Parallel computing on the grid: Experiences from computational finance

1. Ian STOKES-REES INRIA Sophia-Antipolis France Parallel computing on the grid: Experiences from computational finance

2. Outline • Reminder: Grid Vision • Grid Computing Strategies • Parallel Application Development on the Grid • ProActive • PicsouGrid Project

3. Reminder: Grid Vision • Federated • Large Scale • Heterogeneous • Collaborative • Dynamic • Globally distributed

4. Strategy 1: Infrastructure-level Grid • Fire-and-forget non-interactive “tasks” • Queued individually • Run individually • Results collected and collated at a later date • Example Problems: particle physics computing: reconstruction, Monte Carlo simulation • Example Systems: EGEE/WLCG, NGS, TerraGrid, OSG, Grid5000 • Users: have traditional computing tasks, with no “grid” in them, and just need CPUs to run them on

5. Strategy 2: Application-level Grid • Builds on infrastructure grid resources • Provides complete application • Grid interface built into application • Or application is only way to access underlying Grid • Example Applications: eDiaMoND, MyGrid, UNICORE • Users: Specific to the application, but are “end users”. Typically don’t expect to “download and install” software. Rather, use “grid application” designed for their specific needs.

6. Strategy 3: Library-level grid • Single-system application linked-in with grid library • (semi-) transparently handles application deployment and execution across grid resources • Developers look after “grid” issues either directly or via Library APIs/functionality. • Varying levels of transparency in current offerings • Example Libraries: Globus, OMII services, gLite (perhaps not yet), ProActive, MPICH-G2 (Globus MPI), GridMPI • Users: Software developers who want to leverage grid computing in their applications.

7. Parallel Application Development on the Grid • Big grid resources are out there (WLCG, NGS, OSG, Grid5000) • Managed by other people (great!) • Not always possible to install individually on each system and monitor/tweak operation (not so great!) • Remember “Grid Vision”: • Heterogeous • Dynamic • Federated • How to develop parallel algorithms/applications for distributed, heterogenous systems?

8. Parallel Application Development on the Grid (II) • Synchronisation is difficult (obviously) • Distributed logging is difficult • Distributed debugging is really difficult • Requires a slightly different development paradigm: • Granularity of computation needs to be more coarse • Asynchrony is important to avoid blocking • Simplicity is important to aid debugging and reduce sources of error

9. ProActive: Value Proposition • Java VM to reduce/eliminate hardware and software heterogeneity • Forces use of Java everywhere • Doesn’t hide performance differences! • Benefit from reflection and dynamic class loading • Wrap objects in “gridified” sub-class • Provide asynchrony and multi-threading through “Active Objects” • Futures • Wait-by-necessity • And other features auto-magically added either by developers or at run-time via Active Object factory onto wrapped classes.

10. Active Objects • Deterministic, multi-threaded, distributed inter-object communication, without a priori knowledge of object deployment. Sequential Multithreaded Distributed

11. Futures and Wait-by-necessity • Method calls on Active Objects: • Asynchronous • Implicit Futures as RMI result • Wait-By-Necessity: • Automatic wait upon the use of an implicit future • First-Class Futures: • Futures passed to other activities • Sending a future is not blocking

12. Creating Active Objects MyClass obj_norm = new MyClass(<params>); MyClass obj_act = newActive(“MyClass”, <params>); Result r1 = obj_norm.foo(param); Result r2 = obj_act.foo(param); Result r3 = obj_act.bar(param); //... r2.bar(); //Wait-By-Necessity • In other words, very little effort by developer to introduce distributed multi-threading into application.

13. 3 Active Object Internals Standard object • An active object is composed of several objects : • The object being activated: Active Object • A single thread • The queue of pending requests • A set of standard Java objects 1 Objet Active object Proxy Object 1 2 Body

14. And lots of other nice features… • P2P interface • File sharing/distribution • Security • Typed group communication (OOSPMD) • Graphical Distributed Monitoring/Debugging • IC2D application • Timing and performance API (TimIT) • Object migration • Load balancing • Fault Tolerance/Check-pointing • Run time deployment configuration (clusters/nodes) • Component model • Plus lots of docs, APIs, tutorials, examples, etc.

15. Check it out • Web: http://proactive.objectweb.org • Email: Ian.Stokes-Rees@inria.fr • Or, ask me more about it at the pub • BTW, group has spin-off company coming this summer…

16. PicsouGrid: Computational Finance on the Grid • What? • Option pricing • Why? • Surprisingly, not done much in a grid domain • Not many openly available implementations (parallel or not) • How? • ProActive (i.e. Java) on Grid5000 and other grids (WLCG, NGS, DAS-3, …)

17. High Level Project Objectives • Framework for distributed computational finance algorithms • Investigate grid component model • http://gridcomp.ercim.org/ • Implement open source versions of parallel algorithms for computational finance • Utilise ProActive grid middleware • Deploy and evaluate on various grid platforms • Grid5000 (France) • DAS3 (Netherlands) • EGEE (Europe)

18. Grid Emphasis • This presentation and subsequent paper focuses on developing an architecture for parallel grid computing with: • Multi site (5+) • Large scale (500-2000 cores) • Long term (days to weeks) • Multi-grid (2+) • Consequently, de-emphasizes computational finance-specific aspects (i.e. algorithms and application domain) • However other team members are working hard on this!

