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PVM and MPI What Else is Needed For Cluster Computing?

PVM and MPI What Else is Needed For Cluster Computing?. Al Geist Oak Ridge National Laboratory www.csm.ornl.gov/~geist. DAPSYS/EuroPVM-MPI Balatonfured, Hungary September 11, 2000. EuroPVM-MPI Dedicated to the hottest developments of PVM and MPI .

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PVM and MPI What Else is Needed For Cluster Computing?

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  1. PVM and MPI What Else is Needed For Cluster Computing? Al Geist Oak Ridge National Laboratory www.csm.ornl.gov/~geist DAPSYS/EuroPVM-MPI Balatonfured, Hungary September 11, 2000

  2. EuroPVM-MPI Dedicated to the hottest developments of PVM and MPI PVM and MPI are the most used tools for parallel programming The hottest trend driving PVM and MPI today is PC clusters running Linux and/or Windows This talk will look at gaps in what PVM and MPI provide for Cluster Computing. What role the GRID may play and What is happening to fill the gaps…

  3. PVM Latest News: New release this summer – PVM 3.4.3 includes: Optimized msgbox routines • More scalable, more robust New Beowulf-linux port • Allows clusters to be behind firewalls yet work together Smart virtual machine startup • Automatically determines the reason for “Can’t start pvmd” Works with Windows2000 • Installshield version available • Improved Win32 communication performance • New Third party PVM Software: • PythonPVM 0.9, JavaPVM, • PVM port using SCI interface

  4. ORNL Sandia PSC Ten Years of Cluster Computing Building a Cluster Computing Environment for 21st Century Networks of Workstations PC Clusters Wide-area GRID experiments MPI-1 MPI-2 I-MPI Harness PVM-1 PVM-2 PVM-3 PVM-3.4 1989 90 94 96 97 99 2000

  5. Compaq ASCI IBM TOP500 Trends – Next 10 years Even the largest machines are clusters 1 PFlop/s 1000000 100000 10000 1 TFlop/s 1000 Performance [GFlop/s] N=1 100 N=10 2005 Entry at 1TFlop/s 2010 Peak at 1PFlop/s 10 1 N=500 0.1 Jun-93 Nov-94 Jun-96 Nov-97 Jun-99 Nov-00 Jun-02 Nov-03 Jun-05 Nov-06 Jun-08 Nov-09 http://www.netlib.org/benchmark/top500.html

  6. Trend in Affordable PC clusters PC Clusters are cost effective from a hardware perspective Many Universities and companies can afford 16 to 100 nodes. System administration is an overlooked cost: people to maintain cluster software written for each cluster higher failure ratesfor COTS Presently there is lack of tools for managing large clusters www.csm.ornl.gov/torc

  7. Cluster Computing Tools C3 Command line cluster toolset C3 is a command line based toolset for system administration and user level operations on a single cluster. C3 functions may also be called in a program. C3 is multithreaded and each function executes in parallel. Functions only executable by sysadmin • cl_pushimage() – push system image across cluster • cl_shutdown() – shutdown specified nodes • cl_push() – push files/directories across cluster • cl_rm() – remove files from multiple nodes • cl_get() – gather cluster files to one location • cl_ps() – returns results of multi-node ps • cl_kill() – kill application across entire cluster • cl_exec() – execution of any command across specified nodes Software available www.csm.ornl.gov/torc

  8. Visualization using Clusters Lowering the Cost of High Performance Graphics VTK ported to Linux Clusters • Visualization toolkit making use of C3 cluster package AVS Express ported to PC Cluster • Expensive but standard package asked for by apps. • Requires AVS Site license to eliminate the cost of individual node licenese Cumulvs plug-in for AVS Express • Combines the interactive visualization and computational steering of Cumulvs with the visualization tools of AVS.

  9. Cluster Computing Tools M3C tool suite Suite of user interface tools for system administration and simultaneous monitoring of multiple PC clusters. Written in Java – web based remote access. Growing list of plug-in modules • Reserve nodes within or across clusters • Submit Job to queue system • Monitor nodes of cluster – also adding a PAPI interface • Install software on selected nodes • Reboot, shutdown, add user, etc. to selected cluster nodes. • Display properties of nodes www.csm.ornl.gov/torc

  10. OSCAR National Consortium for Cluster software OSCAR is a collection of the best known software for building, programming, and using clusters. The collection effort is lead by a national consortium which includes: IBM, SGI, Intel, ORNL, NCSA, MCS Software. Other vendors invited. Goals • Bring uniformity to cluster creation and use • Make clusters more broadly acceptable • Foster commercial versions of the cluster software For more details see Stephen Scott (talk Monday 12:00 DAPSYS track) www.csm.ornl.gov/oscar

