1. technology and computing

Volunteer Computing
David P. Anderson
Space Sciences Lab
U.C. Berkeley
May 7, 2008
Where’s the computing power?
Volunteer
computing
Individuals
(~1 billion PCs)
Government
(~50M PCs)
Companies
(~100M PCs)
A brief history of volunteer computing
1995
2000
2005
distributed.net, GIMPS
Projects
SETI@home, Folding@home
Climateprediction.net
IBM World Community Grid
Einstein, Rosetta@home
Platforms
Popular Power
Entropia
United Devices, Parabon
BOINC
The BOINC project
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Based at UC Berkeley Space Sciences Lab
Funded by NSF since 2002
Personnel
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What we do
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director: David Anderson
other employees: 1.5 programmers
lots of volunteers
develop open-source software
enable online communities
What we don’t do
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branding, hosting, authorizing, endorsing, controlling
The BOINC community
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Projects
Volunteer programmers
Alpha testers
Online Skype-based help
Translators (web, client)
Documentation (Wiki)
Teams
The BOINC model
Your PC
BOINC-based proje
Climateprediction.net
Oxford; climate study
Rosetta@home
U. of Washington; biology
MalariaControl.net
STI; malaria epidemiology
World Community Grid
IBM; several applications
...
Attachments
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Simple (but configurable)
Secure
Invisible
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Independent
No central authority
Unique ID: URL
The volunteer computing ecosystem
Projects
Public
Teach, motivate
volunteer
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Do more science
Involve public in science
Participation and computing power
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BOINC
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330K active participants
580K computers
~40 projects
1.2 PetaFLOPS average throughput
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about 3X an IBM Blue Gene L
Folding@home (non-BOINC)
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200K active participants
1.4 PetaFLOPS (mostly PS3)
Cost per TeraFLOPS-year
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Cluster: $124K
Amazon EC2: $1.75M
BOINC: $2K
The road to ExaFLOPS
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CPUs in PCs (desktop, laptop)
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GPUs
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.25 ExaFLOPS = 10M x 100GFLOPS x 0.25 avail
Mobile devices (cell phone, PDA, iPod, Kindle)
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1 ExaFLOPS = 4M x 1 TFLOPS x 0.25 avail.
Video-game consoles (PS3, Xbox)
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1 ExaFLOPS = 50M PCs x 80 GFLOPS x 0.25 avail.
.05 ExaFLOPS = 1B x 100MFLOPS x 0.5 avail
Home media (cable box, Blu-ray player)
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0.1 ExaFLOPS = 100M x 1 GFLOPS x 1.0 avail
But it’s not about numbers
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The real goals:
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And that means we must:
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enable new computational science
change the way resources are allocated
avoid return to the Dark Ages
make volunteer computing feasible for all scientists
involve the entire public, not just the geeks
solve the “project discovery” problem
Progress towards these goals: nonzero but small
BOINC server software
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Goals
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Clients
high performance (10M jobs/day)
scalability
scheduler
(CGI)
shared
memory
(~1K jobs)
feeder
MySQL DB
(~1M jobs)
Various
daemons
Database tables
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Application
Platform
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App version
Job
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resource usage estimates, bounds
latency bound
input file descriptions
Job instance
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Win32, Win64, Linux x86, Java, etc.
output file descriptions
Account, team, etc.
Data model
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Files
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have both logical and physical names
immutable (per physical name)
may originate on client or server
may be “sticky”
may be compressed in various ways
transferred via HTTP or BitTorrent
app files must be signed
Upload/download directory hierarchies
Submitting jobs
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Create XML description
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Put input files into dir hierarchy
Call create_work()
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input, output files
resource usage estimates, bounds
latency bound
creates DB record
Mass production
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bags of tasks
flow-controlled stream of tasks
self-propagating computations
trickle messages
Server scheduling policy
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Request message:
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platform(s)
description of hardware
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CPU, memory, disk, coprocessors
description of availability
current jobs queued and in progress
work request (CPU seconds)
Send a set of jobs that
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are feasible (will fit in memory/disk)
will probably get done by deadline
satisfy the work request
Multithread and coprocessor support
Application
platform
app
appversions
version
List of platforms,
Coprocessors
#CPUs
scheduler
client
jobs
avg/max #CPUs,
coprocessor usage
command line
app planning
function
job
Result validation
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Problem: can’t trust volunteers
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computational result
claimed credit
Approaches:
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Application-specific checking
Job replication
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do N copies, require that M of them agree
Adaptive replication
Spot-checking
How to compare results?
