Download Cluster Computing

(c) Raj
Architecture Alternatives for Scalable
Single System Image Clusters
Rajkumar Buyya, Monash University, Melbourne, Australia.
[email protected]
http://www.dgs.monash.edu.au/~rajkumar
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Agenda
Cluster ? Enabling Tech. & Motivations
Cluster Architecture
What Single System Image (SSI) ?
Levels of SSI ?
Cluster Middleware Vs. Underware
Representative SSI implementations
Pros and Cons of SSI at difference levels
Ann.--IEEE Task Force on Cluster Computing
Resources and Conclusions
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Need of more Computing Power:
Grand Challenge Applications
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Solving technology problems using
computer modeling, simulation and analysis
Geographic
Information
Systems
Life Sciences
Aerospace
Mechanical Design & Analysis (CAD/CAM)
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Competing Computer
Architectures
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
Vector Computers (VC) ---proprietary system
– provided the breakthrough needed for the emergence of
computational science, buy they were only a partial answer.

Massively Parallel Processors (MPP)-proprietary
system
– high cost and a low performance/price ratio.

Symmetric Multiprocessors (SMP)
– suffers from scalability

Distributed Systems
– difficult to use and hard to extract parallel performance.

Clusters -- gaining popularity
– High Performance Computing---Commodity Supercomputing
– High Availability Computing ---Mission Critical Applications
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Technology Trend...
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

Performance of PC/Workstations
components has almost reached
performance of those used in
supercomputers…
– Microprocessors (50% to 100% per year)
– Networks (Gigabit ..)
– Operating Systems
– Programming environment
– Applications
Rate of performance improvements of
commodity components is too high.
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Technology Trend
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The Need for Alternative
Supercomputing Resources
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 Cannot
afford to buy “Big Iron”
machines
– due to their high cost and short life span.
– cut-down of funding
– don’t “fit” better into today's funding model.
– ….
 Paradox:
time required to develop a
parallel application for solving GCA is
equal to:
– half Life of Parallel Supercomputers.
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Clusters are bestalternative!
 Supercomputing-class
commodity
components are available
 They “fit” very well with today’s/future
funding model.
 Can leverage upon future
technological advances
– VLSI, CPUs, Networks, Disk, Memory, Cache,
OS, programming tools, applications,...
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Best of both Worlds!
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 High
on this)
Performance Computing (talk focused
– parallel computers/supercomputer-class
workstation cluster
– dependable parallel computers
 High
Availability Computing
– mission-critical systems
– fault-tolerant computing
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What is a cluster?
A
cluster is a type of parallel or distributed
processing system, which consists of a
collection of interconnected stand-alone
computers cooperatively working together
as a single, integrated computing resource.
 A typical cluster:
– Network: Faster, closer connection than a typical
network (LAN)
– Low latency communication protocols
– Looser connection than SMP
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So What’s So Different about
Clusters?
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Commodity Parts?
 Communications Packaging?
 Incremental Scalability?
 Independent Failure?
 Intelligent Network Interfaces?
 Complete System on every node

– virtual memory
– scheduler
– files
–…

Nodes can be used individually or
combined...
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Clustering of Computers
for Collective Computating
1960
1990
1995+
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Computer Food Chain (Now and Future)
Demise of Mainframes, Supercomputers, & MPPs
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Cluster Configuration..1
Dedicated Cluster
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Cluster Configuration..2
Enterprise Clusters (use JMS like Codine)
Shared Pool of
Computing Resources:
Processors, Memory, Disks
Interconnect
Guarantee at least one
workstation to many individuals
(when active)
Deliver large % of collective
resources to few individuals
at any one time
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Windows of Opportunities
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
MPP/DSM:
– Compute across multiple systems: parallel.

Network RAM:
– Idle memory in other nodes. Page across
other nodes idle memory

Software RAID:
– file system supporting parallel I/O and
reliability, mass-storage.

