Cluster Computing – Development
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 file system.
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.
Cluster Computing 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 is 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.
gLite is a set of middleware technologies created by the Enabling Grids for E-sciencE (EGEE) project.
Another example of consumer game products being adapted to high-performance computing is the Nvidia Tesla workstation, which gets its processing power by harnessing the power of multiple graphics accelerator processor chips.
Study: From Wikipedia, the free encyclopedia. The text is available under the Creative Commons.
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