19. ProActive http://www.objectweb.org/proactive • Java Library for Distributed Computing • Developed by INRIA Sophia Antipolis, France (Project OASIS) • 50-100 person-years R&D work invested • Provides transparent asynchronous distributed method calls • Implemented on top of Java RMI • Fully documented (600 page manual) • Available under LGPL • Used in commercial applications • Graphical debugger

20. ProActive (II) • OO SPMD with “Active Objects” • Any Java Object can automatically be turned into an “Active Object” • Utilises Java Reflection • “Wait by necessity” and “futures” allow method calls to return immediately and then subsequent object access blocks until result is ready • Objects appear local but may be deployed on any system within ProActive environment (local system/cluster, or remote system, cluster, or grid) • Easy Integration with Existing Systems • Extensions seamlessly support various cluster, network, and grid environments: Globus, ssh, http(s), LSF, PBS, SGE, EGEE, Grid5000

21. Background – Options • Option trading: financial instruments which allow buyers to bet on future asset prices and sellers to reduce risk of owning asset • Call option: allows holder to purchase an asset at a fixed price in the future • Put option: allows holder to sell an asset at a fixed price in the future • Option Pricing: • European: fixed future exercise date • American: can be exercised any time up to expiry date • Basket: prices a set of options together • Barrier: exercise depends on a certain barrier price being reached • Uses Monte Carlo simulations • Possibility to aggregate statistical results

22. Background – PicsouGrid v1,2,3 • Original versions of PicsouGrid utilised: • Grid5000 • ProActive • JavaSpaces • Implemented • European Simple, Basket, and Barrier Pricing • Medium-size distributed system: 4 sites, 180 nodes • Short operational runs (5-10 minutes) • Fault Tolerance mechanisms • Achieved 90x speed-up with 140 systems • 65% efficiency • Reported in e-Science 2006 (Amsterdam, Nov 2006) • A Fault Tolerant and Multi-Paradigm Grid Architecture for Time Constrained Problems. Application to Option Pricing in Finance.

23. PicsouGrid v3 Performance Multi-site Peak speed-up Performance degradation

24. PicsouGrid Architecture • Server/Control Node • Provides User Interface • Instantiates network of Sub-Servers • Allows configuration of Simulator network • Creates “Request for Option Price” (with algorithm parameters) • Controls Sub-Servers and aggregates/reports results • Monitors Sub-Servers for failures and spawns new Sub-Servers if necessary • Sub-Server • Acts as local site/cluster/system controller • Instantiates local Simulators • Delegates simulations in packets to Simulators • Collects results, aggregates, and returns to Server • Monitors Simulators for failures and spawns new Simulators if necessary • Simulator • Computes Monte Carlo simulations for option pricing using packets

25. option pricing request Worker Sub-Server reserve workers ProActive MC simulation packet heartbeat monitor MC result Sub-Server Worker ProActive ProActive Server Client ProActive JavaSpace virtual shared memory (to v3) DB PicsouGrid Deployment and Operation

26. PicsouGrid v5 Design Objectives • Multi-Grid • Grid5000 • gLite/EGEE • INRIA Sophia desktop cluster • Decoupled Workers • Autonomous • Independent deployment and operation • P2P discover and acquire • Long Running, Multi-Algorithm • Create “standing” application • Augment (or reduce) P2P worker network based on demand • Computational tasks specify algorithm and parameters

27. Grid Performance Monitoring and State Machines • Grid-ified distributed applications add at least three new layers of complexity compared to serial counterpart: • Grid interaction and management • Local cluster interaction and management • Distributed application code • Notoriously difficult to figure out what is going on where and when it is happening: • Bottlenecks • Hot spots • Idle time • Limiting factor: CPU, storage, network? • What state is an application/task/process/system currently in? • Solution: Utilise a common state machine model for grid applications/processes

28. Layered System Grid Site Cluster Host Core VM Process

29. “Proof” of layering • What I execute on a Grid5000 Submit (UI) Node: • mysub -l nodes=30 es-bench1e6 • What eventually runs on Worker Node: • /bin/sh -c /usr/lib/oar/oarexecuser.sh /tmp/OAR_59658 30 59658 istokes-rees \/bin/bash ~/proc/fgrillon1.nancy.grid5000.fr/submit N script-wrapper \~/bin/script-wrapper fgrillon1.nancy.grid5000.fr \~/es-bench1e6 • Granted, this is nothing more than good system design and separation of concerns • We are just looking at the implicit API layers of “the grid” • Universal interface: command shell, environment variables and file system

