High-performance Computing with Beowulf Clusters

PCQ Bureau
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Beowulf is the legendary hero from the Old English poem of

the same name. In this epic tale, Beowulf saves the Danish kingdom of Hrothgar

from two man-eating monsters and a dragon by slaying each.


Beowulf is used today as a metaphor for a new strategy in

high-performance computing that exploits mass-market technologies to overcome

the oppressive costs in time and money of supercomputing, thus freeing people to

devote themselves to their respective disciplines. Ironically, building a

Beowulf cluster is so much fun that scientists and researchers eagerly roll up

their sleeves to undertake the many tedious tasks involved–at least for the

first time they build one.

The concept of Beowulf clusters originated at the Center of

Excellence in Space Data and Information Sciences (CESDIS), located at the NASA

Goddard Space Flight Center in Maryland. The goal in building a Beowulf cluster

was to create a cost-effective parallel computing system from mass-market

commodity, off-the-shelf components to satisfy specific computational

requirements in the earth and space sciences community.

Clusters of commodity computing systems



first Beowulf cluster was built from 16 Intel DX4 processors connected by a

channel-bonded 10 Mbps Ethernet, and it ran Linux. It was an instant success,

demonstrating the concept of using a commodity cluster as an alternative choice

for high-performance computing (HPC). After the success of the first Beowulf

cluster, several more were built by CESDIS using several generations and

families of processors and network interconnects.

At Supercomputing ’96, a supercomputing conference

sponsored by the Association for Computing Machinery (ACM) and the Institute of

Electrical and Electronics Engineers (IEEE), both NASA and the US Department of

Energy demonstrated clusters costing less than $50,000 that achieved greater

than a gigaflop-per-second sustained performance. As of November 1999, the

fastest Beowulf-class cluster, Cplant (Computational Plant) at Sandia National

Laboratory, ranked 44th in the Top 500 supercomputer list.

While many commercial supercomputers use the same processor,

memory, and controller chips that are employed by Beowulf clusters, they also

integrate proprietary gluing technologies (for example, interconnection

networks, special I/O subsystems, and advanced compiler technologies) that

greatly increase cost and development time. On the other hand, Beowulf clusters

only use mass-market components and are not subject to delays and costs from

custom parts and proprietary design.


Logical view of a Beowulf cluster

A Beowulf cluster uses a multicomputer architecture. It

features a parallel computing system that usually consists of one or more master

nodes and one or more compute nodes, or cluster nodes, interconnected via widely

available network interconnects. All of the nodes in a typical Beowulf cluster

are commodity systems–PCs , workstations, or servers–running commodity

software such as Linux.

The master node acts as a server for Network File System (NFS)

and as a gateway to the outside world. As an NFS server, the master node

provides user file space and other common system software to the compute nodes

via NFS. As a gateway, the master node allows users to gain access through it to

the compute nodes. Usually, the master node is the only machine that is also

connected to the outside world using a second network interface card (NIC).


The sole task of the compute nodes is to execute parallel

jobs. In most cases, therefore, the compute nodes do not have keyboards, mice,

video cards, or monitors. All access to the client nodes is provided via remote

connections from the master node. Because compute nodes do not need to access

machines outside the cluster, nor do machines outside the cluster need to access

compute nodes directly, compute nodes commonly use private IP addresses, such as

the or 192. 168.0.0/16 address ranges.

From a user’s perspective, a Beowulf cluster appears as a

Massively Parallel Processor (MPP) system. The most common methods of using the

system are to access the master node either directly or through telnet or remote

login from personal workstations. Once on the master node, users can prepare and

compile their parallel applications, and also spawn jobs on a desired number of

compute nodes in the cluster.

Applications must be written in parallel style and use the

message-passing programming model. Jobs of a parallel application are spawned on

compute nodes, which work collaboratively until finishing the application.

During the execution, compute nodes use standard message-passing middle-ware,

such as Message Passing Interface (MPI) and Parallel Virtual Machine (PVM), to

exchange information.


Applications for Beowulf clusters

Since a Beowulf cluster is an MPP system, it suits

applications that can be partitioned into tasks, which can then be executed

concurrently by a number of processors. These applications range from high-end,

floating-point intensive scientific and engineering problems to commercial

data-intensive tasks. Uses of these applications include ocean and climate

modeling for prediction of temperature and precipitation, seismic analysis for

oil exploration, aerodynamic simulation for motor and aircraft design, and

molecular modeling for biomedical research.

Design choices for building a Beowulf cluster


Beowulf is not a special software package, new network

topology, or the latest Linux kernel pack. Beowulf is a concept of clustering

commodity computers to form a parallel, virtual supercomputer. You can easily

build a unique Beowulf cluster from those components that you consider most

appropriate. No Beowulf cluster exists that is general enough to satisfy

everyone’s needs–there are numerous design choices for building a Beowulf

cluster. The bottomline is that a Beowulf cluster must be built to perform your

applications the best (see box "Design considerations for Beowulf


The next steps for Beowulf clusters

Beowulf-class clusters have successfully penetrated the HPC

market, which has traditionally been dominated by expensive, "big iron

boxes" from IBM, Cray, Silicon Graphics, and others. Beowulf clusters

provide an alternative for building cost-effective, high-performance parallel

computing systems.

We expect Beowulf to continue its rapid deployment to fulfill

the need for technical computation. We also anticipate that more commercial

parallel applications such as applications for computational finance and data

mining will find their way into Beowulf clusters.

Jenwei Hsieh is a lead engineer on the Internet

Infrastructure Technologies team and Cluster Development

Group at Dell. Reprinted with permission from "Dell Power Solutions"