of general utility:

– document them

– add them to trunk