High Performance Linux Clusters with OSCAR, Rocks, OpenMosix, and MPI

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Computing speed isn't just a convenience. Faster computers allow us to solve larger problems, and to find solutions more quickly, with greater accuracy, and at a lower cost. All this adds up to a competitive advantage. In the sciences, this may mean the difference between being the first to publish and not publishing. In industry, it may determine who's first to the patent office.

Traditional high-performance clusters have proved their worth in a variety of uses—from predicting the weather to industrial design, from molecular dynamics to astronomical modeling. High-performance computing (HPC) has created a new approach to science—modeling is now a viable and respected alternative to the more traditional experiential and theoretical approaches.

Clusters are also playing a greater role in business. High performance is a key issue in data mining or in image rendering. Advances in clustering technology have led to high-availability and load-balancing clusters. Clustering is now used for mission-critical applications such as web and FTP servers. For example, Google uses an ever-growing cluster composed of tens of thousands of computers.

Because of the expanding role that clusters are playing in distributed computing, it is worth considering this question briefly. There is a great deal of ambiguity, and the terms used to describe clusters and distributed computing are often used inconsistently. This chapter doesn't provide a detailed taxonomy—it doesn't include a discussion of Flynn's taxonomy or of cluster topologies. This has been done quite well a number of times and too much of it would be irrelevant to the purpose of this book. However, this chapter does try to explain the language used. If you need more general information, see the Appendix A for other sources. High Performance Computing, Second Edition (O'Reilly), by Dowd and Severance is a particularly readable introduction.

When computing, there are three basic approaches to improving performance—use a better algorithm, use a faster computer, or divide the calculation among multiple computers. A very common analogy is that of a horse-drawn cart. You can lighten the load, you can get a bigger horse, or you can get a team of horses. (We'll ignore the option of going into therapy and learning to live with what you have.) Let's look briefly at each of these approaches.

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Because of the expanding role that clusters are playing in distributed computing, it is worth considering this question briefly. There is a great deal of ambiguity, and the terms used to describe clusters and distributed computing are often used inconsistently. This chapter doesn't provide a detailed taxonomy—it doesn't include a discussion of Flynn's taxonomy or of cluster topologies. This has been done quite well a number of times and too much of it would be irrelevant to the purpose of this book. However, this chapter does try to explain the language used. If you need more general information, see the Appendix A for other sources. High Performance Computing, Second Edition (O'Reilly), by Dowd and Severance is a particularly readable introduction.

When computing, there are three basic approaches to improving performance—use a better algorithm, use a faster computer, or divide the calculation among multiple computers. A very common analogy is that of a horse-drawn cart. You can lighten the load, you can get a bigger horse, or you can get a team of horses. (We'll ignore the option of going into therapy and learning to live with what you have.) Let's look briefly at each of these approaches.

First, consider what you are trying to calculate. All too often, improvements in computing hardware are taken as a license to use less efficient algorithms, to write sloppy programs, or to perform meaningless or redundant calculations rather than carefully defining the problem. Selecting appropriate algorithms is a key way to eliminate instructions and speed up a calculation. The quickest way to finish a task is to skip it altogether.

If you need only a modest improvement in performance, then buying a faster computer may solve your problems, provided you can find something you can afford. But just as there is a limit on how big a horse you can buy, there are limits on the computers you can buy. You can expect rapidly diminishing returns when buying faster computers. While there are no hard and fast rules, it is not unusual to see a quadratic increase in cost with a linear increase in performance, particularly as you move away from commodity technology.

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Originally, "clusters" and "high-performance computing" were synonymous. Today, the meaning of the word "cluster" has expanded beyond high-performance to include high-availability (HA) clusters and load-balancing (LB) clusters . In practice, there is considerable overlap among these—they are, after all, all clusters. While this book will focus primarily on high-performance clusters, it is worth taking a brief look at high-availability and load-balancing clusters.

High-availability clusters, also called failover clusters, are often used in mission-critical applications. If you can't afford the lost business that will result from having your web server go down, you may want to implement it using a HA cluster. The key to high availability is redundancy. An HA cluster is composed of multiple machines, a subset of which can provide the appropriate service. In its purest form, only a single machine or server is directly available—all other machines will be in standby mode. They will monitor the primary server to insure that it remains operational. If the primary server fails, a secondary server takes its place.

The idea behind a load-balancing cluster is to provide better performance by dividing the work among multiple computers. For example, when a web server is implemented using LB clustering, the different queries to the server are distributed among the computers in the clusters. This might be accomplished using a simple round-robin algorithm. For example, Round-Robin DNS could be used to map responses to DNS queries to the different IP addresses. That is, when a DNS query is made, the local DNS server returns the addresses of the next machine in the cluster, visiting machines in a round-robin fashion. However, this approach can lead to dynamic load imbalances. More sophisticated algorithms use feedback from the individual machines to determine which machine can best handle the next task.