Multi-path Communication:
– Communicate across multiple networks:
Ethernet, ATM, Myrinet
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Cluster Computer
Architecture
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Major issues in cluster
design
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
Size Scalability (physical & application)

Enhanced Availability (failure management)

Single System Image (look-and-feel of one system)

Fast Communication (networks & protocols)

Load Balancing (CPU, Net, Memory, Disk)

Security and Encryption (clusters of clusters)

Distributed Environment (Social issues)

Manageability (admin. And control)

Programmability (simple API if required)

Applicability (cluster-aware and non-aware app.)
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Cluster Middleware
and
Single System Image
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A typical Cluster Computing
Environment
Application
PVM / MPI/ RSH
???
Hardware/OS
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CC should support
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
Multi-user, time-sharing environments

Nodes with different CPU speeds and
memory sizes (heterogeneous configuration)

Many processes, with unpredictable
requirements

Unlike SMP: insufficient “bonds” between
nodes
– Each computer operates independently
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The missing link is provide by
cluster middleware/underware
Application
PVM / MPI/ RSH
Middleware or
Underware
Hardware/OS
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SSI Clusters--SMP services on a
CC
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“Pool Together” the “Cluster-Wide” resources

Adaptive resource usage for better
performance

Ease of use - almost like SMP

Scalable configurations - by decentralized
control
Result: HPC/HAC at PC/Workstation prices
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What is Cluster Middleware ?
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


An interface between between use
applications and cluster hardware and OS
platform.
Middleware packages support each other at
the management, programming, and
implementation levels.
Middleware Layers:
– SSI Layer
– Availability Layer: It enables the cluster services of
• Checkpointing, Automatic Failover, recovery from
failure,
• fault-tolerant operating among all cluster nodes.
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
Middleware Design Goals
Complete Transparency (Manageability)
– Lets the see a single cluster system..

• Single entry point, ftp, telnet, software loading...
Scalable Performance
– Easy growth of cluster

• no change of API & automatic load distribution.
Enhanced Availability
– Automatic Recovery from failures
• Employ checkpointing & fault tolerant technologies
– Handle consistency of data when replicated..
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What is Single System Image
(SSI) ?
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A
single system image is the
illusion, created by software or
hardware, that presents a
collection of resources as one,
more powerful resource.
 SSI makes the cluster appear like a
single machine to the user, to
applications, and to the network.
 A cluster without a SSI is not a
cluster
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Benefits of Single System
Image
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






Usage of system resources transparently
Transparent process migration and load
balancing across nodes.
Improved reliability and higher availability
Improved system response time and
performance
Simplified system management
Reduction in the risk of operator errors
User need not be aware of the underlying
system architecture to use these machines
effectively
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Desired SSI Services
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
Single Entry Point
– telnet cluster.my_institute.edu
– telnet node1.cluster. institute.edu






Single File Hierarchy: xFS, AFS, Solaris MC Proxy
Single Control Point: Management from single
GUI
Single virtual networking
Single memory space - Network RAM / DSM
Single Job Management: Glunix, Codine, LSF
Single User Interface: Like workstation/PC
windowing environment (CDE in Solaris/NT),
may it can use Web technology
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
Availability Support
Functions
Single I/O Space (SIO):
– any node can access any peripheral or disk devices
without the knowledge of physical location.

Single Process Space (SPS)
– Any process on any node create process with cluster
wide process wide and they communicate through
signal, pipes, etc, as if they are one a single node.

Checkpointing and Process Migration.
– Saves the process state and intermediate results in
memory to disk to support rollback recovery when
node fails. PM for Load balancing...
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Scalability Vs. Single System
Image
UP
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SSI Levels/How do we
implement SSI ?
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
It is a computer science notion of levels of
abstractions (house is at a higher level of
abstraction than walls, ceilings, and floors).
Application and Subsystem Level
Operating System Kernel Level
Hardware Level
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SSI Characteristics
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 1.
Every SSI has a boundary
 2. Single system support can exist
at different levels within a system,
one able to be build on another
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SSI Boundaries -- an
applications SSI boundary
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Batch System
SSI
Boundary
Source: In search of clusters
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SSI via OS path!
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
1. Build as a layer on top of the existing OS
– Benefits: makes the system quickly portable, tracks
vendor software upgrades, and reduces development
time.
– i.e. new systems can be built quickly by mapping
new services onto the functionality provided by the
layer beneath. Eg: Glunix

