Computer-System Architecture - Operating System

Computer-System Architecture

We introduced the general structure of a typical computer system. A computer system can be organized in a number of different ways, which we can categorize roughly according to the number of general-purpose processors used.

1.3.1 Single-Processor Systems 

Until recently, most computer systems used a single processor. On a single-processor system, there is one main CPU capable of executing a general-purpose instruction set, including instructions from user processes. Almost all single processor systems have other special-purpose processors as well. They may come in the form of device-specific processors, such as disk, keyboard, and graphics controllers; or, on mainframes, they may come in the form of more general-purpose processors, such as I/O processors that move data rapidly among the components of the system. All of these special-purpose processors run a limited instruction set and do not run user processes.

Sometimes, they are managed by the operating system, in that the operating system sends them information about their next task and monitors their status. For example, a disk-controller microprocessor receives a sequence of requests from the main CPU and implements its own disk queue and scheduling algorithm. This arrangement relieves the main CPU of the overhead of disk scheduling. PCs contain a microprocessor in the keyboard to convert the keystrokes into codes to be sent to the CPU. 

In other systems or circumstances, special-purpose processors are low-level components built into the hardware. The operating system cannot communicate with these processors; they do their jobs autonomously. The use of special-purpose microprocessors is common and does not turn a single-processor system into a multiprocessor. If there is only one general-purpose CPU, then the system is a single-processor system.

1.3.2 Multiprocessor Systems

Within the past several years, multiprocessor systems (also known as parallel systems or multicore systems) have begun to dominate the landscape of computing. Such systems have two or more processors in close communication, sharing the computer bus and sometimes the clock, memory, and peripheral devices. Multiprocessor systems first appeared prominently appeared in servers and have since migrated to desktop and laptop systems. Recently, multiple processors have appeared on mobile devices such as smartphones and tablet computers. Multiprocessor systems have three main advantages: 

1. Increased throughput. By increasing the number of processors, we expect to get more work done in less time. The speed-up ratio with N processors is not N, however; rather, it is less than N. When multiple processors cooperate on a task, a certain amount of overhead is incurred in keeping all the parts working correctly. This overhead, plus contention for shared resources, lowers the expected gain from additional processors. Similarly, N programmers working closely together do not produce N times the amount of work a single programmer would produce.

2. Economy of scale. Multiprocessor systems can cost less than equivalent multiple single-processor systems because they can share peripherals, mass storage, and power supplies. If several programs operate on the same set of data, it is cheaper to store those data on one disk and to have all the processors share them than to have many computers with local disks and many copies of the data. 

3. Increased reliability. If functions can be distributed properly among several processors, then the failure of one processor will not halt the system, only slow it down. If we have ten processors and one fails, then each of the remaining nine processors can pick up a share of the work of the failed processor. Thus, the entire system runs only 10 percent slower, rather than failing altogether. 

Increased reliability of a computer system is crucial in many applications. The ability to continue providing service proportional to the level of surviving hardware is called graceful degradation. Some systems go beyond graceful degradation and are called fault-tolerant because they can suffer a failure of any single component and still continue operation. Fault tolerance requires a mechanism to allow the failure to be detected, diagnosed, and, if possible, corrected.

The HP NonStop (formerly Tandem) system uses both hardware and software duplication to ensure continued operation despite faults. The system consists of multiple pairs of CPUs, working in lockstep. Both processors in the pair execute each instruction and compare the results. If the results differ, then one CPU of the pair is at fault, and both are halted. The process that was being executed is then moved to another pair of CPUs, and the instruction that failed is restarted. This solution is expensive since it involves special hardware and considerable hardware duplication.

The multiple-processor systems in use today are of two types. Some systems use asymmetric multiprocessing, in which each processor is assigned a specific task. A boss processor controls the system; the other processors either look to the boss for instruction or have predefined tasks. This scheme defines a boss–worker relationship. The boss processor schedules and allocates work to the worker processors. The most common systems use symmetric multiprocessing (SMP), in which each processor performs all tasks within the operating system. 

SMP means that all processors are peers; no boss–worker relationship exists between processors. The figure illustrates a typical SMP architecture. Notice that each processor has its own set of registers, as well as a private—or local —cache. However, all processors share physical memory. An example of an SMP system is AIX, a commercial version of UNIX designed by IBM. An AIX system can be configured to employ dozens of processors. 

The benefit of this model is that many processes can run simultaneously N processes can run if there are N CPUs—without causing performance to deteriorate significantly. However, we must carefully control I/O to ensure that the data reach the appropriate processor. Also, since the CPUs are separate, one may be sitting idle while another is overloaded, resulting in inefficiencies. These inefficiencies can be avoided if the processors share certain data structures. 

A multiprocessor system of this form will allow processes and resources such as memory to be shared dynamically among the various processors and can lower the variance among the processors. Such a system must be written carefully, as we shall see in Chapter 5. Virtually all modern operating systems including Windows, Mac OS X, and Linux now provide support for SMP. The difference between symmetric and asymmetric multiprocessing may result from either hardware or software.

Special hardware can differentiate the multiple processors, or the software can be written to allow only one boss and multiple workers. For instance, Sun Microsystems’ operating system SunOS Version 4 provided asymmetric multiprocessing, whereas Version 5 (Solaris) is symmetric on the same hardware. Multiprocessing adds CPUs to increase computing power. If the CPU has an integrated memory controller, then adding CPUs can also increase the amount of memory addressable in the system. Either way, multiprocessing can cause a system to change its memory access model from uniform memory access (UMA) to non-uniform memory access (NUMA). 

