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

Outline Volunteer computing BOINC Applications Research directions

High-throughput computing High-performance computing program runs too slow on PC cluster (MPI) supercomputer cluster (batch) Grid Commercial cloud Volunteer computing single job # processors multiple jobs 10K-1M

Volunteer computing Early projects – 1997: GIMPS, distributed.net – 1999: Today – 50 projects – 500K volunteers – 900K computers – 10 PetaFLOPS

The PetaFLOPS barrier September 2007: January 2008: BOINC June 2008: IBM Roadrunner

ExaFLOPS Current PetaFLOPS breakdown: Potential: ExaFLOPS by 2010 – 4M GPUs * 1 TFLOPS * 0.25 availability

BOINC Middleware for volunteer computing – client, server, web Based at UC Berkeley Space Sciences Lab Open source (LGPL) NSF-funded since

BOINC: volunteers and projects volunteers projects CPDN WCG attachments

The BOINC computing ecosystem Goals: Better research gets more computing power The public decides what’s better The world’s computing power Scientific research The public

BOINC software overview client apps screensaver GUI scheduler MySQL data server daemons volunteer host project server HTTP

Scheduler RPC Request: – hardware, software description – work requests (CPU, GPU) – completed jobs Reply – application descriptions – job descriptions

Client: job scheduling Queue lots of jobs – to avoid starvation – for 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 max min

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

Scheduling: server Possible outcomes of a job: – success – runs but returns wrong answer – doesn’t run, returns wrong answer (hacker) – crashes, client reports it – never hear from client again Job delay bounds Replicated computing – homogeneous replication

Server abstractions applications Win32 + NVIDIA Win64 Mac OS X app versions jobs instances Win32 N-core Win32

Scheduler overview MySQL feeder schedulers share-memory job cache client

How scheduler chooses app versions App versions have project-supplied “planning function” Inputs: – host description Outputs: – Whether host can run app version – Resource usage (#CPUs, #GPUs) – expected FLOPS

App version selection Call planning function for platform’s app versions Skip versions that use resources for which no work is being requested Use the version with highest expected FLOPS Repeat this when a resource request is satisfied

Anonymous platform mechanism The idea: volunteer supplies app versions. Why? – security – optimization – unsupported platforms

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-inspired optimization Non-CPU-intensive – Internet study – distributed sensor network

Malariacontrol.net Simulation models of the transmission dynamics and health effects of malaria are an important tool for malaria control. They can be used to determine optimal strategies for delivering mosquito nets, chemotherapy, or new vaccines which are currently under development and testing.

Climateprediction.net

Gravitational waves; gravitational pulsars

GPUGRID.net

D-Wave Systems Simulation of “adiabatic quantum algorithms” for binary quadratic optimization

Collatz Conjecture even N → N/2 odd N → 3N + 1 always goes to 1?

Quake Catcher Network

Organizational models Umbrella projects Institutional – Lattice, Corporate – IBM World Community Grid Community – AlmereGrid Research community – MindModeling.org Project publicity web development sysadmin

Volunteer computing research Goals (mutually incompatible) – maximize throughput – minimize makespan of job batches – minimize average time until credit – minimize network traffic – minimize server disk usage

Characterizing hosts What are good models? What are correlations with other characteristics? How to model churn? BOINC client is instrumented to log all this; have data from 200K hosts over 1 year Mining for Statistical Models of Availability in Large-Scale Distributed Systems: An Empirical Study of Bahman Javadi, Derrick Kondo, Jean-Marc Vincent, David P. Anderson. 17th Annual Meeting of the IEEE/ACM International Symposium on Modelling, Analysis and Simulation of Computer and Telecommunication Systems, Sept , London. On Correlated Availability in Internet-Distributed Systems. Derrick Kondo, Artur Andrzejak, and David P. Anderson. 9th IEEE/ACM International Conference on Grid Computing (Grid 2008), Tsukuba, Japan, Sept 29 - Oct powered on connected available

Studying server scheduling policies EmBOINC: BOINC project emulator Performance Prediction and Analysis of BOINC Projects: An Empirical Study with EmBOINC. Trilce Estrada, Michela Taufer, David Anderson. To appear, Journal of Grid Computing. EmBOINC: An Emulator for Performace Analysis of BOINC Projects. Trilce Estrada, Michela Taufer, Kevin Reed, David Anderson. 3rd Workshop on Desktop Grids and Volunteer Computing Systems (PCGrid 2009), May 29, 2009, Rome. MySQL feeder scheduler share-memory job cache Simulator of a large, dynamic set of volunteer hosts

Studying client scheduling policies BOINC client simulator – simulates a client connected to several projects – based on actual client code Performance Evaluation of Scheduling Policies for Volunteer Computing. Derrick Kondo, David P. Anderson and John McLeod VII. 3rd IEEE International Conference on e-Science and Grid Computing. Banagalore, India, December Local Scheduling for Volunteer Computing. David P. Anderson and John McLeod VII. Workshop on Large-Scale, Volatile Desktop Grids (PCGrid 2007) held in conjunction with the IEEE International Parallel & Distributed Processing Symposium (IPDPS), March 30, 2007, Long Beach.

Supporting distributed applications Volpex: Linda-like “dataspace” system – MPI layer – centralized implementation – fault tolerance, performance issues A Communication Framework for Fault-tolerant Parallel Execution. Nagarajan Kanna, Jaspal Subhlok, Edgar Gabriel, Eshwar Rohit and David Anderson. The 22nd International Workshop on Languages and Compilers for Parallel Computing, Newark, Delaware, Oct

Using virtual machines App version is VM wrapper + virtual machine image VM image may contain the client of a non- BOINC distributed batch system BOINC client VM wrapper hypervisor (VirtualBox, kQEMU,etc.) VM

Data-intensive computing Maintain large data set on clients – 10 years of radio telescope data – gene/protein data Compute against data set – MapReduce, other models

Volunteer motivation study Online survey correlated with participation data Survey is currently being designed Preliminary findings: – Talk is cheap: claimed motivations not supported by data – Team members contribute more – Contribution decreases over time (especially for non-team members)

Conclusion Volunteer computing: Exa-scale potential – GPUs are crucial BOINC: enabling technology Bottlenecks – organizational models – public awareness Lots of research opportunities