Cluster (Computing)

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Cluster (computing)

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The Silicon Graphics Cluster-SGI; an example of a cluster computer

A computer cluster is a group of linked computers, working together closely so that in many respects they form a single computer. The components of a cluster are commonly, but not always, connected to each other through fast local area networks. Clusters are usually deployed to improve performance and/or availability over that of a single computer, while typically being much more cost-effective than single computers of comparable speed or availability.[1]

* 1 Cluster categorizations
o 1.1 High-availability (HA) clusters
o 1.2 Load-balancing clusters
o 1.3 Compute clusters
o 1.4 Grid computing
* 2 Implementations
o 2.1 Consumer game consoles
* 3 History
* 4 Technologies
* 5 See also
* 6 References
o 6.1 Further reading
* 7 External links

[edit] Cluster categorizations
[edit] High-availability (HA) clusters

High-availability clusters (also known as Failover Clusters) are implemented primarily for the purpose of improving the availability of services that the cluster provides. They operate by having redundant nodes, which are then used to provide service when system components fail. The most common size for an HA cluster is two nodes, which is the minimum requirement to provide redundancy. HA cluster implementations attempt to use redundancy of cluster components to eliminate single points of failure.

There are commercial implementations of High-Availability clusters for many operating systems. The Linux-HA project is one commonly used free software HA package for the Linux operating system.
[edit] Load-balancing clusters

Load-balancing is when multiple computers are linked together to share computational workload or function as a single virtual computer. Logically, from the user side, they are multiple machines, but function as a single virtual machine. Requests initiated from the user are managed by, and distributed among, all the standalone computers to form a cluster. This results in balanced computational work among different machines, improving the performance of the cluster system.
[edit] Compute clusters

Often clusters are used primarily for computational purposes, rather than handling IO-oriented operations such as web service or databases. For instance, a cluster might support computational simulations of weather or vehicle crashes. The primary distinction within compute clusters is how tightly-coupled the individual nodes are. For instance, a single compute job may require frequent communication among nodes - this implies that the cluster shares a dedicated network, is densely located, and probably has homogenous nodes. This cluster design is usually referred to as Beowulf Cluster. The other extreme is where a compute job uses one or few nodes, and needs little or no inter-node communication. This latter category is sometimes called "Grid" computing. Tightly-coupled compute clusters are designed for work that might traditionally have been called "supercomputing". Middleware such as MPI (Message Passing Interface) or PVM (Parallel Virtual Machine) permits compute clustering programs to be portable to a wide variety of clusters.
[edit] Grid computing
Main article: Grid computing

Grids are usually computer clusters, but more focused on throughput like a computing utility rather than running fewer, tightly-coupled jobs. Often, grids will incorporate heterogeneous collections of computers, possibly distributed geographically, sometimes administered by unrelated organizations.

Grid computing is optimized for workloads which consist of many independent jobs or packets of work, which do not have to share data between the jobs during the computation process. Grids serve to manage the allocation of jobs to computers which will perform the work independently of the rest of the grid cluster. Resources such as storage may be shared by all the nodes, but intermediate results of one job do not affect other jobs in progress on other nodes of the grid.

An example of a very large grid is the Folding@home project. It is analyzing data that is used by researchers to find cures for diseases such as Alzheimer's and cancer. Another large project is the SETI@home project, which may be the largest distributed grid in existence. It uses approximately three million home computers all over the world to analyze data from the Arecibo Observatory radiotelescope, searching for evidence of extraterrestrial intelligence. In both of these cases, there is no inter-node communication or shared storage. Individual nodes connect to a main, central location to retrieve a small processing job. They then perform the computation and return the result to the central server. In the case of the @home projects, the software is generally run when the computer is otherwise idle. U of C Berkley has developed an open source application BOINC to allow individual users to contribute to the above and other projects such as lhc@home (Large Hadron Collider) from a single manager which can then be set to allocate a percentage of idle time to each of the projects a node is signed up for. The Software can be downloaded and a project list can be found here BOINC

The grid setup means that the nodes can take however many jobs they are able to process in one session and then return the results and acquire a new job from a central project server.
[edit] Implementations

The TOP500 organization's semiannual list of the 500 fastest computers usually includes many clusters. TOP500 is a collaboration between the University of Mannheim, the University of Tennessee, and the National Energy Research Scientific Computing Center at Lawrence Berkeley National Laboratory. As of November 17, 2009, the top supercomputer is the Department of Energy's Jaguar system with performance of 1759 TFlops measured with the High-Performance LINPACK benchmark.

Clustering can provide significant performance benefits versus price. The System X supercomputer at Virginia Tech, the 28th most powerful supercomputer on Earth as of June 2006[2], is a 12.25 TFlops computer cluster of 1100 Apple XServe G5 2.3 GHz dual-processor machines (4 GB RAM, 80 GB SATA HD) running Mac OS X and using InfiniBand interconnect. The cluster initially consisted of Power Mac G5s; the rack-mountable XServes are denser than desktop Macs, reducing the aggregate size of the cluster. The total cost of the previous Power Mac system was $5.2 million, a tenth of the cost of slower mainframe computer supercomputers. (The Power Mac G5s were sold off.)

