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Survey of HPCC Middlewares Architecture

Survey of HPCC Middlewares Architecture. Mohammad Aghajany Universuty of science & Technology of Mazandaran - Babol , Distributed Systems Class Seminar, Supervisor: Hadi Salimi. Types of clusters . High Availability Load Balancing High Performance Computing.

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Survey of HPCC Middlewares Architecture

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  1. Survey of HPCC Middlewares Architecture Mohammad Aghajany Universutyof science & Technology of Mazandaran- Babol, Distributed Systems Class Seminar, Supervisor: Hadi Salimi

  2. Types of clusters  • High Availability • Load Balancing • High Performance Computing

  3. Evaluation of Cluster Middleware in a Heterogeneous Computing EnvironmentMASTER'S THESISStefan Georgiev • Low Level Middleware • Middleware for Parallel File Systems • High Level Middleware • Grid Middleware

  4. High Performance Computing Cluster Softwarehttp://www.squidoo.com/linux-clustering • File System • Installation • Management/Administration • Monitoring • Queueing and Scheduling

  5. Architecture Base • Communication • Scheduler • File System • Resource Manager • Workload Manager

  6. Communication MiddleWares • MPI • Open MPI • MPICH2 • LAM/MPI • MPI/Pro • PACX-MPI

  7. MPI Historically, the evolution of the standards is from MPI1.0 (June 1994) to MPI-1.1 (June 12, 1995) to MPI-1.2 (July 18, 1997), with several clarications and additions and, to . . MPI-2.1 (June 23, 2008),combining the previous documents. This version, MPI-2.2, is based on MPI-2.1 and provides additional clarications and errata corrections as well as a few enhancements.

  8. Using MPI

  9. Michael J. Flynn Taxonomy • SISD – Single Instruction stream / Single Data stream • SIMD – Single Instruction stream / Multiple Data stream • MISD –Multiple Instruction stream/Single Data stream(No real application) • MIMD – Multiple Instruction stream / Multiple Data stream

  10. E. E. Johnson Taxonomy • GMSV – Global Memory / Shared Variables. • GMMP – Global Memory / Message Passing (No real application) • DMSV – Distributed Memory / Shared Variables. • DMMP – Distributed Memory / Message Passing

  11. Flynn-Johnson Taxonomy

  12. Overview • MPI (Message-Passing Interface) is a message-passing library interface specication. • All parts of this denition are signicant. • MPI is a specication, not an implementation; • This specication is for a library interface; • MPI is not a language, and all MPI operations are expressed as functions,subroutines, or methods, according to the appropriate language bindings, which for C, C++, Fortran-77, and Fortran-95, are part of the MPI standard.

  13. MPI Architecture • SPMD: Single Program Multiple Data • Data types • Communicators • Pair-wise communication • Collective communication

  14. Open MPI • Open MPI API (OMPI) • Open Run-Time Environment (ORTE) • Open Portable Access Layer (OPAL)

  15. MCA (modular Component rchitecture)

  16. Scheduler MiddleWares • Maui Cluster Scheduler • Condor

  17. Maui Cluster Scheduler • Maui is an advanced job scheduler for use on clusters and supercomputers. • It is a highly optimized and configurable tool capable of supporting a large array of scheduling policies, dynamic priorities, extensive reservations.

  18. Condor A Distributed Scheduler A typical Condor system consists of four components: • A Condor pool • Central manager • Submitting machine • Execution machine

  19. Condor Architecture

  20. File System • PVFS • HDFS • OpenAFS • DRBD • GFS • GPFS • Lustre

  21. PVFS Architecture

  22. Resource Manager • TORQUE • LSF • PBS Pro • OpenPBS • Load Leveler

  23. TORQUE TORQUE is an open source resource manager providing control over batch jobs and distributed compute nodes. It is a community effort based on the original *PBS project and, with more than 1,200 patches, has incorporated significant advances in the areas of scalability, fault tolerance, and feature extensions.

  24. Load Leveler • Pool • Daemons LoadLeveler • Central machine

  25. WorkLoad Manager Moab Workload Manager • Moab Workload Manager is a highly advanced scheduling and management system designed for clusters, grids, and on-demand/utility computing systems. • Moab enables true adaptive computing allowing compute resources to be customized to changing needs and failed systems to be automatically fixed or replaced. Moab increases system resource availability, offers extensive cluster diagnostics, delivers powerful QoS/SLA features, and provides rich visualization of cluster performance through advanced statistics, reports, and charts.

  26. Evaluation Stefan Georgiev A. Low Level Middleware • 1. MPI • 2. Open MPI • 3. PVM B. Middleware for Parallel File Systems • 1. MPI-IO • 2. PVFS • 3. Hadoop • 4. Sector-Sphere C. High Level Middleware • 1. Beowulf • 2. OSCAR • 3. OpenMosix • 4. CAOS NSA/ Perceus • 5. ROCKS D. Grid Middleware • 1. Condor • 2. Globus ToolKit

  27. Evaluation (Cont.) • Communications • MPI • MPICH2 • MPICH-GM • MetaMPICH • MVAPICH • LAM/MPI • PACX-MPI • ScaMPI or Scali MPI • MPI/Pro • Open MPI • ESSL • VMI Resource Manager • LSF • TORQUE • PBS Pro • OpenPBS • LoadLeveler • Condor • SGE - Sun Grid Engine • GreenTe Workload Manager • Moab Advanced Scheduler • Moab • Maui Scheduler

  28. References • 1- Georgiev,Stefan,Evaluation of Cluster Middleware in a Heterogeneous Computinge nvironment , Master of Science , INTERNATIONALER UNIVERSITÄTSLEHRGANG , Hagenberg ,July, 2009 . • 2- Chang Liu,Zhiwen Zhao , Fang Liu, “An Insight Into The Architecture Of Condor “,Computer network and Multimedia Technology, CNMT,2009. • 3- http://www.squidoo.com/linux-clustering • 4-http://www.mpi-forum.org/ • 5- Toby Sebast ian,Sanjay Lalwani,Munira Huss ain,Utilizing Open MPI Middleware for HPC Clusters, Dell Power Solutions, November ,2007. • 6- M. Vilayannur, S. Lang, R. Ross, R. Klundt and L. Ward , Extending the POSIX I/O Interface:A Parallel File System Perspective, UChicago Argonne, October, 2008. • 7- http://www.clustermonkey.net//content/view/35/28/1/2/

  29. References (Cont.) • 8- Garhan Attebury, Andrew Baranovski, Ken Bloom, “Hadoop Distributed File System for the Grid”, IEEE Nuclear Science Symposium Conference Record (NSS/MIC), 2009. • 9-http://hadoop.apache.org/common/docs/current/hdfs_design.html • 10-http://www.clusterbuilder.org/software/cluster-middleware/resource-manager/loadleveler/more-info.php • 11- Subramanian Kannan,Mark Roberts,Peter Mayes, Workload Management with LoadLeveler, IBM Corporation, 2001. • 12-http://www.clusterresources.com/products/mwm • 13-Jameela Al-Jaroodi, Nader Mohamed, Hong Jiang, David Swanson,” Middleware Infrastructure for Parallel and Distributed Programming Models in Heterogeneous Systems”,IEEE Transactions on Parallel and Distributed Systems,pp.1100-1111,2003. • 14- Narravula, S. Subramoni, H. Lai, P. Noronha, R. Panda, D.K.Panda ,” Performance of HPC Middleware over InfiniBand WAN”, 37th International Conference on Parallel Processing, ICPP '08, 2008.

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