A Beowulf cluster is a computer cluster of what are normally identical, commodity-grade computers networked into a small local area network with libraries and programs installed which allow processing to be shared among them. The result is a high-performance parallel computing cluster from inexpensive personal computer hardware.
The name Beowulf originally referred to a specific computer built in 1994 by Thomas Sterling and Donald Becker at NASA. The name "Beowulf" comes from the Old English epic poem of the same name.
No particular piece of software defines a cluster as a Beowulf. Typically only free and open source software is used, both to save cost and to allow customization. Most Beowulf clusters run a Unix-like operating system, such as BSD, Linux, or Solaris. Commonly used parallel processing libraries include Message Passing Interface (MPI) and Parallel Virtual Machine (PVM). Both of these permit the programmer to divide a task among a group of networked computers, and collect the results of processing. Examples of MPI software include Open MPI or MPICH. There are additional MPI implementations available.
Beowulf systems operate worldwide, chiefly in support of scientific computing. Since 2017, every system on the Top500 list of the world's fastest supercomputers has used Beowulf software methods and a Linux operating system. At this level, however, most are by no means just assemblages of commodity hardware; custom design work is often required for the nodes (often blade servers), the networking and the cooling systems.
A description of the Beowulf cluster, from the original "how-to", which was published by Jacek Radajewski and Douglas Eadline under the Linux Documentation Project in 1998:
Beowulf is a multi-computer architecture which can be used for parallel computations. It is a system which usually consists of one server node, and one or more client nodes connected via Ethernet or some other network. It is a system built using commodity hardware components, like any PC capable of running a Unix-like operating system, with standard Ethernet adapters, and switches.
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Parallel computing is a type of computation in which many calculations or processes are carried out simultaneously. Large problems can often be divided into smaller ones, which can then be solved at the same time. There are several different forms of parallel computing: bit-level, instruction-level, data, and task parallelism. Parallelism has long been employed in high-performance computing, but has gained broader interest due to the physical constraints preventing frequency scaling.
A computer cluster is a set of computers that work together so that they can be viewed as a single system. Unlike grid computers, computer clusters have each node set to perform the same task, controlled and scheduled by software. The components of a cluster are usually connected to each other through fast local area networks, with each node (computer used as a server) running its own instance of an operating system. In most circumstances, all of the nodes use the same hardware and the same operating system, although in some setups (e.
Message Passing Interface (MPI) is a standardized and portable message-passing standard designed to function on parallel computing architectures. The MPI standard defines the syntax and semantics of library routines that are useful to a wide range of users writing portable message-passing programs in C, C++, and Fortran. There are several open-source MPI implementations, which fostered the development of a parallel software industry, and encouraged development of portable and scalable large-scale parallel applications.
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