Keep in mind, the term "load-balancing" means different things to different people. A high-performance cluster used for scientific calculation and a cluster used as a web server would likely approach load-balancing in entirely different ways. Each application has different critical requirements.

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While the term parallel is often used to describe clusters, they are more correctly described as a type of distributed computing . Typically, the term parallel computing refers to tightly coupled sets of computation. Distributed computing is usually used to describe computing that spans multiple machines or multiple locations. When several pieces of data are being processed simultaneously in the same CPU, this might be called a parallel computation, but would never be described as a distributed computation. Multiple CPUs within a single enclosure might be used for parallel computing, but would not be an example of distributed computing. When talking about systems of computers, the term parallel usually implies a homogenous collection of computers, while distributed computing typically implies a more heterogeneous collection. Computations that are done asynchronously are more likely to be called distributed than parallel. Clearly, the terms parallel and distributed lie at either end of a continuum of possible meanings. In any given instance, the exact meanings depend upon the context. The distinction is more one of connotations than of clearly established usage.

Since cluster computing is just one type of distributed computing, it is worth briefly mentioning the alternatives. The primary distinction between clusters and other forms of distributed computing is the scope of the interconnecting network and the degree of coupling among the individual machines. The differences are often ones of degree.

Clusters are generally restricted to computers on the same subnetwork or LAN. The term grid computing is frequently used to describe computers working together across a WAN or the Internet. The idea behind the term "grid" is to invoke a comparison between a power grid and a computational grid. A computational grid is a collection of computers that provide computing power as a commodity. This is an active area of research and has received (deservedly) a lot of attention from the National Science Foundation. The most significant differences between cluster computing and grid computing are that computing grids typically have a much larger scale, tend to be used more asynchronously, and have much greater access, authorization, accounting, and security concerns. From an administrative standpoint, if you build a grid, plan on spending a lot of time dealing with security-related issues. Grid computing has the potential of providing considerably more computing power than individual clusters since a grid may combine a large number of clusters.

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While clusters have a lot to offer, they are not panaceas. There is a limit to how much adding another computer to a problem will speed up a calculation. In the ideal situation, you might expect a calculation to go twice as fast on two computers as it would on one. Unfortunately, this is the limiting case and you can only approach it.

Any calculation can be broken into blocks of code or instructions that can be classified in one of two exclusive ways. Either a block of code can be parallelized and shared among two or more machines, or the code is essentially serial and the instructions must be executed in the order they are written on a single machine. Any code that can't be parallelized won't benefit from any additional processors you may have.

There are several reasons why some blocks of code can't be parallelized and must be executed in a specific order. The most obvious example is I/O, where the order of operations is typically determined by the availability, order, and format of the input and the format of the desired output. If you are generating a report at the end of a program, you won't want the characters or lines of output printed at random.

Another reason some code can't be parallelized comes from the data dependencies within the code. If you use the value of x to calculate the value of y, then you'll need to calculate x before you calculate y. Otherwise, you won't know what value to use in the calculation. Basically, to be able to parallelize two instructions, neither can depend on the other. That is, the order in which the two instructions finish must not matter.

Thus, any program can be seen as a series of alternating sections—sections that can be parallelized and effectively run on different machines interspersed with sections that must be executed as written and that effectively can only be run on a single machine. If a program spends most of its time in code that is essentially serial, parallel processing will have limited value for this code. In this case, you will be better served with a faster computer than with parallel computers. If you can't change the algorithm, big iron is the best approach for this type of problem.

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The material covered in this book reflects three of my biases, of which you should be aware. I have tried to write a book to help people get started with clusters. As such, I have focused primarily on mainstream, high-performance computing, using open source software. Let me explain why.

First, there are many approaches and applications for clusters. I do not believe that it is feasible for any book to address them all, even if a less-than-exhaustive approach is used. In selecting material for this book, I have tried to use the approaches and software that are the most useful for the largest number of people. I feel that it is better to cover a limited number of approaches than to try to say too much and risk losing focus. However, I have tried to justify my decisions and point out options along the way so that if your needs don't match my assumptions, you'll at least have an idea where to start looking.

Second, in keeping with my goal of addressing mainstream applications of clusters, the book primarily focuses on high-performance computing. This is the application from which clusters grew and remains one of their dominant uses. Since high availability and load balancing tend to be used with mission-critical applications, they are beyond the scope of a book focusing on getting started with clusters. You really should have some basic experience with generic clusters before moving on to such mission-critical applications. And, of course, improved performance lies at the core of all the other uses for clusters.

