Linux Beowulf集群详解：打造高效廉价计算平台

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《Beowulf集群计算与Linux》是由Thomas Sterling所著，由MIT Press出版于2001年10月的一本专业书籍，其国际标准书号为0262692740，共496页。该书是一本清晰、全面的指南，专为Linux环境下的Beowulf集群计算设计。Beowulf集群是一种利用大众市场PC硬件、低成本商业网络技术和开源软件进行并行计算的方法，它在科学研究、工程应用和商业领域中日益受到青睐。随着Beowulf集群的普及，系统的复杂性也随之增加。《Beowulf Cluster Computing with Linux》和《Beowulf Cluster Computing with Windows》两本书旨在为系统用户和管理员提供必要的工具，帮助他们构建和管理最先进水平的Beowulf集群。考虑到PC集群社区的多样性，这两本书分别针对使用Linux和Windows作为节点操作系统的两个群体。每一本书都分为三个独立的部分。首先，第一部分深入介绍了基础的硬件技术、组装过程以及配置方法，让读者对集群硬件有扎实的理解。这部分涵盖了如何选择适合的CPU、内存、存储设备以及网络组件，并详细指导了它们的安装和连接。第二部分则是核心内容，专注于并行编程库的详细阐述。Beowulf集群依赖于高效的并行编程模型，如OpenMP、MPI（Message Passing Interface）等，以实现数据共享、任务分割和通信协调。这部分内容不仅涵盖了编程技术，还包括了性能调优、错误处理和调试策略，确保用户能有效地编写和运行大规模并行程序。最后，第三部分则聚焦于集群的管理和维护。这部分包括了集群监控、故障排除、负载均衡策略、安全性和资源管理的最佳实践，帮助读者确保集群的稳定运行和高效利用。《Beowulf Cluster Computing with Linux》是一本不可或缺的参考文献，无论是对想要入门Beowulf集群的Linux爱好者，还是经验丰富的系统管理员，都能从中获取宝贵的知识和实践经验。通过这本书，读者可以掌握从基础构建到高级应用的方方面面，为构建高性能、低成本的分布式计算平台奠定坚实基础。

At the other end of the cluster spectrum are the constellations. A constellation is a cluster of

large SMP nodes scaled such that the number of processors per node is greater than the

number of such nodes making up the entire system. This is more than an arbitrary distinction.

Performance of a cluster for many applications is derived through program and system

parallelism. For most commodity clusters and Beowulf systems, the primary parallelism

exploited is the internode parallelism. But for clusters, the primary parallelism is intranode,

meaning most of the parallelism used is within the node. Generally, processors within an

SMP node are more tightly coupled through shared memory and can exploit finer-grained

parallelism than can Beowulf clusters. But shared-memory systems require the use of a

different programming model from that of distributed-memory systems, and therefore

programming constellations may prove rather different from programming Beowulf clusters

for optimal performance. Constellations are usually restricted to the largest systems.

1.2 Opportunities and Advantages

Commodity clusters and Beowulf-class systems bring many advantages to scalable parallel

computing, opening new opportunities for users and application domains. Many of these

advantages are a consequence of superior price/performance over many other types of

system of comparable peak capabilities. But other important attributes exhibited by clusters

are due to the nature of their structure and method of implementation. Here we highlight and

expand on these, both to motivate the deployment and to guide the application of

Beowulf-class systems for myriad purposes.

Capability Scaling. More than even cost effectiveness, a Beowulf system's principle attribute

is its scalability. Through the aggregation of commercial off-the-shelf components, ensembles

of specific resources deemed critical to a particular mode of operation can be integrated to

provide a degree of capability not easily acquired through other means. Perhaps most well

known in high-end computing circles is peak performance measured in flops (floating-point

operations per second). Even modest Beowulf systems can attain a peak performance

between 10 and 100 gigaflops. The largest commodity cluster under development will

achieve 30 teraflops peak performance. But another important capability is mass storage,

usually through collections of hard disk drives. Large commodity disks can contain more than

100 gigabytes, but commercial database and scientific data-intensive applications both can

demand upwards of 100 terabytes of on-line storage. In addition, certain classes of memory

intensive applications such as those manipulating enormous matrices of multivariate data can

be processed effectively only if sufficient hardware main memory is brought to bear on the

problem. Commodity clusters provide one method of accumulating sufficient DRAM (dynamic

random access memory) in a single composite system for these large datasets. We note that

while clusters enable aggregation of resources, they do so with limited coupling, both logical

and physical, among the constituent elements. This fragmentation within integrated systems

can negatively impact performance and ease of use.

