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Our discussion thus far has focused on the problems of scheduling the CPU in a system with a single processing core. If multiple CPUs are available, load sharing, where multiple threads may run in parallel, becomes possible, however scheduling issues become correspondingly more complex. Many possibilities have been tried; and as we saw with CPU scheduling with a single-core CPU, there is no one best solution.
Traditionally, the term multiprocessor referred to systems that provided multiple physical processors, where each processor contained one single-core CPU. However, the definition of multiprocessor has evolved significantly, and on modern computing systems, multiprocessor now applies to the following system architectures:
• Multicore CPUs
• Multithreaded cores
• NUMA (Non-uniform memory access) systems
• Heterogeneous multiprocessing
Here, we discuss several concerns in multiprocessor scheduling in the context of these different architectures. In the first three examples we concentrate on systems in which the processors are identical - homogeneous - in terms of their functionality. We can then use any available CPU to run any process in the queue. In the last example we explore a system where the processors are not identical in their capabilities.
Approaches to Multiple-Processor Scheduling
One approach to CPU scheduling in a multiprocessor system has all scheduling decisions, I/O processing, and other system activities handled by a single processor - the master server. The other processors execute only user code. This asymmetric multiprocessing is simple because only one core accesses the system data structures, reducing the need for data sharing. The downfall of this approach is the master server becomes a potential bottleneck where overall system performance may be reduced.
The standard approach for supporting multiprocessors is symmetric multiprocessing (SMP), where each processor is self-scheduling. Scheduling proceeds by having the scheduler for each processor examine the ready queue and select a thread to run. Note that this provides two possible strategies for organizing the threads eligible to be scheduled:
1. All threads may be in a common ready queue.
2. Each processor may have its own private queue of threads.
These two strategies are contrasted in Figure 5.11. If we select the first option, we have a possible race condition on the shared ready queue and therefore must ensure that two separate processors do not choose to schedule the same thread and that threads are not lost from the queue. We could use some form of locking to protect the common ready queue from this race condition. Locking would be highly contended, however, as all accesses to the queue would require lock ownership, and accessing the shared queue would likely be a performance bottleneck.
The second option permits each processor to schedule threads from its private run queue and therefore does not suffer from the possible performance problems associated with a shared run queue. Thus, it is the most common approach on systems supporting SMP. Additionally, having private, perprocessor run queues in fact may lead to more efficient use of cache memory. There are issues with per - processor run queues - most notably, workloads of varying sizes. However, as we shall see, balancing algorithms can be used to equalize workloads among all processors.
Virtually all modern operating systems support SMP, including Windows, Linux, and macOS as well as mobile systems including Android and iOS. In the remainder of this section, we discuss issues concerning SMP systems when designing CPU scheduling algorithms.
Traditionally, SMP systems have allowed several processes to run in parallel by providing multiple physical processors. However, most contemporary computer hardware now places multiple computing cores on the same physical chip, resulting in a multicore processor. Each core maintains its architectural state and thus appears to the operating system to be a separate logical CPU. SMP systems that use multicore processors are faster and consume less power than systems in which each CPU has its own physical chip.
Multicore processors may complicate scheduling issues. Let's consider how this can happen. Researchers have discovered that when a processor accesses memory, it spends a significant amount of time waiting for the data to become available. This situation, known as a memory stall, occurs primarily because modern processors operate at much faster speeds than memory. However, a memory stall can also occur because of a cache miss (accessing data that are not in cache memory). Figure 5.12 illustrates a memory stall. In this scenario, the processor can spend up to 50 percent of its time waiting for data to become available from memory.
To remedy this situation, many recent hardware designs have implemented multithreaded processing cores in which two (or more) hardware threads are assigned to each core. That way, if one hardware thread stalls while waiting for memory, the core can switch to another thread. Figure 5.13 illustrates a dual-threaded processing core on which the execution of thread 0 and the execution of thread 1 are interleaved. From an operating system perspective, each hardware thread maintains its architectural state, such as instruction pointer and register set, and thus appears as a logical CPU that is available to run a software thread.
This technique - known as chip multithreading (CMT) - is illustrated in Figure 5.14. Here, the processor contains four computing cores, with each core containing two hardware threads. From the perspective of the operating system, there are eight logical CPUs.
Intel processors use the term hyper-threading (also known as simultaneous multithreading or SMT) to describe assigning multiple hardware threads to a single processing core. Contemporary Intel processors - such as the i7 - support two threads per core, while the Oracle Sparc M7 processor supports eight threads per core, with eight cores per processor, thus providing the operating system with 64 logical CPUs.
In general, there are two ways to multithread a processing core: coarsegrained and fine-graine multithreading. With coarse-grained multithreading, a thread executes on a core until a long-latency event such as a memory stall occurs. Because of the delay caused by the long-latency event, the core must switch to another thread to begin execution. However, the cost of switching between threads is high, since the instruction pipeline must be flushed before the other thread can begin execution on the processor core. Once this new thread begins execution, it begins filling the pipeline with its instructions. Fine-grained (or interleaved) multithreading switches between threads at a much finer level of granularity - typically at the boundary of an instruction cycle. However, the architectural design of fine- grained systems includes logic for thread switching. As a result, the cost of switching between threads is small.
To learn more about Multi-Processor Scheduling and Chip Multithreading along with Load Balancing, and Processor Affinity, see The tenth edition of Operating System Concepts page 224.
About the Authors
Abraham Silberschatz is the Sidney J. Weinberg Professor of Computer Science at Yale University. Prior to joining Yale, he was the Vice President of the Information Sciences Research Center at Bell Laboratories. Prior to that, he held a chaired professorship in the Department of Computer Sciences at the University of Texas at Austin.
Professor Silberschatz is a Fellow of the Association of Computing Machinery (ACM), a Fellow of Institute of Electrical and Electronic Engineers (IEEE), a Fellow of the American Association for the Advancement of Science (AAAS), and a member of the Connecticut Academy of Science and Engineering.
Greg Gagne is chair of the Computer Science department at Westminster College in Salt Lake City where he has been teaching since 1990. In addition to teaching operating systems, he also teaches computer networks, parallel programming, and software engineering.
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The tenth edition of
Operating System Concepts
has been revised to keep it fresh and up-to-date with contemporary examples of how operating systems function, as well as enhanced interactive elements to improve learning and the student's experience with the material. It combines instruction on concepts with real-world applications so that students can understand the practical usage of the content. End-of-chapter problems, exercises, review questions, and programming exercises help to further reinforce important concepts. New interactive self-assessment problems are provided throughout the text to help students monitor their level of understanding and progress. A Linux virtual machine (including C and Java source code and development tools) allows students to complete programming exercises that help them engage further with the material.
A reader in the U.S. says, "This is what computer-related books should be like. It is thorough, in depth, information packed, authoritative, and exhaustive. You cannot get this kind of excellent information from the Internet - or many other computer books these days. It's a shame that quality computer books are declining so rapidly in number. I hope they continue to update and publish this book for many years to come.
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