pvm and mpi what else is needed for cluster computing? al geist oak ridge national laboratory geist...
TRANSCRIPT
PVM and MPI What Else is Needed
For Cluster Computing?
Al GeistOak Ridge National Laboratory
www.csm.ornl.gov/~geist
DAPSYS/EuroPVM-MPIBalatonfured, HungarySeptember 11, 2000
EuroPVM-MPIEuroPVM-MPIDedicated to the hottest developments of PVM and 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…
New release this summer – PVM 3.4.3 includes:New release this summer – PVM 3.4.3 includes:Optimized msgbox routines
– More scalable, more robustNew Beowulf-linux port
– Allows clusters to be behind firewalls yet work togetherSmart virtual machine startup
– Automatically determines the reason for “Can’t start pvmd”
Works with Windows2000– Installshield version available– Improved Win32 communication performance
PVM Latest News: PVM Latest News:
New Third party PVM Software: New Third party PVM Software: PythonPVM 0.9, JavaPVM, PVM port using SCI interface interface
1989 9490 96 97 99 2000
PVM-1
MPI-1 MPI-2 I-MPI
PVM-2 PVM-3 PVM-3.4Harness
Wide-area GRID experiments
Ten Years of Cluster ComputingTen Years of Cluster ComputingBuilding a Cluster Computing Environment for 21Building a Cluster Computing Environment for 21stst Century Century
Networksof Workstations PC Clusters
0.1
1
10
100
1000
10000
100000
1000000
Jun-9
3
Nov-94
Jun-9
6
Nov-97
Jun-9
9
Nov-00
Jun-0
2
Nov-03
Jun-0
5
Nov-06
Jun-0
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Nov-09
Per
form
ance
[G
Flo
p/s
]
N=1
N=500
N=10
1 TFlop/s
1 PFlop/s
2005 Entry at 1TFlop/s 2010 Peak at 1PFlop/s
ASCI
IBMCompaq
TOP500 Trends – Next 10 yearsTOP500 Trends – Next 10 yearsEven the largest machines are clusters Even the largest machines are clusters
http://www.netlib.org/benchmark/top500.html
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 rates for COTS
Presently there is lack of tools for managing large clusters
www.csm.ornl.gov/torc
Trend in Affordable PC clusters Trend in Affordable PC clusters
C3 Command line cluster toolset
Cluster Computing Tools Cluster Computing Tools
• 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
Functions
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.
only executableby sysadmin
Software available www.csm.ornl.gov/torc
VTK ported to Linux Clusters– Visualization toolkit making
use of C3 cluster packageAVS 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.
Visualization using Clusters Visualization using Clusters Lowering the Cost of High Performance Graphics
M3C tool suite
Cluster Computing Tools Cluster Computing Tools
• 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
Growing list of plug-in modules
Suite of user interface tools for system administration and simultaneous monitoring of multiple PC clusters.
Written in Java – web based remote access.
www.csm.ornl.gov/torc
OSCAROSCAR
• Bring uniformity to cluster creation and use• Make clusters more broadly acceptable• Foster commercial versions of the cluster software
Goals
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.
www.csm.ornl.gov/oscar
National Consortium for Cluster software
For more details see Stephen ScottFor more details see Stephen Scott(talk Monday 12:00 DAPSYS track)
ORNL M C ToolORNL M C ToolArchitecture designed to work both within and across organizations Architecture designed to work both within and across organizations
3
URL CGI
C3 Scripts/monitors
URL CGI
Custom Scripts
URL CGI
Third-party Scripts
GUIproxy
URL
front-end
back-end
M C3
Java appletBased GUI
cluster 1 cluster 2 cluster 1 cluster 1
Interface thruXML files
M3C proxy allows one sysadmin to monitor and update multiple clusters
M3C GUI allowsuser to submitand monitor jobs
ORNL UTK SDSC
• Information services• Uniform naming, locating, and allocating
distirbuted resources• Data management and access• Single log-on security
GRID “Ubiquitous” ComputingGRID “Ubiquitous” Computing
GRID Forum is helping define higher level servicesGRID Forum is helping define higher level services
MPI and PVM are often seen asMPI and PVM are often seen aslower level capabilities thatlower level capabilities thatGRID frameworks support.GRID frameworks support.
GlobusGlobusNetSolveNetSolveCondorCondor
LegionLegionNeosNeosSinRGSinRG
Cumulvs – collaborative computational steering Cumulvs – collaborative computational steering
Recent Highlights
• Release of new version• Development of CAVE viewer• Works w/ PNL global arrays• Made CCA compliant
Cumulvs was the initial reason we started Harness.Cumulvs was the initial reason we started Harness.
Collective portA DOE effort to provide a standard for interoperability of high performance components developed by many different groups in different languages or frameworks.
Common Component ArchitectureCommon Component Architecture
http://z.ca.sandia.gov/~cca-forum/port-spec
HARNESSHARNESS Exploring New Capabilities in Heterogeneous Distributed Exploring New Capabilities in Heterogeneous Distributed ComputingComputing
www.epm.ornl.gov/harness
• 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.
Building on our experience and success with PVMcreate a fundamentally new heterogeneous virtual machinebased on three research concepts:
Goal
Host D
Host C
Host B
Host A
VirtualMachine
Operation within VM usesDistributed Control
process control
user features
HARNESS daemon
Customizationand extensionby dynamicallyadding plug-ins
Componentbased daemon
Merge/split with other VMs
AnotherVM
HARNESS Virtual MachineHARNESS Virtual MachineScalable Distributed control and Component based Daemon Scalable Distributed control and Component based Daemon
HARNESS Latest NewsHARNESS Latest News Provide a practical environment and illustrate extensibility Provide a practical environment and illustrate extensibility
• Harness Core (beta release ready)(see talk Monday 16:30 Track 1) Task library and Harness Daemon softwareProvides 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-inscan be used within Harness. And to provide high performance
Parallel Plug-in ResearchParallel Plug-in ResearchFor Heterogeneous Distributed Virtual MachineFor Heterogeneous Distributed Virtual Machine
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
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.
Fault Tolerant MPIFault Tolerant MPI Motivation 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 tasksleads 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 MPICHPresently uses PVM3.4.3 fault recovery until Harness is ready)
Fault Tolerant MPIFault Tolerant MPI Recovery requires MPI semantic changes Recovery requires MPI semantic changes
Key step to MPI recovery is creating a communicator that app can use to continue.
Accomplished by modifying the semantics of two MPI functions.
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.
• 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”
Symmetric Peer-to-Peer Distributed ControlSymmetric Peer-to-Peer Distributed ControlCharacteristicsCharacteristics
Harness kernels on each hosthave arbitrary priority assigned to them(new kernels are always given the lowest priority)
Virtual machine
A task on this hostrequests a new host be added
VM state held by
each kernel
2.
Each adds request to alist of pending changes
1.
Send host/T#/datato neighbor in ring
3.
Distributed ControlDistributed ControlHarness Two Phase ArbitrationHarness Two Phase Arbitration
Supportsmultiple
simultaneous updates
Harness Distributed ControlHarness Distributed ControlControl is Scalable, Asynchronous, and ParallelControl is Scalable, Asynchronous, and Parallel
addhost
Fast host deleteor recoveryfrom fault
Parallel recoveryfrom multiplehost failures
Supportsfast hostadding
Scalable Design1<=S<=P
Follow the links from my Web site
www.csm.ornl.gov/~geist
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