Convergence Architecture. Not anticipated by its originators, commodity clusters and

Beowulf-class systems have evolved into what has become the de facto standard for parallel

computer structure, having converged on a communitywide system architecture. Since the

mid-1970s, the high-performance computing industry has dragged its small user and

customer base through a series of often-disparate parallel architecture types, requiring major

software rework across successive generations. These changes were often a consequence

of individual vendor decisions and resulted in low customer confidence and a strong reticence

to invest in porting codes to a system that could easily be obsolete before the task was

complete and incompatible with any future generation systems. Commodity clusters

employing communitywide message-passing libraries offer a common structure that crosses

vendor boundaries and system generations, ensuring software investment longevity and

providing customer confidence. Through the evolution of clusters, we have witnessed a true

convergence of parallel system architectures, providing a shared framework in which

hardware and software suppliers can develop products with the assurance of customer

acceptance and application developers can devise advanced user programs with the

confidence of continued support from vendors.

Price/Performance. No doubt the single most widely recognized attribute of Beowulf-class

cluster systems is their exceptional cost advantage compared with other parallel computers.

For many (but not all) user applications and workloads, Beowulf clusters exhibit a

performance-to-cost advantage of as much as an order of magnitude or more compared with

massively parallel processors (MPPs) and distributed shared-memory systems of equivalent

scale. Today, the cost of Beowulf hardware is approaching one dollar per peak megaflops

using consumer-grade computing nodes. The implication of this is far greater than merely the

means of saving a little money. It has caused a revolution in the application of

high-performance computing to a range of problems and users who would otherwise be

unable to work within the regime of supercomputing. It means that for the first time,

computing is playing a role in industry, commerce, and research unaided by such technology.

The low cost has made Beowulfs ideal for educational platforms, enabling the training in

parallel computing principles and practices of many more students than previously possible.

More students are now learning parallel programming on Beowulf-class systems than all

other types of parallel computer combined.

Flexibility of Configuration and Upgrade. Depending on their intended user and

application base, clusters can be assembled in a wide array of configurations, with very few

constraints imposed by commercial vendors. For those systems configured at the final site by

the intended administrators and users, a wide choice of components and structures is

available, making possible a broad range of systems. Where clusters are to be dedicated to

specific workloads or applications, the system structure can be optimized for the required

capabilities and capacities that best suit the nature of the problem being computed. As new

technologies emerge or additional financial resources are available, the flexibility with which

clusters are imbued is useful for upgrading existing systems with new component

technologies as a midlife "kicker" to extend the life and utility of a system by keeping it

current.

Technology Tracking. New technologies most rapidly find their way into those products

likely to provide the most rapid return: mainstream high -end personal computers and SMP

servers. Only after substantial lag time might such components be incorporated into MPPs.

Clustering, however, provides an immediate path to integration of the latest technologies,

even those that may never be adopted by other forms of high-performance computer

systems.

High Availability. Clusters provide multiple redundant identical resources that, if managed

correctly, can provide continued system operation through graceful degradation even as

individual components fail.

Personal Empowerment. Because high-end cluster systems are derived from readily

available hardware and software components, installation sites, their system administrators,

and users have more control over the structure, elements, operation, and evolution of this

system class than over any other system. This sense of control and flexibility has provided a

strong attractor to many, especially those in the research community, and has been a

significant motivation for many installations.

Development Cost and Time. The emerging cluster industry is being fueled by the very low

cost of development and the short time to product delivery. Based on existing computing and

networking products, vendor-supplied commodity clusters can be developed through basic

systems integration and engineering, with no component design required. Because the

constituent components are manufactured for a much larger range of user purposes than is

the cluster market itself, the cost to the supplier is far lower than custom elements would

otherwise be. Thus commodity clusters provide vendors with the means to respond rapidly to

diverse customer needs, with low cost to first delivery.

1.3 A Short History

Cluster computing originated within a few years of the inauguration of the modern electronic

stored-program digital computer. SAGE was a cluster system built for NORAD under Air

Force contract by IBM in the 1950s based on the MIT Whirlwind computer architecture. Using

vacuum tube and core memory technologies, SAGE consisted of a number of separate

standalone systems cooperating to manage early warning detection of hostile airborne

intrusion of the North American continent. Early commercial applications of clusters employed

paired loosely coupled computers, with one computer performing user jobs while the other

managed various input/output devices.

Breakthroughs in enabling technologies occurred in the late 1970s, both in hardware and

software, which were to have significant long -term effects on future cluster computing. The

first generations of microprocessors were designed with the initial development of VLSI (very

large scale integration) technology, and by the end of the decade the first workstations and

personal computers were being marketed. The advent of Ethernet provided the first widely

used local area network technology, creating an industry standard for a modestly priced

multidrop interconnection medium and data transport layer. Also at this time, the multitasking

Unix operating system was created at AT&T Bell Labs and extended with virtual memory and

network interfaces at the University of California-Berkeley. Unix was adopted in its various

commercial and public domain forms by the scientific and technical computing community as

the principal environment for a wide range of computing system classes from scientific

workstations to supercomputers.