Finally, I have focused on open source software. There are a number of proprietary solutions available, some of which are excellent. But given the choice between comparable open source software and proprietary software, my preference is for open source. For clustering, I believe that high-quality, robust open source software is readily available and that there is little justification for considering proprietary software for most applications.

While I'll cover the basics of clusters here, you would do well to study the specifics of clusters that closely match your applications as well. There are a number of well-known clusters that have been described in detail. A prime example is Google, with literally tens of thousands of computers. Others include clusters at Fermilab, Argonne National Laboratory (Chiba City cluster), and Oak Ridge National Laboratory. Studying the architecture of clusters similar to what you want to build should provide additional insight. Hopefully, this book will leave you well prepared to do just that.

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This chapter is an overview of cluster planning. It begins by introducing four key steps in developing a design for a cluster. Next, it presents several questions you can ask to help you determine what you want and need in a cluster. Finally, it briefly describes some of the software decisions you'll make and how these decisions impact the overall architecture of the cluster. In addition to helping people new to clustering plan the critical foundations of their cluster, the chapter serves as an overview of the software described in the book and its uses.

Designing a cluster entails four sets of design decisions. You should:

Determine the overall mission for your cluster.
Select a general architecture for your cluster.
Select the operating system, cluster software, and other system software you will use.
Select the hardware for the cluster.

While each of these tasks, in part, depends on the others, the first step is crucial. If at all possible, the cluster's mission should drive all other design decisions. At the very least, the other design decisions must be made in the context of the cluster's mission and be consistent with it.

Selecting the hardware should be the final step in the design, but often you won't have as much choice as you would like. A number of constraints may drive you to select the hardware early in the design process. The most obvious is the need to use recycled hardware or similar budget constraints. Chapter 3 describes hardware consideration is greater detail.

Defining what you want to do with the cluster is really the first step in designing it. For many clusters, the mission will be clearly understood in advance. This is particularly true if the cluster has a single use or a few clearly defined uses. However, if your cluster will be an open resource, then you'll need to anticipate potential uses. In that case, the place to start is with your users.

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Designing a cluster entails four sets of design decisions. You should:

Determine the overall mission for your cluster.
Select a general architecture for your cluster.
Select the operating system, cluster software, and other system software you will use.
Select the hardware for the cluster.

While each of these tasks, in part, depends on the others, the first step is crucial. If at all possible, the cluster's mission should drive all other design decisions. At the very least, the other design decisions must be made in the context of the cluster's mission and be consistent with it.

Selecting the hardware should be the final step in the design, but often you won't have as much choice as you would like. A number of constraints may drive you to select the hardware early in the design process. The most obvious is the need to use recycled hardware or similar budget constraints. Chapter 3 describes hardware consideration is greater detail.

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Defining what you want to do with the cluster is really the first step in designing it. For many clusters, the mission will be clearly understood in advance. This is particularly true if the cluster has a single use or a few clearly defined uses. However, if your cluster will be an open resource, then you'll need to anticipate potential uses. In that case, the place to start is with your users.

While you may think you have a clear idea of what your users will need, there may be little semblance between what you think they should need and what they think they need. And while your assessment may be the correct one, your users are still apt to be disappointed if the cluster doesn't live up to their expectations. Talk to your users.

You should also keep in mind that clusters have a way of evolving. What may be a reasonable assessment of needs today may not be tomorrow. Good design is often the art of balancing today's resources with tomorrow's needs. If you are unsure about your cluster's mission, answering the following questions should help.

In designing a cluster, you must take into consideration the needs of all users. Ideally this will include both the potential users as well as the obvious early adopters. You will need to anticipate any potential conflicting needs and find appropriate compromises.

The best way to avoid nasty surprises is to include representative users in the design process. If you have only a few users, you can easily poll the users to see what you need.

If you have a large user base, particularly one that is in flux, you will need to anticipate all reasonable, likely needs. Generally, this will mean supporting a wider range of software. For example, if you are the sole user and you only use one programming language and parallel programming library, there is no point in installing others. If you have dozens of users, you'll probably need to install multiple programming languages and parallel programming libraries.

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Once you have established the mission for your cluster, you can focus on its architecture and select the software. Most high-performance clusters use an architecture similar to that shown in Figure 1-5. The software described in this book is generally compatible with that basic architecture. If this does not match the mission of your cluster, you still may be able to use many of the packages described in this book, but you may need to make a few adaptations.

Putting together a cluster involves the selection of a variety of software. The possibilities are described briefly here. Each is discussed in greater detail in subsequent chapters in this book.