2. Build SSI at kernel level, True Cluster OS
– Good, but Can’t leverage of OS improvements by
vendor
– E.g. Unixware, Solaris-MC, and MOSIX
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SSI Representative Systems
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 OS
level SSI
– SCO NSC UnixWare
– Solaris-MC
– MOSIX, ….
 Middleware level SSI
– PVM, TreadMark (DSM), Glunix,
Condor, Codine, Nimrod, ….
 Application level SSI
– PARMON, Parallel Oracle, ...
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SCO NonStop® Cluster for UnixWare
UP or SMP node
UP or SMP node
Users, applications, and
systems management
Standard OS
kernel calls
Standard SCO
UnixWare®
with clustering
hooks
Extensions
Modular
kernel
extensions
Users, applications, and
systems management
Extensions
Standard OS
kernel calls
Standard SCO
UnixWare
with clustering
hooks
Modular
kernel
extensions
Devices
Devices
ServerNet™
Other nodes
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How does NonStop Clusters
Work?
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
Modular Extensions and Hooks to Provide:
–
–
–
–
–
–
–
–
–
–
–
–
Single Clusterwide Filesystem view
Transparent Clusterwide device access
Transparent swap space sharing
Transparent Clusterwide IPC
High Performance Internode Communications
Transparent Clusterwide Processes, migration,etc.
Node down cleanup and resource failover
Transparent Clusterwide parallel TCP/IP networking
Application Availability
Clusterwide Membership and Cluster timesync
Cluster System Administration
Load Leveling
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Solaris-MC: Solaris for
MultiComputers
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

Applications
System call interface

Network
File system
C++
Processes
Solaris MC
Other
nodes
global file
system
globalized
process
manageme
nt
globalized
networking
and I/O
Object framework
Object invocations
Existing Solaris 2.5 kernel
Kernel
Solaris MC Architecture
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Solaris MC components
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Applications
System call interface
Network
File system
C++
Processes
Object and
communication
support
 High availability
support
 PXFS global
distributed file
system
 Process
mangement
 Networking

Solaris MC
Object framework
Object invocations
Existing Solaris 2.5 kernel
Kernel
Solaris MC Architecture
Other
nodes
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Multicomputer OS for UNIX
(MOSIX)
An OS module (layer) that provides the
applications with the illusion of working on a single
system
 Remote operations are performed like local
operations
 Transparent to the application - user interface
unchanged
Application

PVM / MPI / RSH
Hardware/OS
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Main tool
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Preemptive process migration that can
migrate--->any process, anywhere, anytime

Supervised by distributed algorithms that
respond
on-line to global resource
availability - transparently
Load-balancing - migrate process from overloaded to under-loaded nodes
 Memory ushering - migrate processes from a
node that has exhausted its memory, to prevent
paging/swapping

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MOSIX for Linux at HUJI
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
A scalable cluster configuration:
– 50 Pentium-II 300 MHz
– 38 Pentium-Pro 200 MHz (some are SMPs)
– 16 Pentium-II 400 MHz (some are SMPs)
Over 12 GB cluster-wide RAM
 Connected by the Myrinet 2.56 G.b/s LAN
Runs Red-Hat 6.0, based on Kernel 2.2.7
 Upgrade: HW with Intel, SW with Linux
 Download MOSIX:

– http://www.mosix.cs.huji.ac.il/
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Nimrod - A Job Management
System
http://www.dgs.monash.edu.au/~davida/nimrod.html
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Job processing with Nimrod
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Nimrod Architecture
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PARMON: A Cluster
Monitoring Tool
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PARMON Client on JVM
PARMON Server
on each node
parmon
parmond
PARMON
High-Speed
Switch
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Resource Utilization at a
Glance
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Relationship Among
Middleware Modules
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Globalised Cluster Storage
Single I/O Space and
Design Issues
Reference:
Designing SSI Clusters with Hierarchical Checkpointing and Single I/O
Space”, IEEE Concurrency, March, 1999
by K. Hwang, H. Jin et.al
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Clusters with & without Single
I/O Space
Users
Users
Single I/O Space Services
Without Single I/O Space
With Single I/O Space Services
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Benefits of Single I/O Space
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
Eliminate the gap between accessing local disk(s) and remote
disks

Support persistent programming paradigm

Allow striping on remote disks, accelerate parallel I/O
operations

Facilitate the implementation of distributed checkpointing and
recovery schemes
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Single I/O Space Design Issues
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
Integrated I/O Space