The central concept of a Beowulf cluster is the use of commercial off-the-shelf (COTS) computers to produce a cost-effective alternative to a traditional supercomputer. One project that took this to an extreme was the Stone Soupercomputer.

However it is worth noting that FLOPs (floating point operations per second), aren't always the best metric for supercomputer speed. Clusters can have very high FLOPs, but they cannot access all data in the cluster as a whole has at once. Therefore clusters are excellent for parallel computation, but much poorer than traditional supercomputers at non-parallel computation.

JavaSpaces is a specification from Sun Microsystems that enables clustering computers via a distributed shared memory.
[edit] Consumer game consoles

Due to the increasing computing power of each generation of game consoles, a novel use has emerged where they are repurposed into HPC clusters. Some examples of game console clusters are Sony PlayStation clusters and Microsoft Xbox clusters. It has been suggested on a news website that countries which are restricted from buying supercomputing technologies may be obtaining game systems to build computer clusters for military use.[3]
[edit] History

The history of cluster computing is best captured by a footnote in Greg Pfister's In Search of Clusters: “Virtually every press release from DEC mentioning clusters says ‘DEC, who invented clusters…’. IBM did not invent them either. Customers invented clusters, as soon as they could not fit all their work on one computer, or needed a backup. The date of the first is unknown, but it would be surprising if it was not in the 1960s, or even late 1950s.”[4]

The formal engineering basis of cluster computing as a means of doing parallel work of any sort was arguably invented by Gene Amdahl of IBM, who in 1967 published what has come to be regarded as the seminal paper on parallel processing: Amdahl's Law. Amdahl's Law describes mathematically the speedup one can expect from parallelizing any given otherwise serially performed task on a parallel architecture. This article defined the engineering basis for both multiprocessor computing and cluster computing, where the primary differentiator is whether or not the interprocessor communications are supported "inside" the computer (on for example a customized internal communications bus or network) or "outside" the computer on a commodity network.

Consequently the history of early computer clusters is more or less directly tied into the history of early networks, as one of the primary motivation for the development of a network was to link computing resources, creating a de facto computer cluster. Packet switching networks were conceptually invented by the RAND corporation in 1962. Using the concept of a packet switched network, the ARPANET project succeeded in creating in 1969 what was arguably the world's first commodity-network based computer cluster by linking four different computer centers (each of which was something of a "cluster" in its own right, but probably not a commodity cluster). The ARPANET project grew into the Internet—which can be thought of as "the mother of all computer clusters" (as the union of nearly all of the compute resources, including clusters, that happen to be connected). It also established the paradigm in use by all computer clusters in the world today—the use of packet-switched networks to perform interprocessor communications between processor (sets) located in otherwise disconnected frames.

The development of customer-built and research clusters proceeded hand in hand with that of both networks and the Unix operating system from the early 1970s, as both TCP/IP and the Xerox PARC project created and formalized protocols for network-based communications. The Hydra operating system was built for a cluster of DEC PDP-11 minicomputers called C.mmp at Carnegie Mellon University in 1971. However, it was not until circa 1983 that the protocols and tools for easily doing remote job distribution and file sharing were defined (largely within the context of BSD Unix, as implemented by Sun Microsystems) and hence became generally available commercially, along with a shared filesystem.

The first commercial clustering product was ARCnet, developed by Datapoint in 1977. ARCnet was not a commercial success and clustering per se did not really take off until DEC released their VAXcluster product in 1984 for the VAX/VMS operating system. The ARCnet and VAXcluster products not only supported parallel computing, but also shared file systems and peripheral devices. The idea was to provide the advantages of parallel processing, while maintaining data reliability and uniqueness. VAXcluster, now VMScluster, is still available on OpenVMS systems from HP running on Alpha and Itanium systems.

Two other noteworthy early commercial clusters were the Tandem Himalaya (a circa 1994 high-availability product) and the IBM S/390 Parallel Sysplex (also circa 1994, primarily for business use).

No history of commodity computer clusters would be complete without noting the pivotal role played by the development of Parallel Virtual Machine (PVM) software in 1989. This open source software based on TCP/IP communications enabled the instant creation of a virtual supercomputer—a high performance compute cluster—made out of any TCP/IP connected systems. Free form heterogeneous clusters built on top of this model rapidly achieved total throughput in FLOPS that greatly exceeded that available even with the most expensive "big iron" supercomputers. PVM and the advent of inexpensive networked PCs led, in 1993, to a NASA project to build supercomputers out of commodity clusters. In 1995 the invention of the "beowulf"-style cluster—a compute cluster built on top of a commodity network for the specific purpose of "being a supercomputer" capable of performing tightly coupled parallel HPC computations. This in turn spurred the independent development of Grid computing as a named entity, although Grid-style clustering had been around at least as long as the Unix operating system and the Arpanet, whether or not it, or the clusters that used it, were named.
[edit] Technologies

MPI is a widely-available communications library that enables parallel programs to be written in C, Fortran, Python, OCaml, and many other programming languages.