One of the first selections you will probably want to make is the operating system, but this is actually the final software decision you should make. When selecting an operating system, the fundamental question is compatibility. If you have a compelling reason to use a particular piece of software and it will run only under a single operating system, the choice has been made for you. For example, openMosix uses extensions to the Linux kernel, so if you want openMosix, you must use Linux. Provided the basic issue of compatibility has been met, the primary reasons to select a particular operating system are familiarity and support. Stick with what you know and what's supported.

All the software described in this book is compatible with Linux. Most, but not all, of the software will also work nicely with other Unix systems. In this book, we'll be assuming the use of Linux. If you'd rather use BSD or Solaris, you'll probably be OK with most of the software, but be sure to check its compatibility before you make a commitment. Some of the software, such as MPICH, even works with Windows.

There is a natural human tendency to want to go with the latest available version of an operating system, and there are some obvious advantages to using the latest release. However, compatibility should drive this decision as well. Don't expect clustering software to be immediately compatible with the latest operating system release. Compatibility may require that you use an older release. (For more on Linux, see Chapter 4.)

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If installing all of this software sounds daunting, don't panic. There are a couple of options you can consider. For permanent clusters there are, for lack of a better name, cluster kits, software packages that automate the installation process. A cluster kit provides all the software you are likely to need in a single distribution.

Cluster kits tend to be very complete. For example, the OSCAR distribution contains both PVM and two versions of MPI. If some software isn't included, you can probably get by without it. Another option, described in the next section, is a CD-ROM-based cluster.

Cluster kits are designed to be turnkey solutions. Short of purchasing a prebuilt, preinstalled proprietary cluster, a cluster kit is the simplest approach to setting up a full cluster. Configuration parameters are largely preset by people who are familiar with the software and how the different pieces may interact. Once you have installed the kit, you have a functioning cluster. You can focus on using the software rather than installing it. Support groups and mailing lists are generally available.

Some kits have a Linux distribution included in the package (e.g., Rocks), while others are installed on top of an existing Linux installation (e.g., OSCAR). Even if Linux must be installed first, most of the configuration and the installation of needed packages will be done for you.

There are two problems with using cluster kits. First, cluster kits do so much for you that you can lose touch with your cluster, particularly if everything is new to you. Initially, you may not understand how the cluster is configured, what customizations have been made or are possible, or even what has been installed. Even making minor changes after installing a kit can create problems if you don't understand what you have. Ironically, the more these kits do for you, the worse this problem may be. With a kit, you may get software you don't want to deal with—software your users may expect you to maintain and support. And when something goes wrong, as it will, you may be at a loss about how to deal with it.

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If you just want to learn about clusters, only need a cluster occasionally, or can't permanently install a cluster, you might consider one of the CD-ROM-based clusters. With these, you create a set of bootable CD-ROMs, sometimes called "live filesystem" CDs. When you need the cluster, you reboot your available systems using the CD-ROMs, do a few configuration tasks, and start using your cluster. The cluster software is all available from the CD-ROM and the computers' hard disks are unchanged. When you are done, you simply remove the CD-ROM and reboot the system to return to the operating system installed on the hard disk. Your cluster persists until you reboot.

Clearly, this is not an approach to use for a high-availability or mission-critical cluster, but it is a way to get started and learn about clusters. It is a viable way to create a cluster for short-term use. For example, if a computer lab is otherwise idle over the weekend, you could do some serious calculations using this approach.

There are some significant difficulties with this approach, most notably problems with storage. It is possible to work around this problem by using a hybrid approach—setting up a dedicated system for storage and using the CD-ROM-based systems as compute-only nodes.

Several CD-ROM-based systems are available. You might look at ClusterKnoppix, http://bofh.be/clusterknoppix/, or Bootable Cluster CD (BCCD), http://bccd.cs.uni.edu/. The next subsection, a very brief description of BCCD, should give you the basic idea of how these systems work.

BCCD was developed by Paul Gray as an educational tool. If you want to play around with a small cluster, BCCD is a very straightforward way to get started. On an occasional basis, it is a viable alternative. What follows is a general overview of running BCCD for the first time.

The first step is to visit the BCCD download site, download an ISO image for a CD-ROM, and use it to burn a CD-ROM for each system. (Creating CD-ROMs from ISO images is briefly discussed in Chapter 4.) Next, boot each machine in your cluster from the CD-ROM. You'll need to answer a few questions as the system boots. First, you'll enter a password for the default user,

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Once you have your cluster running, you'll probably want to run a benchmark or two just to see how well it performs. Unfortunately, benchmarking is, at best, a dark art. In practice, sheep entrails may give better results.

Often the motivation for benchmarks is hubris—the desire to prove your system is the best. This can be crucial if funding is involved, but otherwise is probably a meaningless activity and a waste of time. You'll have to judge for yourself.

