Volunteer Computing with BOINC
Dr. David P. Anderson University of California, Berkeley
SC10 Nov. 14, 2010
Goals
Explain volunteer computing Teach how to create a volunteer computing
project using BOINC
Target audience: High-throughput computing users Technical skills:
Basic Linux/Apache sysadmin, familiarity with PHP, SQL and XML, C/C++ (optional)
Outline Why use volunteer computing? Basic concepts of BOINC Developing BOINC applications (15 minute break) Deploying a BOINC server Deploying applications Submitting jobs Organizational issues
Part 1:
Why use volunteer computing?
The Consumer Digital Infrastructure
1 billion PCs current GPUs: 1 TeraFLOPS (1,000 ExaFLOPS
total) Storage: ~1,000 Exabytes
Commodity Internet: 10-1,000 Mbps to home Consumers pay for
hardware sysadmin network costs electricity
Volunteer computing
PC owners donate computing resources to projects (e.g., computational science)
Applications run at zero priority while PC in use, and/or while PC is not in use
Examples Project start where area peak #hosts
GIMPS 1994 math 10,000 distributed.net 1995 cryptography 100,000 SETI@home I 1999 UCB SETI 600,000 Folding@home 1999 Stanford biology 200,000 United Devices 2002 commercial biomedicine 200,000 CPDN 2003 Oxford climate change 150,000 LHC@home 2004 CERN physics 60,000 Predictor@home 2004 Scripps biology 100,000 WCG 2004 commercial biomedicine 200,000 Einstein@home 2005 LIGO astrophysics 200,000 SETI@home II 2005 UCB SETI 850,000 Rosetta@home 2005 U. Wash biology 100,000 SIMAP 2005 T.U. Munich bioinformatics 10,000 ... ... ... ... ...
Current status
~50 projects 500,000 vounteers 800,000 computers
High-throughput computing
High-performance computing
cluster (MPI)
supercomputer
cluster (batch)
Grid
Commercial cloud
Volunteer computing
single job
# processors
multiple jobs
10K-1M
1000
100
1
Volunteer computing is different
You don’t buy resources; you ask for them Resources are:
- heterogeneous - sporadically available and connected - untrusted and not private - behind firewalls/NATs/proxies
Part 2:
Basic concepts of BOINC
About BOINC
Funded by NSF since 2002 Open-source (LGPL) Based at UC Berkeley Few staff, but lots of volunteers
software testing translation documentation support (email lists, message boards, Skype)
Volunteers and projects
volunteers projects
CPDN
LHC@home
WCG attachments
BOINC software overview
client
apps
screensaver
GUI
scheduler MySQL
data server
daemons
volunteer host
project server HTTP
BOINC scheduler applications
Win32 + NVIDIA
Win64
Mac OS X
app versions
jobs
instances
Win32 N-core
Win32 - HW, SW description - existing workload - per resource type: # of instances requested # of seconds requested
- app version descriptions - job descriptions
Job replication
Job instances may fail or return wrong results Job replication: do 2, see if they agree
- “agree” may be fuzzy
Homogeneous replication - numerical equivalence of hosts
Adaptive replication - reduce replication for hosts that seem
trustworthy
The job pipeline
work generator
BOINC
validator
assimilator
The BOINC data model
App versions, job inputs, job output can consist of arbitrarily many files
Each file has a physical name (unique, immutable); each reference to a file has a “logical name”
Files have various attributes (e.g., sticky) Each file can have one or more URLs, and are
transferred via HTTP App version files are digitally signed
What kinds of jobs can BOINC handle?
Pretty much anything you’d run on a Grid Bag of tasks (but IPC support soon) Short/long jobs Data intensive, up to a point Geared towards
- Few apps, many jobs (high startup cost per app)
- Jobs with high slack time
Part 3:
Application development for BOINC
The BOINC runtime environment processes
files
Native BOINC applications
boinc_init() - create runtime system thread
boinc_finish() - write finish file
boinc_resolve_filename(logical, physical) boinc_fraction_done(x)
Checkpointing
bool boinc_time_to_checkpoint() - call when in checkpointable state
boinc_checkpoint_done()
The BOINC wrapper
Can use for legacy apps XML input file lists sub-jobs
- executable, input files
What it does: - interfaces to BOINC client - copies files to/from slot directory - runs executables - does checkpointing at sub-job level
Building app versions
Linux - gcc
Windows - Visual Studio - minGW (gcc)
Mac OS X - xcode
Multithread apps
boinc_init_parallel() Allows suspend/resume of all threads
- Unix: fork/exec - Windows: direct thread control
GPU app versions
Develop for NVIDIA or ATI, with CUDA, CAL, OpenCL, etc. (BOINC supplies samples)
Each version has a “plan class” For each plan class, supply a function that
determines - can app run on this host?
hardware, driver version, etc. - what resources will it use?