Addressing and Mapping Mechanisms

Data movement procedures
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Integrated I/O Space
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LD1
LD2
...
...
D11 D12
D21 D22
D1t
D2t
...
Sequential
addresses
LDn
...
Dn1 Dn2
B11
B12
SD1
...
...
B21
B22
SD2
...
Dnt
Bm1
Bm2
SDm
...
B1k
B2k
P1
. . .
Local
Disks,
(RADD
Space)
Shared
RAIDs,
(NASD Space)
Bmk
Peripherals
(NAP Space)
Ph
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Addressing and Mapping
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User Applications
Name Agent
I/O Agent
Disk/RAID/
NAP Mapper
I/O Agent
RADD
Block Mover
I/O Agent
I/O Agent
NASD
NAP
User-level
Middleware
plus some
Modified OS
System Calls
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Data Movement Procedures
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User
Application
I/O Agent
Node 1
Block
Mover
Request
Data
Block A
Node 2
I/O Agent
LD2 or SDi
LD1
User
Application
I/O Agent
of the NASD
Node 1
A
Node 2
Block
Mover
A
I/O Agent
LD2 or SDi
LD1
of the NASD
A
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Adoption of the Approach
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Summary
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We have discussed Cluster
Enabling Technologies
Architecture & its Components
Cluster Middleware
Single System Image
Representative SSI Tools
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Conclusions Remarks
Clusters are promising..
Solve parallel processing paradox
Offer incremental growth and matches with
funding pattern
New trends in hardware and software
technologies are likely to make clusters more
promising and fill SSI gap..so that
Clusters based supercomputers can be seen
everywhere!
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Breaking High Performance Computing Barriers
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G
F
L
O
P
S
2100
2100
2100
2100
2100
2100
2100
2100
2100
Single
Processor
Shared
Memory
Local
Parallel
Cluster
Global
Parallel
Cluster
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Announcement: formation
of
IEEE Task Force on Cluster Computing
(TFCC)
http://www.dgs.monash.edu.au/~rajkumar/tfcc/
http://www.dcs.port.ac.uk/~mab/tfcc/
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Well, Read my book for….
Thank You ...
?
http://www.dgs.monash.edu.au/~rajkumar/c
luster/
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Thank You ...
?
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Appendix
Pointers to Literature on
Cluster Computing
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Reading Resources..1a
Internet & WWW
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– Computer Architecture:
• http://www.cs.wisc.edu/~arch/www/
– PFS & Parallel I/O
• http://www.cs.dartmouth.edu/pario/
– Linux Parallel Procesing
• http://yara.ecn.purdue.edu/~pplinux/Sites/
– DSMs
• http://www.cs.umd.edu/~keleher/dsm.html
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Reading Resources..1b
Internet & WWW
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– Solaris-MC
• http://www.sunlabs.com/research/solaris-mc
– Microprocessors: Recent Advances
• http://www.microprocessor.sscc.ru
– Beowulf:
• http://www.beowulf.org
– MOSIX
• http://www.mosix.cs.huji.ac.il/
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Reading Resources..2
Papers
– A Case of NOW, IEEE Micro, Feb’95
• by Anderson, Culler, Paterson
– Designing SSI Clusters with Hierarchical Checkpointing
and Single I/O Space”, IEEE Concurrency, March, 1999
• by K. Hwang, H. Jin et.al
– Cluster Computing: The Commodity Supercomputing,
Journal of Software Practice and Experience, June 1999
• by Mark Baker & Rajkumar Buyya
– Implementing a Full Single System Image UnixWare
Cluster: Middleware vs. Underware
• Bruce Walker and Douglas Steel (SCO NSC Unixware)
• http://www.dgs.monash.edu.au/~rajkumar/pdpta99/
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Reading Resources..3
Books
– In Search of Cluster
• by G.Pfister, Prentice Hall (2ed), 98
– High Performance Cluster Computing
• Volume1: Architectures and Systems
• Volume2: Programming and Applications
– Edited by Rajkumar Buyya, Prentice Hall, 1999.
– Scalable Parallel Computing
• by K Hwang & Z Xu, McGraw Hill,98
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