Transcript boinc.berkeley.edu

BOINC
The Year in Review
David P. Anderson
Space Sciences Laboratory
U.C. Berkeley
22 Oct 2009
Volunteer computing
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Throughput is now 10 PetaFLOPS
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Volunteer population is constant
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mostly Folding@home
330K BOINC, 200K F@h
Volunteer computing still unknown in
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HPC world
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scientific computing world
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general public
ExaFLOPS
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Current PetaFLOPS breakdown:
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Potential: ExaFLOPS by 2010
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4M GPUs * 1 TFLOPS * 0.25 availability
Projects
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No significant new academic projects
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but signs of life in Asia
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No new umbrella projects
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AQUA@home: D-Wave systems
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Several hobbyist projects
BOINC funding
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Funded into 2011
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New NSF proposal
Facebook apps
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Progress thru Processors (Intel/GridRepublic)
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Web-only registration process
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lots of fans, not so many participants
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BOINC Milestones
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IBM WCG
Research
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Host characterization
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Scheduling policy analysis
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EmBOINC: project emulator
Distributed applications
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Volpex
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Apps in VMs
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Volunteer motivation study
Fundamental changes
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App versions now have dynamically-determined
processor usage attributes (#CPUs, #GPUs)
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Server can have multiple app versions per (app,
platform) pair
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Client can have multiple versions per app
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An issued job is linked to an app version
Scheduler request
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Old (CPU only)
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requested # seconds
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current queue length
New: for each resource type (CPU, NVIDIA, ...)
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requested # seconds
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current high-priority queue length
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# of idle instances
Schedule reply
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Application versions include
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resource usage (# CPUs, # GPUs)
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FLOPS estimate
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Jobs specify an app version
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A given reply can include both CPU and GPU
jobs for a given application
Client: work fetch policy
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When? From which project? How much?
Goals
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maintain enough work
minimize scheduler requests
honor resource shares
CPU 0
CPU 1
CPU 2
CPU 3
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min
per-project “debt”
max
Work fetch for GPUs: goals
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Queue work separately for different resource
types
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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
B
A
Work fetch for GPUs
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For each resource type
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per-project backoff
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per-project debt
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accumulate only while not backed off
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A project’s overall debt is weighted average of
resource debts
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Get work from project with highest overall debt
Client: job scheduling
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GPU job scheduling
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client allocates GPUs
GPU prefs
Multi-thread job scheduling
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handle a mix of single-, multi-thread jobs
don’t overcommit CPUs
GPU odds and ends
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Default install is non-service
Dealing with sporadic usability
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e.g. Remote Desktop
Multiple non-identical GPUs
GPUs and anonymous platform
Other client changes
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Proxy auto-detection
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Exclusive app feature
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Don’t write state file on each checkpoint
Screensaver
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Screensaver coordinator
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configurable
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New default screensaver
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Intel screensaver
Scheduler/feeder
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Handle multiple app versions per platform
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Handle requests for multiple resources
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app selection
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completion estimate, deadline check
Show specific messages to users
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Project-customized job check
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“no work because you need driver version N”
jobs need different # of GPU processors
Mixed locality and non-locality scheduling
Server
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Automated DB update
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Protect admin web interface
Manager
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Terms of use feature
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Show only projects supporting platform
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need to extend for GPUs
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Advanced view is keyboard navigable
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Manager can read cookies (Firefox, IE)
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web-only install
Apps
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Enhanced wrapper
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checkpointing, fraction done
PyMW: master/worker Python system
Community contributions
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Pootle-based translation system
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Testing
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alpha test project
Packaging
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projects can use this
Linux client, server packages
Programming
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lots of flames, little code
What didn’t get done
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Replace runtime system
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Installer: deal with “standby after X minutes”
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Global shutdown switch
Things on hold
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BOINC on mobile devices
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Replace Simple GUI
Important things to do
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New system for credit and runtime estimation
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we have a design!
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Keep track of GPU availability separately
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Steer computers with GPUs towards projects
with GPU apps
Sample CUDA app
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BOINC development
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Let us know if you want something
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If you make changes of general utility:
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document them
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add them to trunk