During the decade of the 1980s, increased interest in the potential of cluster computing was

marked by important experiments in research and industry. A collection of 160 interconnected

Apollo workstations was employed as a cluster to perform certain computational tasks by the

National Security Agency. Digital Equipment Corporation developed a system comprising

interconnected VAX 11/750 computers, coining the term "cluster" in the process. In the area

of software, task management tools for employing workstation farms were developed, most

notably the Condor software package from the University of Wisconsin. Different strategies

for parallel processing were explored during this period by the computer science research

community. From this early work came the communicating sequential processes model more

commonly referred to as the message-passing model, which has come to dominate much of

cluster computing today.

An important milestone in the practical application of the message-passing model was the

development of PVM (Parallel Virtual Machine), a library of linkable functions that could allow

routines running on separate but networked computers to exchange data and coordinate their

operation. PVM (developed by Oak Ridge National Laboratory, Emery University, and the

University of Tennessee) was the first widely deployed distributed software system available

across different platforms. By the beginning of the 1990s, a number of sites were

experimenting with clusters of workstations. At the NASA Lewis Research Center, a small

cluster of IBM workstations was used to simulate the steady-state behavior of jet aircraft

engines in 1992. The NOW (network of workstations) project at UC Berkeley began operating

the first of several clusters there in 1993, which led to the first cluster to be entered on the

Top500 list of the world's most powerful computers. Also in 1993, Myrinet, one of the first

commercial system area networks, was introduced for commodity clusters, delivering

improvements in bandwidth and latency an order of magnitude better than the Fast Ethernet

local area network (LAN) most widely used for the purpose at that time.

The first Beowulf-class PC cluster was developed at the NASA Goddard Space Flight center

in 1994 using early releases of the Linux operating system and PVM running on 16 Intel 100

MHz 80486-based personal computers connected by dual 10 Mbps Ethernet LANs. The

Beowulf project developed the necessary Ethernet driver software for Linux and additional

low-level cluster management tools and demonstrated the performance and cost

effectiveness of Beowulf systems for real-world scientific applications. That year, based on

experience with many other message-passing software systems, the first Message -Passing

Interface (MPI) standard was adopted by the parallel computing community to provide a

uniform set of message-passing semantics and syntax. MPI has become the dominant

parallel computing programming standard and is supported by virtually all MPP and cluster

system vendors. Workstation clusters running Sun Microsystems Solaris operating system

and NCSA's PC cluster running the Microsoft NT operating system were being used for

real-world applications.

In 1996, the DOE Los Alamos National Laboratory and the California Institute of Technology

with the NASA Jet Propulsion Laboratory independently demonstrated sustained

performance of over 1 Gflops for Beowulf systems costing under $50,000 and was awarded

the Gordon Bell Prize for price/performance for this accomplishment. By 1997, Beowulf-class

systems of over a hundred nodes had demonstrated sustained performance of greater than

10 Gflops, with a Los Alamos system making the Top500 list. By the end of the decade, 28

clusters were on the Top500 list with a best performance of over 200 Gflops. In 2000, both

DOE and NSF announced awards to Compaq to implement their largest computing facilities,

both clusters of 30 Tflops and 6 Tflops, respectively.

1.4 Elements of a Cluster

A Beowulf cluster comprises numerous components of both hardware and software. Unlike

pure closed-box turnkey mainframes, servers, and workstations, the user or hosting

organization has considerable choice in the system architecture of a cluster, whether it is to

be assembled on site from parts or provided by a systems integrator or vendor. A Beowulf

cluster system can be viewed as being made up of four major components, two hardware and

two software. The two hardware components are the compute nodes that perform the work

and the network that interconnects the node to form a single system. The two software

components are the collection of tools used to develop user parallel application programs and

the software environment for managing the parallel resources of the Beowulf cluster. The

specification of a Beowulf cluster reflects user choices in each of these domains and

determines the balance of cost, capacity, performance, and usability of the system.

The hardware node is the principal building block of the physical cluster system. After all, it is

the hardware node that is being clustered. The node incorporates the resources that provide

both the capability and capacity of the system. Each node has one or more microprocessors

that provide the computing power of the node combined on the node's motherboard with the

DRAM main memory and the I/O interfaces. In addition the node will usually include one or

more hard disk drives for persistent storage and local data buffering although some clusters

employ nodes that are diskless to reduce both cost and power consumption as well as

increase reliability.

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