Exa-Scale Volunteer Computing David P. Anderson Space Sciences Laboratory U.C. Berkeley.

Exa-Scale Volunteer Computing

David P. Anderson

Space Sciences LaboratoryU.C. Berkeley

Outline

• Volunteer computing

• BOINC

• Applications

• Research directions

High-throughputcomputing

High-performancecomputing

program runstoo slow on PC

cluster(MPI)

supercomputer

cluster(batch)

Grid

Commercialcloud

Volunteercomputing

single job

# processors

multiple jobs

10K-1M

1000

100

1

Volunteer computing

• Early projects

– 1997: GIMPS, distributed.net

– 1999: SETI@home, Folding@home

• Today

– ~50 projects

– 500K volunteers

– 1M computers, 2.4M cores

– 10 PetaFLOPS

The potential of volunteer computing

• The volunteer resource pool• Current PetaFLOPS breakdown:

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

Processor type0

0.51

1.52

2.53

3.54

4.55

4.6

2.4 2.2

1.2

NVIDIA

CPU

PS3 (Cell)

ATI

BOINC

• Middleware for volunteer computing

– client, server, web

• Based at UC Berkeley Space Sciences Lab

• Open source (LGPL)

• NSF-funded since 2002

• http://boinc.berkeley.edu

BOINC: volunteers and projects

volunteers projects

CPDN

LHC@home

WCGattachments

The Utopian vision

• Better research gets more computing power

• An enlightened public decides what’s better

ScientificresearchThe public

resources

education/outreach

Science areas using BOINC• Biology

– protein study, genetic analysis• Medicine

– drug discovery, epidemiology• Physics

– LHC, nanotechnology, quantum computing• Astronomy

– data analysis, cosmology, galactic modeling• Environment

– climate modeling, ecosystem simulation• Math• Graphics rendering

Application types

• Computing-intensive analysis of large data

• Physical simulations

• Genetic algorithms

– GA-based optimization

Climateprediction.net

Einstein@home

• Gravitational waves; gravitational pulsars

SETI@home

Milkyway@home

GPUGRID.net

AQUA@home

• D-Wave Systems

• Simulation of “adiabatic quantum algorithms” for binary quadratic optimization

Quake Catcher Network

Account managers

BOINC software overview

client

apps

screensaver

GUI

scheduler

MySQL

data server

daemons

volunteer host

project serverHTTP

Client: job scheduling

• Queue multiple jobs

– avoid starvation

– minimize communication

– variety

• Job scheduling

– Round-robin time-slicing

– Earliest deadline first

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

BOINC schedulerapplications

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

Anonymous platform mechanism

• Volunteer supplies app versions.

– security

– optimization

– unsupported platforms

Umbrella projects

Example: IBM World Community Grid

Projectpublicityweb developmentsysadminapp 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

Volunteer computing research

• Host characterization• Simulation-based performance study• MPI-type apps• Apps in VMs• Data-intensive computing• Volunteer motivation

Conclusion

• Volunteer computing: Exa-scale potential– GPUs are crucial

• BOINC: enabling technology

• Bottlenecks

– the culture of scientific computing

– organizational models