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Exa-Scale Volunteer Computing

David P. Anderson Space Sciences Laboratory U.C. Berkeley. Exa-Scale Volunteer Computing. Outline. Volunteer computing BOINC Applications Research directions. # processors. program runs too slow on PC. 1. multiple jobs. single job. High-throughput computing. cluster (batch).

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Exa-Scale Volunteer Computing

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

  2. Outline • Volunteer computing • BOINC • Applications • Research directions

  3. # processors program runs too slow on PC 1 multiple jobs single job High-throughput computing cluster (batch) High-performance computing 100 cluster (MPI) Grid 1000 Commercial cloud supercomputer Volunteer computing 10K-1M

  4. Volunteer computing • Early projects • 1997: GIMPS, distributed.net • 1999: SETI@home, Folding@home • Today • 50 projects • 500K volunteers • 900K computers • 10 PetaFLOPS

  5. The PetaFLOPS barrier • September 2007: Folding@home • January 2008: BOINC • June 2008: IBM Roadrunner

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

  7. 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

  8. BOINC: volunteers and projects projects volunteers LHC@home CPDN attachments WCG

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

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

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

  12. Client: job scheduling • Queue lots of jobs • to avoid starvation • for variety • Job scheduling • Round-robin time-slicing • Earliest deadline first

  13. 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 1 CPU 2 CPU 3 max min

  14. 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 A B CPU A

  15. 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

  16. 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

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

  18. Scheduler overview MySQL schedulers feeder share-memory job cache client

  19. 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

  20. 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

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

  22. 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

  23. Application types • Computing-intensive analysis of large data • Physical simulations • Genetic algorithms • GA-inspired optimization • Non-CPU-intensive • Internet study • distributed sensor network

  24. 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. Malariacontrol.net

  25. Climateprediction.net

  26. Einstein@home • Gravitational waves; gravitational pulsars

  27. SETI@home

  28. Milkyway@home

  29. GPUGRID.net

  30. AQUA@home • D-Wave Systems • Simulation of “adiabatic quantum algorithms” for binary quadratic optimization

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

  32. Quake Catcher Network

  33. Organizational models • Umbrella projects • Institutional • Lattice, VTU@home • Corporate • IBM World Community Grid • Community • AlmereGrid • Research community • MindModeling.org publicity web development sysadmin Project

  34. 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

  35. Characterizing hosts powered on available • 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 SETI@home. 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 21-23 2009, 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 1 2008. connected

  36. Studying server scheduling policies MySQL • 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. Simulator of a large, dynamic set of volunteer hosts feeder scheduler share-memory job cache

  37. 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 10-13 2007. 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.

  38. 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 8-10 2009.

  39. Using virtual machines hypervisor (VirtualBox, kQEMU,etc.) • 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 VM

  40. 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

  41. 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)

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

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