Keep in mind that a benchmark supplies a single set of numbers that is very difficult to interpret in isolation. Benchmarks are mostly useful when making comparisons between two or more closely related configurations on your own cluster.

There are at least three reasons you might run benchmarks. First, a benchmark will provide you with a baseline. If you make changes to your cluster or if you suspect problems with your cluster, you can rerun the benchmark to see if performance is really any different. Second, benchmarks are useful when comparing systems or cluster configurations. They can provide a reasonable basis for selecting between alternatives. Finally, benchmarks can be helpful with planning. If you can run several with differently sized clusters, etc., you should be able to make better estimates of the impact of scaling your cluster.

Benchmarks are not infallible. Consider the following rather simplistic example: Suppose you are comparing two clusters with the goal of estimating how well a particular cluster design scales. Cluster B is twice the size of cluster A. Your goal is to project the overall performance for a new cluster C, which is twice the size of B. If you rely on a simple linear extrapolation based on the overall performance of A and B, you could be grossly misled. For instance, if cluster A has a 30% network utilization and cluster B has a 60% network utilization, the network shouldn't have a telling impact on overall performance for either cluster. But if the trend continues, you'll have a difficult time meeting cluster C's need for 120% network utilization.

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It is tempting to let the hardware dictate the architecture of your cluster. However, unless you are just playing around, you should let the potential uses of the cluster dictate its architecture. This in turn will determine, in large part, the hardware you use. At least, that is how it works in ideal, parallel universes.

In practice, there are often reasons why a less ideal approach might be necessary. Ultimately, most of them boil down to budgetary constraints. First-time clusters are often created from recycled equipment. After all, being able to use existing equipment is often the initial rationale for creating a cluster. Perhaps your cluster will need to serve more than one purpose. Maybe you are just exploring the possibilities. In some cases, such as learning about clusters, selecting the hardware first won't matter too much.

If you are building a cluster using existing, cast-off computers and have a very limited budget, then your hardware selection has already been made for you. But even if this is the case, you will still need to make a number of decisions on how to use your hardware. On the other hand, if you are fortunate enough to have a realistic budget to buy new equipment or just some money to augment existing equipment, you should begin by carefully considering your goals. The aim of this chapter is to guide you through the basic hardware decisions and to remind you of issues you might overlook. For more detailed information on PC hardware, you might consult PC Hardware in a Nutshell (O'Reilly).

While you may have some idea of what you want, it is still worthwhile to review the implications of your choices. There are several closely related, overlapping key issues to consider when acquiring PCs for the nodes in your cluster:

Will you have identical systems or a mixture of hardware?
Will you scrounge for existing computers, buy assembled computers, or buy the parts and assemble your own computers?

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While you may have some idea of what you want, it is still worthwhile to review the implications of your choices. There are several closely related, overlapping key issues to consider when acquiring PCs for the nodes in your cluster:

Will you have identical systems or a mixture of hardware?
Will you scrounge for existing computers, buy assembled computers, or buy the parts and assemble your own computers?
Will you have full systems with monitors, keyboards, and mice, minimal systems, or something in between?
Will you have dedicated computers, or will you share your computers with other users?
Do you have a broad or shallow user base?

This is this most important thing I'll say in this chapter—if at all possible, use identical systems for your nodes. Life will be much simpler. You'll need to develop and test only one configuration and then you can clone the remaining machines. When programming your cluster, you won't have to consider different hardware capabilities as you attempt to balance the workload among machines. Also, maintenance and repair will be easier since you will have less to become familiar with and will need to keep fewer parts on hand. You can certainly use heterogeneous hardware, but it will be more work.

In constructing a cluster, you can scrounge for existing computers, buy assembled computers, or buy the parts and assemble your own. Scrounging is the cheapest way to go, but this approach is often the most time consuming. Usually, using scrounged systems means you'll end up with a wide variety of hardware, which creates both hardware and software problems. With older scrounged systems, you are also more likely to have even more hardware problems. If this is your only option, try to standardize hardware as much as possible. Look around for folks doing bulk upgrades when acquiring computers. If you can find someone replacing a number of computers at one time, there is a good chance the computers being replaced will have been a similar bulk purchase and will be very similar or identical. These could come from a computer laboratory at a college or university or from an IT department doing a periodic upgrade.

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You are going to need some place to put your computers. If you are lucky enough to have a dedicated machine room, then you probably have everything you need. Otherwise, select or prepare a location that provides physical security, adequate power, and adequate heating and cooling. While these might not be issues with a small cluster, proper planning and preparation is essential for large clusters. Keep in mind, you are probably going to be so happy with your cluster that you'll want to expand it. Since small clusters have ways of becoming large clusters, plan for growth from the start.

