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Grid Computing in Hong Kong

Grid Computing in Hong Kong. Dr. Cho-Li Wang Systems Research Group Department of Computer Science and Information Systems The University of Hong Kong. Agenda. Grid computing – a simple picture The Hong Kong Grid SRG Projects SLIM, ODGPC G-JavaMPI JESSICA2 LOTS DSM for Grid

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Grid Computing in Hong Kong

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  1. Grid Computing in Hong Kong Dr. Cho-Li Wang Systems Research Group Department of Computer Science and Information Systems The University of Hong Kong

  2. Agenda • Grid computing – a simple picture • The Hong Kong Grid • SRG Projects • SLIM, ODGPC • G-JavaMPI • JESSICA2 • LOTS DSM for Grid • Summary and Conclusion

  3. Much like “utilities” in our daily lives – electricity, water, etc. Advantages: Cost-effectiveness Platform extensibility Convenience (P&P) Grid Computing : A Simple Picture CPU power, Memory, Network, Storage… Data.. Services.. Resource providers Grid Computing Access to remote resources via standard protocols for cross-domain collaboration End users

  4. Grid Computing in Hong Kong -- The Hong Kong Grid The experimental grid in HK Supported under HKU Foundation Seed Grant http://www.hkgrid.org/

  5. The Hong Kong Grid (HKGrid) • Goals: • to construct and make available a grid test bed to facilitate the development of grid middleware and applications by local industry and institutions in Hong Kong and their partners in the region • to demonstrate the benefits of adopting grid technologies and to showcase any outstanding results of development or application • HKGrid provides a platform for its members to experiment with various research prototypes and pilot applications

  6. HKGrid - Current constituents A 4 Tflop/s theoretical maximum computing power

  7. Grid Point Monitoring with Ganglia URL: http://gideon.csis.hku.hk/status/

  8. HKU Grid Point: Grid and Cluster Software Grid middleware Remote job submission - Globus Toolkit (GT) 2.0, 2.4, 3.0.1 Gatekeeper gideon.csis.hku.hk Job scheduling • OpenPBS 2.3.16 • Maui 3.2.5 Local Job Scheduler Programming • HPF, Fortran 90 • C, C++, Java with MPI • JESSICA2 (HKU) Gideon Ostrich Srgdell Real Communication Lib - MPICH-G2 1.2.3 IPC / Network communication

  9. Main Computing Facilities: HKU-CSIS Gideon 300 Cluster

  10. Research Projects in HKGrid • HKBU: Knowledge Grid (Autonomous grid service composition). • HKPU: Peer-to-peer (P2P) grid, meta scheduler, fault tolerance • HKUST: Development of sensor Grid infrastructure • HKU • ETI: Modelling of Air Quality in Hong Kong (E-Business Technology Institute with the Environmental Protection Department, HKSAR) • Computer Centre : HKU campus grid ; scientific applications running across the ApGrid • CSIS : Robust Speech Recognition (J. Wu and Dr. Q. Huo) • CSIS : Simulation for the DNA Shuffling Experiment (W.H. Hon and Dr. T.W. Lam) • CSIS: Approximate String Matching on DNA Sequences (L.L. Cheng) • CSIS: Whole Genome Alignment via Mutation-Sensitive Sequence Similarity (H.L. Chan, N. Lu, and Dr. T.W. Lam) • ME: Parallel Simulation of Turbulent Flow Model (Dr. C.H. Liu, Dept. of Mechanical Engineering) • CSIS : HKU Grid Point (863 Project: China National Grid) • CSIS: Asia-Pacific Grid • …..

  11. HKGrid – Connections • Links to China National Grid (CNGrid) and Asia-Pacific Grid (ApGrid) via CERNET and APAN • Internet2 connection to the Abilene backbone at Chicago, USA • Plays the role of a gateway for the other bigger grids

  12. 上海超级计算中心 中科院计算所 香港大学 (CSIS) 西安交通大学 中国科技大学 国防科技大学 中科院应用物理所 清华大学 China National Grid (CNGrid) : 863 Project China National Grid Participants 中科院计算所开发的网格系统软件已将计算所、华中科技大学 与香港大学网格节点连接在一起，通过VEGA_GOS … Supporting software : VEGA (织女星)grid management system : dynamic service deployment, single-sign-on, data replication, and performance monitoring. Developed by Institute of Computing Technology, Chinese Academy of Sciences V.1.0 released 8