30. Abstract Recursive Process Model • Question: Is it possible to propose a recursive process model which can be applied at all layers? • Create – process description • Bind – process to the physical layer • Prepare – prepare for execution (software, stage in, config) • Execute – initiate process execution (enter next lower layer) • Complete – book keeping, stage out, clean up • Clear – wipe system, ready for next invocation • Each stage can be in a particular state: • Ready • Active • Done

31. Ready Ready Active Active Done Done Bind Create Grid Process State Machine Fail Cancel System Suspend User Pause Ready Ready Ready Ready Active Active Active Active Done Done Done Done Clear Prepare Execute Complete Create process description Bind to a particular system Prepare system to execute process Execute process (recurse to next lower level) Tidy up system and accounting after completion of process Clear process from system

32. CREAM Job States Create Bind • New LCG/EGEE Workload Management System • Can be mapped to Grid Process State Machine • This only shows one level of mapping • In practice, would apply state machine at Grid level, LRMS level, and task level • Timestamps on state entry: • Layer.Stage.State Prepare Suspend Execute Failed Cancelled Done Failed

33. Grid5000 Stats Lille • 9 Sites across France • 21 Clusters • 17 Batch systems • 3138 cores • Xeons • Opterons • Itaniums • G5 Nancy Paris-Orsay Rennes Lyon Bordeaux Grenoble Toulouse Sophia

34. Characteristics of Grid5000 • Private network • Outbound Internet access possibly via ssh tunnel • Access based on ssh keys (passwordless) • Shared NFS file space at each site • Very limited data management facilities • Myrinet and Infiniband prevalent on many clusters • RENATER French research network, 2.5 to 10 Gb/s inter-site • Focus on multi-node (and multi-site) grid computing • Kadeploy provides mechanism for custom system image to be loaded before job starts Grid5000 site

35. Deployment and Execution on Grid5000 • Limited grid-wide (cross-site) job submission mechanisms • In practice, submit individually at each site • Coordinate between sites via multiple “reservation” job submissions with same reservation window • Limited data-management/staging/configuration • Kadeploy (often too “heavy weight”) • rsync • Configuration wrapper scripts • Node count reservations “best effort” • Rule of thumb: don’t expect more than 80% of requested nodes to be available when reservation starts • Experience shows reservation start times could be delayed 30 seconds to 10 minutes

36. Experimental Setup • European Simple call/put option price • 1e6 Monte Carlo iterations • Single asset pricing reference: • treference = 67.3 seconds • AMD Opteron 2218 (64 bit) 2.6 GHz 1 MB L1 667 MHz bus (best performing core available) • Objective 1: maximize number of options priced in a fixed time window • Objective 2: maximize speed-up efficiency: (noptions  treference) sites(ncores_itreservation_i)

37. “Run Now” Experiment • Make immediate request for maximum number of nodes on all Grid5000 clusters • Price one option per acquired core • Not really fair: Grid5000 is not a production grid • Submit to 15 clusters • 8 clusters at 6 sites completed tasks within 6 hours • Remainder either failed or hadn’t started 24 hours later • 1272 cores utilised • 85 core-hours occupied • This is the total amount of time the tasks “held” a particular core: idle time + execution time • Objective 1(alt): 1272 options priced in “8 minute window” • Objective 2: 1272 options  67.3 s / 85 hr = 28% efficient • Discovered various grid issues (e.g. NTP, rsync)

38. Queuing Queuing Queuing Execution Queuing Result stage-out

39. When everything is working

40. NTP Problems (Time Sync)

41. Unexplained slow downs (homogeneous cluster)

42. Erratic node/core startup

43. Coordinated Start with Reservation • Reservation made 12+ hours in advance • Confirmed no other reservations for time slot • Start time at “low utilisation” point of 6:05am • 5 minutes provided for system restarts and Kadeploy re-imaging after end of reservations going to 6am • Submitted to 12 clusters, at 8 sites • 9 clusters at 7 sites ran successfully • 894 cores utilised • 31.3 core-hours occupied • No cluster reservation started “on time” • Start time delays of 20s to 5.5 minutes • Illustrates difficulty of cross-site coordinated parallel processing • Objective 1: 894 options priced in 9.5 minute window • Objective 2: 894 options  67.3 s / 31.3 hr = 53.4% efficient • Still problems (heterogeneous clusters, NTP, rsync)

45. Intra-node timing variations

46. Core Timeline (detail) • May seem like splitting hairs, but this is important for parallel algorithms with regular communication and synchronisation points • Also, to know where latencies/inefficiencies are introduced

47. Heterogeneous clusters (hyper threading on)

48. Mis-configured timezone

49. Overall cluster benchmarks

50. Parallelism • American option pricing with “floating” exercise date is much more difficult to calculate • Two algorithms with good opportunities for parallelism are available: • Longstaff-Schwartz (2001) • Ibanez-Zapetero (2002) • Interesting to see what speed up can be achieved by parallel implementation • Interested in possibility of cross-site parallel computation utilising ProActive