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Problem: numerical discrepancies
Stable problems: fuzzy comparison
Unstable problems
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Eliminate discrepancies
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compiler/flags/libraries
Homogeneous replication
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send instances only to numerically equivalent hosts
(equivalence may depend on app)
Server scheduling policy revisited
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Goals (possibly conflicting):
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Send retries to fast/reliable hosts
Send long jobs to fast hosts
Send demanding jobs (RAM, disk, etc.) to qualified
hosts
Send jobs already committed to a homogeneous
redundancy class
Project-defined “score” function
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scan N jobs, send those with highest scores
Server daemons
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Per application:
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Transitioner
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work generator
validator
assimilator
manages replication, creates job instances
triggers other daemons
File deleter
DB purger
Ways to create a BOINC server
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Install BOINC on a Linux box
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Run BOINC server VM (Vmware)
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lots of software dependencies
need to worry about hardware
Run BOINC server VM on Amazon EC2
BOINC API
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Typical application structure:
boinc_init()
loop
...
boinc_fraction_done(x)
if boinc_time_to_checkpoint()
write checkpoint file
boinc_checkpoint_completed()
boinc_finish(0)
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Graphics
Multi-program apps
Wrapper for legacy apps
Volunteer’s view
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1-click install
All platforms
Invisible, autonomic
Highly configurable (optional)
BOINC client structure
schedulers, data servers
screensaver
application
local TCP
BOINC library
core client
GUI
Runtime system
user preferences, control
Some BOINC projects
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Climateprediction.net
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Einstein@home
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LIGO scientific collaboration
gravitational wave detection
SETI@home
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Oxford University
Global climate modeling
U.C. Berkeley
Radio search for E.T.I. and black hole evaporation
Leiden Classical
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Leiden University
Surface chemistry using classical dynamics
More projects
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LHC@home
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QMC@home
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Univ. of Muenster
Quantum chemistry
Spinhenge@home
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CERN
simulator of LHC, collisions
Bielefeld Univ.
Study nanoscale magnetism
ABC@home
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Leiden Univ.
Number theory
Biomed-related BOINC projects
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Rosetta@home
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Tanpaku
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University of Washington
Rosetta: Protein folding, docking, and design
Tokyo Univ. of Science
Protein structure prediction using Brownian dynamics
MalariaControl
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The Swiss Tropical Institute
Epidemiological simulation
More projects
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Predictor@home
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SIMAP
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Tech. Univ. of Munich
Protein similarity matrix
Superlink@Technion
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Univ. of Michigan
CHARMM, protein structure prediction
Technion
Genetic linkage analysis using Bayesian networks
Quake Catcher Network
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Stanford
Distributed seismograph
More projects (IBM WCG)
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Dengue fever drug discovery
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Human Proteome Folding
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U. of Texas, U. of Chicago
Autodock
New York University
Rosetta
FightAIDS@home
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Scripps Institute
Autodock
Organizational models
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Single-scientist projects: a dead-end?
Campus-level meta-project
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UC Berkeley:
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Lattice
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~8 applications from various institutions
Extremadura (Spain)
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ACT-R community (~20 universities)
IBM World Community Grid
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U. Maryland Center for Bioinformatics
MindModeling.org
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1,000 instructional PCs
5,000 faculty/staff
30,000 students
400,000 alumni
consortium of 5-10 universities
SZTAKI (Hungary)
Conclusion
Volunteer
computing
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Individuals
(~1 billion PCs)
Contact me about:
Using BOINC
 Research based on
BOINC
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Companies
(~100M PCs)
Government
(~50M PCs)
[email protected]