  13. ApGrid / PRAGMA Testbed • 10 countries • 21 organizations • 22 clusters • 853 CPUs

  14. ApGrid Demon on The HKU School Open Day (Oct. 2003)

  15. Grid Research at HKU-CSIS SRG Projects SLIM + ODGPC G-JavaMPI JESSICA2 LOTS DSM

  16. Our Goal • Utility computing: to aggregate and make use of distributed computing resources transparently • Traditional means: to utilize the dedicated HPC facilities distributed across institutions • Performance and reliability are key • Pervasive means: any user can be resource provider (e.g., idle PCs, etc.) or consumer, or both • Convenience and security are key To construct an advanced grid computing platform to accommodate utility-like computing via traditional and “pervasive” means

  17. Research at HKU – An Advanced Grid Computing Platform (Programming Environment) User’s convenience Convenient system administration Objectives Grid point construction Performance and Reliability G-JavaMPI JESSICA LOTS SLIM ODGPC AGP Load balancing Single- system image Research Issues On-demand Grid point construction (ODGPC)

  18. SLIM Single Linux Image Management

  19. SLIM • Utility computing decouples computing platforms (resources) and computing logic (applications) • I.e., a single platform can run completely different applications • Problem: different applications demand different execution environments (OS, shared libraries, supporting apps, etc.) • Hassles associated with managing execution environments (EE’s) in the resource provider side offset the benefits of resource sharing • SLIM is a network service for managing and constructing EE’s, and disseminating them to remote computing platforms

  20. SLIM – System design How it works? • A node sends a EE specification across the network to find the Boot server • Boot server delivers the requested Linux kernel • Image server constructs an EE by collecting shared libraries, user data, etc. • Linux kernel boots, and contacts the Image Server to “mount” the EE via a file synchronization protocol such as NFS • Aggressive caching techniques are deployed to optimize performance

  21. SLIM – Ongoing and future work • SLIM has been managing: • the HKU-CSIS grid point (350 nodes) for various grid research projects • an addition 300+ lab machines for teaching purpose (different courses have different requirements) • Future work • To overcome the challenges in deploying SLIM over broadband links • Realizing the “pervasive utility computing”

  22. On-Demand Grid Point Construction (ODGPC) 1. Software installation at SLIM server 2. Client boots and obtains kernel 3. OS image/App disseminated 4. Process to generate certificates

  23. SLIM and ODGPC Performance Evaluation 256 PCs < 5 minutes (OS only) • Boot up 100 machines (Linux + GT3) : 6 minutes. • Generate certificates for 100 machines (Step 4) : 30 minutes. • Total time : 6 + 30 = 36 minutes

  24. SLIM – Key references • http://www.csis.hku.hk/~cmlee/slim/ • C.M. Lee, R.S.C. Ho, D.H.F. Hung, C.L. Wang, and F.C.M. Lau, “Managing Execution Environments for Utility Computing,” Network Research Workshop 2004 (with APAN 2004), March, 2004. (LinuxPilot 2004/04)

  25. G-JavaMPI A grid-enabled Java-MPI system with dynamic load-balancing via process migration

  26. G-JavaMPI • A grid-enabled implementation of Java binding of MPI, supporting efficient MPI communication among distributed Java processes • Supports transparent Java process migration (through JVMDI) within and across grid points for balancing CPU and network loads • Communication-aware process migration policies based on: • application’s communication pattern • available network bandwidth on grid overlays

  27. (3) (1) (1*) Gatekeeper LS Gatekeeper LS Java-MPI communication (2) WAN (*) Some legacy messages are redirected during migration (2*) Migrating (restarting a new process through Globus remote job request with delegated user credentials and Java-MPI job credentials) JVM Migration module resides in each JVM M Gatekeeper LS (3*) G-JavaMPI – System design

  28. G-JavaMPI – Ongoing and future work • The migration mechanism has been implemented • Future work targets at process migration policies • Goal: to offset performance pitfalls caused by heterogeneity through dynamic process migration • Sources of heterogeneity in grids • CPU, network, runtime environments, etc. • CPU and network heterogeneities cause long “blocking” periods in cooperative processes, thus limiting the system throughput • G-JavaMPI aims to detect and eliminate “blocking” through process migration (e.g. to migrate a “bottleneck” process to a faster node, etc.)

  29. G-JavaMPI – Key references • L. Chen, C.L. Wang, and F.C.M. Lau, “A Grid Middleware for Distributed Java Computing with MPI Binding and Process Migration Supports,” Journal of Computer Science and Technology (China), Vol. 18, No. 4, July 2003, pp. 505-514. • L. Chen, C.L. Wang, F.C.M. Lau, and R.K.K. Ma, “A Grid Middleware for Distributed Java Computing with MPI Binding and Process Migration Supports,” International Workshop on Grid and Cooperative Computing (GCC-2002), December 26-28, 2002, Hainan, China, pp. 640-652.