Since the more computers you have, the more space they will need, plan your layout with wiring, cooling, and physical access in mind. Ignore any of these at your peril. While it may be tempting to stack computers or pack them into large shelves, this can create a lot of problems if not handled with care. First, you may find it difficult to physically access individual computers to make repairs. If the computers are packed too tightly, you'll create heat dissipation problems. And while this may appear to make wiring easier, in practice it can lead to a rat's nest of cables, making it difficult to divide your computers among different power circuits.

From the perspective of maintenance, you'll want to have physical access to individual computers without having to move other computers and with a minimum of physical labor. Ideally, you should have easy access to both the front and back of your computers. If your nodes are headless (no monitor, mouse, or keyboard), it is a good idea to assemble a crash cart. So be sure to leave enough space to both wheel and park your crash cart (and a chair) among your machines.

To prevent overheating, leave a small gap between computers and take care not to obstruct any ventilation openings. (These are occasionally seen on the sides of older computers!) An inch or two usually provides enough space between computers, but watch for signs of overheating.

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This chapter reviews some of the issues involved in setting up a Linux system for use in a cluster. While several key services are described in detail, for the most part the focus is more on the issues and rationales than on specifics. Even if you are an old pro at Linux system administration, you may still want to skim this chapter for a quick overview of the issues as they relate to clusters, particularly the section on configuring services. If you are new to Linux system administration, this chapter will probably seem very terse. What's presented here is the bare minimum a novice system administrator will need to get started. The Appendix A lists additional sources.

This chapter covers material you'll need when setting up the head node and a typical cluster node. Depending on the approach you take, much of this may be done for you. If you are building your cluster from the ground up, you'll need to install the head node, configure the individual services on it, and build at least one compute node. Once you have determined how a compute node should be configured, you can turn to Chapter 8 for a discussion of how to duplicate systems in an efficient manner. It is much simpler with kits like OSCAR and Rocks.

With OSCAR, you'll need to install Linux on the head system, but OSCAR will configure the services for you. It will also build the client, i.e., generate a system image and install it on the compute nodes. OSCAR will configure and install most of the packages you'll need. The key to using OSCAR is to use a version of Linux that is known to be compatible with OSCAR. OSCAR is described in Chapter 6. With Rocks, described in Chapter 7, everything will be done for you. Red Hat Linux comes as part of the Rocks distribution.

This chapter begins with a discussion of selecting a Linux distribution. A general discussion of installing Linux follows. Next, the configuration of relevant network services is described. Finally, there is a brief discussion of security. If you are adding clustering software to an existing collection of workstations, presumably Linux is already installed on your machines. If this is the case, you can probably skim the first couple of sections. But while you won't need to install Linux, you will need to ensure that it is configured correctly and all the services you'll need are available.

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If Linux isn't built into your cluster software, the first step is to decide what distribution and version of Linux you want.

This decision will depend on what clustering software you want to use. It doesn't matter what the "best" distribution of Linux (Red Hat, Debian, SUSE, Mandrake, etc.) or version (7.3, 8.0, 9.0, etc.) is in some philosophical sense if the clustering software you want to use isn't available for that choice. This book uses the Red Hat distribution because the clustering software being discussed was known to work with that distribution. This is not an endorsement of Red Hat; it was just a pragmatic decision.

Keep in mind that your users typically won't be logging onto the compute nodes to develop programs, etc., so the version of Linux used there should be largely irrelevant to the users. While users will be logging onto the head node, this is not a general-purpose server. They won't be reading email, writing memos, or playing games on this system (hopefully). Consequently, many of the reasons someone might prefer a particular distribution are irrelevant.

This same pragmatism should extend to selecting the version as well as the distribution you use. In practice, this may mean using an older version of Linux. There are basically three issues involved in using an older version—compatibility with newer hardware; bug fixes, patches, and continued support; and compatibility with clustering software.

If you are using recycled hardware, using an older version shouldn't be a problem since drivers should be readily available for your older equipment. If you are using new equipment, however, you may run into problems with older Linux releases. The best solution, of course, is to avoid this problem by planning ahead if you are buying new hardware. This is something you should be able to work around by putting together a single test system before buying the bulk of the equipment.

With older versions, many of the problems are known. For bugs, this is good news since someone else is likely to have already developed a fix or workaround. With security holes, this is bad news since exploits are probably well circulated. With an older version, you'll need to review and install all appropriate security patches. If you can isolate your cluster, this will be less of an issue.

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Once you have the basic installation completed, you'll need to configure the system. Many of the tasks are no different for machines in a cluster than for any other system. For other tasks, being part of a cluster impacts what needs to be done. The following subsections describe the issues associated with several services that require special considerations. These subsections briefly recap how to configure and use these services. Remember, most of this will be done for you if you are using a package like OSCAR or Rocks. Still, it helps to understand the issues and some of the basics.