The GNU/Linux world supports various cluster software; for application clustering, there is Beowulf, distcc, and MPICH. Linux Virtual Server, Linux-HA - director-based clusters that allow incoming requests for services to be distributed across multiple cluster nodes. MOSIX, openMosix, Kerrighed, OpenSSI are full-blown clusters integrated into the kernel that provide for automatic process migration among homogeneous nodes. OpenSSI, openMosix and Kerrighed are single-system image implementations.

Microsoft Windows Compute Cluster Server 2003 based on the Windows Server platform provides pieces for High Performance Computing like the Job Scheduler, MSMPI library and management tools. NCSA's recently installed Lincoln is a cluster of 450 Dell PowerEdge 1855 blade servers running Windows Compute Cluster Server 2003. This cluster debuted at #130 on the Top500 list in June 2006.

gridMathematica provides distributed computations over clusters including data analysis, computer algebra and 3D visualization. It can make use of other technologies such as Altair PBS Professional, Microsoft Windows Compute Cluster Server, Platform LSF and Sun Grid Engine.[5]

gLite is a set of middleware technologies created by the Enabling Grids for E-sciencE (EGEE) project.

Another example of consumer game products being added to high-performance computing is the Nvidia Tesla workstation, which gets its processing power by harnessing the power of multiple graphics accelerator processor chips.
[edit] See also

* Botnet
* Computer cluster in virtual machines
* Clustered file system
* Distributed data store
* Flash mob computing
* GPU cluster
* HP ServiceGuard
* Peer-to-peer
* Portable cluster
* Red Hat Cluster Suite
* RoS (computing)
* Server farm
* Compile farm
* Solaris Cluster
* Symmetric multiprocessing
* Terracotta Cluster
* Two-node cluster
* Veritas Cluster Server
* Grid Computing

[edit] References

1. ^ Bader, David; Robert Pennington (June 1996). "Cluster Computing: Applications". Georgia Tech College of Computing. Retrieved 2007-07-13.
2. ^ TOP500 List - June 2006 (1-100) | TOP500 Supercomputing Sites
3. ^ Farah, Joseph (2000-12-19). "Why Iraq's buying up Sony PlayStation 2s". World Net Daily.
4. ^ Pfister, Gregory (1998). In Search of Clusters (2nd ed.). Upper Saddle River, NJ: Prentice Hall PTR. p. 36. ISBN 0-13-899709-8.
5. ^ gridMathematica Cluster Integration.

[edit] Further reading

* Mark Baker, et al., Cluster Computing White Paper [1], 11 Jan 2001.
* Karl Kopper: The Linux Enterprise Cluster: Build a Highly Available Cluster with Commodity Hardware and Free Software, No Starch Press, ISBN 1-59327-036-4
* Robert W. Lucke: Building Clustered Linux Systems, Prentice Hall, 2005, ISBN 0-13-144853-6
* Evan Marcus, Hal Stern: Blueprints for High Availability: Designing Resilient Distributed Systems, John Wiley & Sons, ISBN 0-471-35601-8
* Greg Pfister: In Search of Clusters, Prentice Hall, ISBN 0-13-899709-8
* Rajkumar Buyya (editor): High Performance Cluster Computing: Architectures and Systems, Volume 1, ISBN 0-13-013784-7, Prentice Hall, NJ, USA, 1999.
* Rajkumar Buyya (editor): High Performance Cluster Computing: Programming and Applications, Volume 2, ISBN 0-13-013785-5, Prentice Hall, NJ, USA, 1999.

[edit] External links
Search Wikimedia Commons Wikimedia Commons has media related to: Computer cluster

* IEEE task force on cluster computing
* www.ClusterGate.RU - information on midrange computer clusters
* J2EE Clustering
* Survey of Popular Clustering Technologies
* Build windows cluster with iSCSI

v • d • e
Parallel computing topics
Cloud computing · High-performance computing · Cluster computing · Distributed computing · Grid computing
Parallelism (levels)
Bit · Instruction · Data · Task
Superthreading · Hyperthreading
Amdahl's law · Gustafson's law · Cost efficiency · Karp-Flatt metric · slowdown · speedup
Process · Thread · Fiber · PRAM
Multiprocessing · Multithreading · Memory coherency · Cache coherency · Barrier · Synchronization · Application checkpointing
Models (Implicit parallelism · Explicit parallelism · Concurrency) · Flynn's taxonomy (SISD • SIMD • MISD • MIMD (SPMD))

Multiprocessing (Symmetric · Asymmetric) · Memory (NUMA · COMA · distributed · shared · distributed shared) · SMT
MPP · Superscalar · Vector processor · Supercomputer · Beowulf
POSIX Threads · OpenMP · PVM · MPI · UPC · Intel Threading Building Blocks · Boost.Thread · Global Arrays · Charm++ · Cilk · Co-array Fortran · CUDA · FastFlow
Embarrassingly parallel · Grand Challenge · Software lockout · Scalability · Race conditions · Deadlock · Deterministic algorithm
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Categories: Cluster computing | Parallel computing | Concurrent computing | Supercomputers | Local area networks | Classes of computers
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