  30. JESSICA2 : A Java-Enabled Single-System Image Computing Architecture • JESSICA2 is a distributed Java Virtual Machine (DJVM) which consists of a group of extended JVMs running on a distributed environment to support true parallel execution of a multithreaded Java application. • Java threads can freely move across node boundaries and execute in parallel to achieve more scalable high-performance computing using clusters • The JESSICA2 DJVM provides standard JVM services, that are compliant with the Java language specification, as if running on a single machine – Single System Image (SSI).

  31. JESSICA2 Architecture A Multithreaded Java Program Thread Migration JIT Compiler Mode Portable Java Frame JESSICA2 JVM JESSICA2 JVM JESSICA2 JVM JESSICA2 JVM JESSICA2 JVM JESSICA2 JVM Master Worker Worker Worker Worker Worker Global Object Space

  32. JESSICA2 Main Features • Transparent Java thread migration • Runtime capturing and restoring of thread execution context. • No source code modification; no bytecode instrumentation (preprocessing); no new API introduced • Enable dynamic load balancing on clusters • Full Speed Computation • JITEE: cluster-aware bytecode execution engine • Operated in Just-In-Time (JIT) compilation mode • Zero cost if no migration • Transparent Remote Object Access • Global Object Space :A shared global heap spanning all cluster nodes • Adaptive migrating home protocol for memory consistency + various optimizing schemes. • I/O redirection

  33. Ray Tracing on JESSICA2 (64 PCs) Linux 2.4.18-3 kernel (Redhat 7.3) 64 nodes: 108 seconds 1 node: 4402 seconds ( 1.2 hour) Speedup = 4402/108=40.75

  34. JESSICA – Key references • W.Z. Zhu , C.L. Wang, and F.C.M. Lau “A Lightweight Solution for Transparent Java Thread Migration in Just-in-Time Compilers,” The 2003 International Conference on Parallel Processing (ICPP-2003), pp. 465-472, Taiwan, Oct. 6-10, 2003 • W.Z. Zhu, C.L. Wang and F.C.M. Lau, “JESSICA2: A Distributed Java Virtual Machine with Transparent Thread Migration Support,” IEEE Fourth International Conference on Cluster Computing (CLUSTER 2002), Chicago, USA, September 23-26, 2002, pp. 381-388.  • M.J.M. Ma, C.L. Wang, F.C.M. Lau. “JESSICA: Java-Enabled Single-System-Image Computing Architecture,” Journal of Parallel and Distributed Computing, Vol. 60, No. 10, October 2000, pp. 1194-1222.

  35. LOTS LOTS OS OS H/W H/W LOTS LOTS OS OS H/W H/W LOTS OS H/W LOTS: Large Object Space on Grid • A large software distributed memory system for Grid. • Provides a global object space larger than the process space (4GB in 32-bit CPU) • Uses local hard disk to store recently unused objects • Scope Consistency + Home Migration to reduce redundant data traffic Grid Large Global Object Space

  36. Summary • Performance • G-JavaMPI, JESSICA, establish extensible grid platforms (good for computation-intensive applications) • Process/thread migration enables performance optimization and load balancing • LOTS supports shared memory programming environment on large object space (easier to develop data grid applications) • Reliability • G-JavaMPI migrates processes from failed machines • SLIM helps construct platforms for failover • Convenience • G-JavaMPI, JESSICA, and LOTS enable users to harness distributed resources via traditional means • SLIM and ODGPC simplify Grid point managements

  37. Conclusion • Grid/utility computing are relatively new paradigms that deserve further investigation • We address the performance, reliability, and user convenience issues in grid/utility computing • Our advanced grid computing platform (consisting of G-JavaMPI, JESSICA2, LOTS, and SLIM/ODGPC) is geared to deploy in the HKGrid for easy adoption of Grid technologies.

  38. Q&A Thank you! The SRGers (Photo: 12/2003)

  39. Reference • Hong Kong Grid • http://www.hkgrid.org/ • Grid Computing Research Portal • http://grid.csis.hku.hk/ • The HKU Systems Research Group • http://www.srg.csis.hku.hk • VEGA Project • http://vega.ict.ac.cn/ • The HK Supercomputing Directory • http://www.hkhpc.org/~SuperDir/

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