Dynamic Host Configuration Protocol (DHCP) is used to supply network configuration parameters, including IP addresses, host names, and other information to clients as they boot. With clusters, the head node is often configured as a DHCP server and the compute nodes as DHCP clients. There are two reasons to do this. First, it simplifies the installation of compute nodes since the information DHCP can supply is often the only thing that is different among the nodes. Since a DHCP server can handle these differences, the node installation can be standardized and automated. A second advantage of DHCP is that it is much easier to change the configuration of the network. You simply change the configuration file on the DHCP server, restart the server, and reboot each of the compute nodes.

The basic installation is rarely a problem. The DHCP system can be installed as a part of the initial Linux installation or after Linux has been installed. The DHCP server configuration file, typically /etc/dhcpd.conf, controls the information distributed to the clients. If you are going to have problems, the configuration file is the most likely source.

The DHCP configuration file may be created or changed automatically when some cluster software is installed. Occasionally, the changes may not be done optimally or even correctly so you should have at least a reading knowledge of DHCP configuration files. Here is a heavily commented sample configuration file that illustrates the basics. (Lines starting with "#" are comments.)

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Security is always a two-edged sword. Adding security always complicates the configuration of your systems and makes using a cluster more difficult. But if you don't have adequate security, you run the risk of losing sensitive data, losing control of your cluster, having it damaged, or even having to completely rebuild it. Security management is a balancing act, one of trying to figure out just how little security you can get by with.

As previously noted, the usual architecture for a cluster is a set of machines on a dedicated subnet. One machine, the head node, connects this network to the outside world, i.e., the organization's network and the Internet. The only access to the cluster's dedicated subnet is through the head node. None of the compute nodes are attached to any other network. With this model, security typically lies with the head node. The subnet is usually a trust-based open network.

There are several reasons for this approach. With most clusters, the communication network is the bottleneck. Adding layers of security to this network will adversely affect performance. By focusing on the head node, security administration is localized and thus simpler. Typically, with most clusters, any sensitive information resides on the head node, so it is the point where the greatest level of protection is needed. If the compute nodes are not isolated, each one will need to be secured from attack.

This approach also simplifies setting up packet filtering, i.e., firewalls. Incorrectly configured, packet filters can create havoc within your cluster. Determining what traffic to allow can be a formidable challenge when using a number of different applications. With the isolated network approach, you can configure the internal interface to allow all traffic and apply the packet filter only to public interface.

This approach doesn't mean you have a license to be sloppy within the cluster. You should take all reasonable precautions. Remember that you need to protect the cluster not just from external threats but from internal ones as well—whether intentional or otherwise.

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openMosix is software that extends the Linux kernel so that processes can migrate transparently among the different machines within a cluster in order to more evenly distribute the workload. This chapter gives the basics of setting up and using an openMosix cluster. There is a lot more to openMosix than described here, but this should be enough to get you started and keep you running for a while unless you have some very special needs.

Basically, the openMosix software includes both a set of kernel patches and support tools. The patches extend the kernel to provide support for moving processes among machines in the cluster. Typically, process migration is totally transparent to the user. However, by using the tools provided with openMosix, as well as third-party tools, you can control the migration of processes among machines.

Let's look at how openMosix might be used to speed up a set of computationally expensive tasks. Suppose, for example, you have a dozen files to compress using a CPU-intensive program on a machine that isn't part of an openMosix cluster. You could compress each file one at a time, waiting for one to finish before starting the next. Or you could run all the compressions simultaneously by starting each compression in a separate window or by running each compression in the background (ending each command line with an &). Of course, either way will take about the same amount of time and will load down your computer while the programs are running.

However, if your computer is part of an openMosix cluster, here's what will happen: First, you will start all of the processes running on your computer. With an openMosix cluster, after a few seconds, processes will start to migrate from your heavily loaded computer to other idle or less loaded computers in the clusters. (As explained later, because some jobs may finish quickly, it can be counterproductive to migrate too quickly.) If you have a dozen idle machines in the cluster, each compression should run on a different machine. Your machine will have only one compression running on it (along with a little added overhead) so you still may be able to use it. And the dozen compressions will take only a little longer than it would normally take to do a single compression.

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Basically, the openMosix software includes both a set of kernel patches and support tools. The patches extend the kernel to provide support for moving processes among machines in the cluster. Typically, process migration is totally transparent to the user. However, by using the tools provided with openMosix, as well as third-party tools, you can control the migration of processes among machines.