  11. M C 3 ORNL M C Tool Architecture designed to work both within and across organizations 3 Java applet Based GUI M3C proxy allows one sysadmin to monitor and update multiple clusters M3C GUI allows user to submit and monitor jobs GUI URL proxy URL CGI URL CGI URL CGI SDSC UTK ORNL Interface thru XML files front-end back-end C3 Scripts/monitors Custom Scripts Third-party Scripts cluster 1 cluster 2 cluster 1 cluster 1

  12. GRID “Ubiquitous” Computing GRID Forum is helping define higher level services • Information services • Uniform naming, locating, and allocating distirbuted resources • Data management and access • Single log-on security MPI and PVM are often seen as lower level capabilities that GRID frameworks support. Legion Neos SinRG Globus NetSolve Condor

  13. Cumulvs – collaborative computational steering Recent Highlights • Release of new version • Development of CAVE viewer • Works w/ PNL global arrays • Made CCA compliant Common Component Architecture A DOE effort to provide a standard for interoperability of high performance components developed by many different groups in different languages or frameworks. Collective port http://z.ca.sandia.gov/~cca-forum/port-spec Cumulvs was the initial reason we started Harness.

  14. HARNESS Exploring New Capabilities in Heterogeneous Distributed Computing Goal Building on our experience and success with PVM create a fundamentally new heterogeneous virtual machine based on three research concepts: • Parallel Plug-in environment • Extend the concept of a plug-in to the parallel computing world. • Dynamic with no restrictions on functions. • Distributed peer-to-peer control • No single point of failure unlike typical client/server models. • Multiple distributed virtual machines merge/split • Provide a means for short-term sharing of resources and collaboration between teams. www.epm.ornl.gov/harness

  15. Merge/split with other VMs Another VM Component based daemon process control Customization and extension by dynamically adding plug-ins user features HARNESS daemon HARNESS Virtual Machine Scalable Distributed control and Component based Daemon Host A Host D Virtual Machine Host B Host C Operation within VM uses Distributed Control

  16. HARNESS Latest News Provide a practical environment and illustrate extensibility • Harness Core (beta release ready)(see talk Monday 16:30 Track 1) • Task library and Harness Daemon software • Provides API to load, unload plug-ins and distributed control. • PVM Plug-in (stalled for summer now back on track) • Provides PVM API veneer to support exiting PVM applications. • Fault tolerant MPI plug-in (see talk Monday 16:50 Track 1) • Provides MPI API for 30 most used functions. Semantics adjusted to allow recovery from corrupted communicator. • VIA communication plug-in (looking at multi-interface transfer) • To illustrate how different low level communication plug-ins • can be used within Harness. And to provide high performance

  17. Parallel Plug-in Research For Heterogeneous Distributed Virtual Machine One research goal is to understand and implement a dynamic parallel plug-in environment. • provides a method for many users to extend Harness • in much the same way that third party serial plug-ins • extend Netscape, Photoshop, and Linux. • Tough Research problems include: • Heterogeneity (has delayed ‘C’ H-core development.) • Synchronization - Dynamic installation • Interoperation • between same plug-in on different tasks • between task plug-in and daemon plug-in • between daemon plug-ins • Partial success

  18. Fault Tolerant MPI Motivation • As application and machine sizes grow • the MTBF is less than the application run time. • MPI standard is based on a static model so any decrease in tasks • leads to corrupted communicator (MPI_COMM_WORLD). • Develop MPI plugin that takes advantage of Harness • robustness to allow a range of recovery alternatives • to an MPI application. Not just another MPI implementation. • FT-MPI follows the syntax of MPI standard • Communication performance on par with MPICH • Presently uses PVM3.4.3 fault recovery until Harness is ready)

  19. Fault Tolerant MPI Recovery requires MPI semantic changes MPI_COMM_CREATE ( comm, group, newcomm) MPI_COMM_SPLIT ( comm, color, key, newcomm) Creates a new communicator that contains all surviving processes Allows MPI_COMM_WORLD to be specified as both input and output communicator. Key step to MPI recovery is creating a communicator that app can use to continue. Accomplished by modifying the semantics of two MPI functions.

  20. Symmetric Peer-to-Peer Distributed Control Characteristics • No single point (or set of points) of failure for Harness. It survives as long as one member still lives. • All members know the state of the virtual machine, and their knowledge is kept consistent w.r.t. the order of changes of state. (Important parallel programming requirement!) • No member is more important than any other (at any instant) i.e. here isn’t a pass-around “control token”

  21. Distributed Control Harness Two Phase Arbitration Virtual machine 3. Each adds request to a list of pending changes 2. Send host/T#/data to neighbor in ring 1. A task on this host requests a new host be added VM state held by each kernel Harness kernels on each host have arbitrary priority assigned to them (new kernels are always given the lowest priority)

  22. Harness Distributed Control Control is Scalable, Asynchronous, and Parallel Scalable Design Supports multiple simultaneous updates 1<=S<=P Supports fast host adding add host Parallel recovery from multiple host failures Fast host delete or recovery from fault

  23. For more Information Also - Copy of these slides Follow the links from my Web site www.csm.ornl.gov/~geist

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