UMA is defined as the situation in which access to any RAM from any CPU takes the same amount of time. With NUMA, some parts of memory may take longer to access than other parts, creating a performance penalty. Operating systems can minimize the NUMA penalty through resource management, as discussed in Section 9.5.4. A recent trend in CPU design is to include multiple computing cores on a single chip. Such multiprocessor systems are termed multicore. They can be more efficient than multiple chips with single cores because on-chip communication is faster than between-chip communication. 

In addition, one chip with multiple cores uses significantly less power than multiple single-core chips. It is important to note that while multicore systems are multiprocessor systems, not all multiprocessor systems are multicore. In our coverage of multiprocessor systems throughout this text, unless we state otherwise, we generally use the more contemporary term multicore, which excludes some multiprocessor systems. In a dual-core design with two cores on the same chip. In this design, each core has its own register set as well as its own local cache. Other designs might use a shared cache or a combination of local and shared caches. Aside from architectural considerations, such as cache, memory, and bus contention, these multicore CPUs appear to the operating system as N standard processors. 

This characteristic puts pressure on operating system designers—and application programmers—to make use of those processing cores. Finally, blade servers are a relatively recent development in which multiple processor boards, I/O boards, and networking boards are placed in the same chassis. The difference between these and traditional multiprocessor systems is that each blade-processor board boots independently and runs its own operating system. Some blade-server boards are multiprocessors as well, which blurs the lines between types of computers. In essence, these servers consist of multiple independent multiprocessor systems.

1.3.3 Clustered Systems 

Another type of multiprocessor system is a clustered system, which gathers together multiple CPUs. Clustered systems differ from the multiprocessor systems described in Section 1.3.2 in that they are composed of two or more individual systems—or nodes—joined together. Such systems are considered loosely coupled. Each node may be a single processor system or a multicore system. We should note that the definition of clustering is not concrete; many commercial packages wrestle to define a clustered system and why one form is better than another. 

The generally accepted definition is that clustered computers share storage and are closely linked via a local-area network LAN or a faster interconnect, such as InfiniBand. Clustering is usually used to provide high-availability service—that is, service will continue even if one or more systems in the cluster fail. Generally, we obtain high availability by adding a level of redundancy in the system. A layer of cluster software runs on the cluster nodes. 

Each node can monitor one or more of the others (over the LAN). If the monitored machine fails, the monitoring machine can take ownership of its storage and restart the applications that were running on the failed machine. The users and clients of the applications see only a brief interruption of service. Clustering can be structured asymmetrically or symmetrically. In asymmetric clustering, one machine is in hot-standby mode while the other is running the applications. The hot-standby host machine does nothing but monitors the active server. If that server fails, the hot-standby host becomes the active server. In symmetric clustering, two or more hosts are running applications and are monitoring each other. This structure is obviously more efficient, as it uses all of the available hardware. 

However, it does require that more than one application be available to run. Since a cluster consists of several computer systems connected via a network, clusters can also be used to provide high-performance computing environments. Such systems can supply significantly greater computational power than single-processor or even SMP systems because they can run an application concurrently on all computers in the cluster. The application must have been written specifically to take advantage of the cluster, however. This involves a technique known as parallelization, which divides a program into separate components that run in parallel on individual computers in the cluster.

Typically, these applications are designed so that once each computing node in the cluster has solved its portion of the problem, the results from all the nodes are combined into a final solution. Other forms of clusters include parallel clusters and clustering over a wide-area network (WAN) (as described in Chapter 17). Parallel clusters allow multiple hosts to access the same data on shared storage. Because most operating systems lack support for simultaneous data access by multiple hosts, parallel clusters usually require the use of special versions of software and special releases of applications. 

For example, Oracle Real Application Cluster is a version of Oracle’s database that has been designed to run on a parallel cluster. Each machine runs Oracle, and a layer of software tracks access to the shared disk. Each machine has full access to all data in the database. To provide this shared access, the system must also supply access control and locking to ensure that no conflicting operations occur. This function, commonly known as a distributed lock manager (DLM), is included in some cluster technology. Cluster technology is changing rapidly. Some cluster products support dozens of systems in a cluster, as well as clustered nodes that are separated by miles. 

Many of these improvements are made possible by storage-area networks (SANs), which allow many systems to attach to a pool of storage. If the applications and their data are stored on the SAN, then the cluster software can assign the application to run on any host that is attached to the SAN. If the host fails, then any other host can take over. In a database cluster, dozens of hosts can share the same database, greatly increasing performance and reliability. Figure 1.8 depicts the general structure of a clustered system.


Beowulf clusters are designed to solve high-performance computing tasks. A Beowulf cluster consists of the commodity hardware—such as personal computers—connected via a simple local-area network. No single specific software package is required to construct a cluster. Rather, the nodes use a set of open-source software libraries to communicate with one another. Thus, there are a variety of approaches to constructing a Beowulf cluster.

Typically, though, Beowulf computing nodes run the Linux operating system. Since Beowulf clusters require no special hardware and operate using open-source software that is available for free, they offer a low-cost strategy for building a high-performance computing cluster. In fact, some Beowulf clusters built from discarded personal computers are using hundreds of nodes to solve computationally expensive scientific computing problems.

Previous Post Next Post