Let's look at how openMosix might be used to speed up a set of computationally expensive tasks. Suppose, for example, you have a dozen files to compress using a CPU-intensive program on a machine that isn't part of an openMosix cluster. You could compress each file one at a time, waiting for one to finish before starting the next. Or you could run all the compressions simultaneously by starting each compression in a separate window or by running each compression in the background (ending each command line with an &). Of course, either way will take about the same amount of time and will load down your computer while the programs are running.

However, if your computer is part of an openMosix cluster, here's what will happen: First, you will start all of the processes running on your computer. With an openMosix cluster, after a few seconds, processes will start to migrate from your heavily loaded computer to other idle or less loaded computers in the clusters. (As explained later, because some jobs may finish quickly, it can be counterproductive to migrate too quickly.) If you have a dozen idle machines in the cluster, each compression should run on a different machine. Your machine will have only one compression running on it (along with a little added overhead) so you still may be able to use it. And the dozen compressions will take only a little longer than it would normally take to do a single compression.

If you don't have a dozen computers, or some of your computers are slower than others, or some are otherwise loaded, openMosix will move the jobs around as best it can to balance the load. Once the cluster is set up, this is all done transparently by the system. Normally, you just start your jobs. openMosix does the rest. On the other hand, if you want to control the migration of jobs from one computer to the next, openMosix

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openMosix originated as a fork from the earlier MOSIX (Multicomputer Operating System for Unix) project. The openMosix project began when the licensing structure for MOSIX moved away from a General Public License. Today, it has evolved into a project in its own right. The original MOSIX project is still quite active under the direction of Amnon Barak (http://www.mosix.org). openMosix is the work of Moshe Bar, originally a member of the MOSIX team, and a number of volunteers. This book focuses on openMosix, but MOSIX is a viable alternative that can be downloaded at no cost.

As noted in Chapter 1, one approach to sharing a computation between processors in a single-enclosure computer with multiple CPUs is symmetric multiprocessor (SMP) computing. openMosix has been described, accurately, as turning a cluster of computers into a virtual SMP machine, with each node providing a CPU. openMosix is potentially much cheaper and scales much better than SMPs, but communication overhead is higher. (openMosix will work with both single-processor systems and SMP systems.) openMosix is an example of what is sometimes called single system image clustering (SSI) since each node in the cluster has a copy of a single operating system kernel.

The granularity for openMosix is the process. Individual programs, as in the compression example, may create the processes, or the processes may be the result of different forks from a single program. However, if you have a computationally intensive task that does everything in a single process (and even if multiple threads are used), then, since there is only one process, it can't be shared among processors. The best you can hope for is that it will migrate to the fastest available machine in the cluster.

Not all processes migrate. For example, if a process only lasts a few seconds (very roughly, less than 5 seconds depending on a number of factors), it will not have time to migrate. Currently, openMosix does not work with multiple processes using shared writable memory, such as web servers. Similarly, processes doing direct manipulation of I/O devices won't migrate. And processes using real-time scheduling won't migrate. If a process has already migrated to another processor and attempts to do any these things, the process will migrate back to its

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Since openMosix is a kernel extension, it won't work with just any kernel. At this time, you are limited to a relatively recent (at least version 2.4.17 or more recent) IA32-compatible Linux kernel. An IA64 port is also available. However, don't expect openMosix to be available for a new kernel the same day a new kernel is released. It takes time to develop patches for a kernel. Fortunately, your choice of Linux distributions is fairly broad. Among others, openMosix has been reported to work on Debian, Gentoo, Red Hat, and SuSe Linux. If you just want to play with it, you might consider Bootable Cluster CD (BCCD), Knoppix, or PlumpOS, three CD-bootable Linux distributions that include openMosix. You'll also need a reasonably fast network and a fair amount of swap space to run openMosix.

To build your openMosix cluster, you need to install an openMosix extended kernel on each of the nodes in the cluster. If you are using a suitable version of Linux and have no other special needs, you may be able to download a precompiled version of the kernel. This will significantly simplify setup. Otherwise, you'll need to obtain a clean copy of the kernel sources, apply the openMosix patches to the kernel source code, recompile the sources, and install the patched kernel. This isn't as difficult as it might sound, but it is certainly more involved than just installing a precompiled kernel. Recompiling the kernel is described in detail later in this chapter. We'll start with precompiled kernels.

While using a precompiled kernel is the easiest way to go, it has a few limitations. The documentation is a little weak with the precompiled kernels, so you won't know exactly what options have been compiled into the kernel without doing some digging. (However, the .config files are available via CVS and the options seem to be reasonable.) If you already have special needs that required recompiling your kernel, e.g., nonstandard hardware, don't expect those needs to go away.

You'll need to use the same version of the patched kernel on all your systems, so choose accordingly. This doesn't mean you must use the same kernel image. For example, you can use different compiles to support different hardware. But all your kernels should have the same version number.

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