#CPUs, #GPUs, GPU RAM, etc.
VM apps
Develop apps on your favorite OS Create a VirtualBox VM image App version consists of
- VM wrapper (supplied by BOINC) - VM image - app executable
Part 4:
Deploying a BOINC server
Hardware options
Native Linux host - download/compile BOINC software
BOINC server VM (VMware/Debian) BOINC Amazon EC2 image
Components of a project
Master URL name MySQL database Directory hierarchy A set of daemon processes and cron jobs
Processes
work generator
validator
assimilator feeder
MySQL DB
scheduler
transitioner
file deleter
DB purger
clients
Project directory hierarchy apps/ application files
bin/ daemon programs
cgi-bin/ BOINC scheduler and upload GCI
config.xml configuration file
download/ downloadable files
html/ web site; master URL points here
keys/ keys for code signing, upload auth
log_(hostname) daemon log files
project.xml list of platforms and apps
upload/ uploaded files
BOINC database platform app app_version user host workunit result ...
Creating a project
make_project name
creates - directory hierarchy - DB - mods for httpd.conf - crontab entry
Project configuration and control
config.xml - scheduling and other options - list of daemons - list of periodic tasks
project control - bin/start: start daemons, enable scheduler - bin/stop: stop daemons, disable scheduler - bin/status
Scaling a BOINC server
Components can run on different machines sharing a file system
Each component can be distributed MySQL server is typically the bottleneck 1 server machine can issue ~100K jobs/day; 4
machines can issue > 1 million
Part 5:
Deploying applications
Adding an application
edit project.xml
run bin/xadd
<app> <name>multi_thread</name> <user_friendly_name>Test multi-thread apps</user_friendly_name> </app>
Adding an application version
Create application version directory
Sign files on offline computer run bin/update_versions
apps/ uppercase/ uppercase_6.14_windows_intelx86__cuda.exe/ uppercase_6.14_windows_intelx86__cuda.exe graphics_app=uppercase_graphics_6.14_windows_intelx86.exe logo.jpg Helvetica.txf
Part 6:
Submitting jobs
Describing job inputs Input template file <file_info> <number>0</number> </file_info> <workunit> <file_ref> <file_number>0</file_number> <open_name>in</open_name> </file_ref> <target_nresults>1</target_nresults> <min_quorum>1</min_quorum> <command_line>-cpu_time 60</command_line> <rsc_fpops_bound>446797000000000</rsc_fpops_bound> <rsc_fpops_est>279248000000000</rsc_fpops_est> </workunit>
Describing job outputs Output template file
<file_info> <name><OUTFILE_0/></name> <generated_locally/> <upload_when_present/> <max_nbytes>5000000</max_nbytes> <url><UPLOAD_URL/></url> </file_info> <result> <file_ref> <file_name><OUTFILE_0/></file_name> <open_name>out</open_name> </file_ref> </result>
Submitting a job
Stage input files
Submit job create_work –appname A –wu_name B –wu_template C –result_template D
cp test_files/12ja04aa `bin/dir_hier_path 12ja04aa`
Part 7:
Organizational issues
Single-scientist projects
Need to: Port apps Get publicity interface with public maintain servers
Not many research groups have the resources And it creates a lot of competing “brands”
Umbrella projects
Example: IBM World Community Grid
Project publicity web development sysadmin app porting
The Berkeley@home model
• A university has – scientists – a powerful “brand” – PR resources – IT infrastructure – lots of alumni (UCB: 500,000)
Hubs • nanoHUB: “science portal” for nanoscience
– social network + “app store” – sharing of ideas, data, software – computational portal
• HUBzero: generalization to other areas – currently ~20 hubs
• Integration of BOINC with HUBzero – each hub has a volunteer computing project
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