Chapter 4: Threads &
Concurrency
Operating System Concepts – 10 th Edition Silberschatz, Galvin and Gagne ©2018
Outline
▪ Overview
▪ Multicore Programming
▪ Multithreading Models
▪ Thread Libraries
▪ Implicit Threading
▪ Threading Issues
▪ Operating System Examples
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Objectives
▪ Identify the basic components of a thread, and contrast threads
and processes
▪ Describe the benefits and challenges of designng
multithreaded applications
▪ Illustrate different approaches to implicit threading including
thread pools, fork-join, and Grand Central Dispatch
▪ Describe how the Windows and Linux operating systems
represent threads
▪ Designing multithreaded applications using the Pthreads, Java,
and Windows threading APIs
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Motivation
▪ Most modern applications are multithreaded
▪ Threads run within application
▪ Multiple tasks with the application can be implemented by
separate threads
• Update display
• Fetch data
• Spell checking
• Answer a network request
▪ Process creation is heavy-weight while thread creation is
light-weight
▪ Can simplify code, increase efficiency
▪ Kernels are generally multithreaded
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Single and Multithreaded Processes
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Multithreaded Server Architecture
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Benefits
▪ Responsiveness – may allow continued execution if part of process is blocked,
especially important for user interfaces
▪ Resource Sharing – threads share resources of process, easier than shared
memory or message passing
▪ Economy – cheaper than process creation, thread switching lower overhead than
context switching
▪ Scalability – process can take advantage of multicore architectures
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Multicore Programming
▪ Multicore or multiprocessor systems puts pressure on programmers,
challenges include:
• Dividing activities
• Balance
• Data splitting
• Data dependency
• Testing and debugging
▪ Parallelism implies a system can perform more than one task
simultaneously
▪ Concurrency supports more than one task making progress
• Single processor / core, scheduler providing concurrency
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Concurrency vs. Parallelism
▪ Concurrent execution on single-core system:
▪ Parallelism on a multi-core system:
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Multicore Programming
▪ Types of parallelism
• Data parallelism – distributes subsets of the same data
across multiple cores, same operation on each
• Task parallelism – distributing threads across cores, each
thread performing unique operation
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Data and Task Parallelism
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Amdahl’s Law
▪ Identifies performance gains from adding additional cores to an
application that has both serial and parallel components
▪ S is serial portion
▪ N processing cores
▪ That is, if application is 75% parallel / 25% serial, moving from 1 to 2
cores results in speedup of 1.6 times
▪ As N approaches infinity, speedup approaches 1 / S
Serial portion of an application has disproportionate effect on
performance gained by adding additional cores
▪ But does the law take into account contemporary multicore systems?
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Amdahl’s Law
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User Threads and Kernel Threads
▪ User threads - management done by user-level threads library
▪ Three primary thread libraries:
• POSIX Pthreads
• Windows threads
• Java threads
▪ Kernel threads - Supported by the Kernel
▪ Examples – virtually all general-purpose operating systems, including:
• Windows
• Linux
• Mac OS X
• iOS
• Android
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Thread Libraries
▪ Thread library provides programmer with API for creating and
managing threads
▪ Two primary ways of implementing
• Library entirely in user space
• Kernel-level library supported by the OS
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Pthreads
▪ May be provided either as user-level or kernel-level
▪ A POSIX standard (IEEE 1003.1c) API for thread creation and
synchronization
▪ Specification, not implementation
▪ API specifies behavior of the thread library, implementation is up to
development of the library
▪ Common in UNIX operating systems (Linux & Mac OS X)
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Fork-Join Parallelism
▪ Multiple threads (tasks) are forked, and then joined.
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Pthreads Example
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Pthreads Example (Cont.)
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Pthreads Code for Joining 10 Threads
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Implicit Threading
▪ Growing in popularity as numbers of threads increase, program
correctness more difficult with explicit threads
▪ Creation and management of threads done by compilers and run-
time libraries rather than programmers
▪ Five methods explored
• Thread Pools
• Fork-Join
• OpenMP
• Grand Central Dispatch
• Intel Threading Building Blocks
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Fork-Join Parallelism
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OpenMP openMP0.c
▪ Set of compiler directives and an
API for C, C++, FORTRAN
▪ Provides support for parallel
programming in shared-memory
environments
▪ Identifies parallel regions –
blocks of code that can run in
parallel
#pragma omp parallel
Create as many threads as there are
cores
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▪ Run the for loop in parallel
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Thread Cancellation
▪ Terminating a thread before it has finished
▪ Thread to be canceled is target thread
▪ Two general approaches:
• Asynchronous cancellation terminates the target thread immediately
• Deferred cancellation allows the target thread to periodically check if it should
be cancelled
▪ Pthread code to create and cancel a thread:
• thread_0.c
thread_1.c
thread_2.c
thread_3.c
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Thread Cancellation (Cont.)
▪ Invoking thread cancellation requests cancellation, but actual cancellation
depends on thread state
▪ If thread has cancellation disabled, cancellation remains pending until thread
enables it
▪ Default type is deferred
• Cancellation only occurs when thread reaches cancellation point
i.e., pthread_testcancel() thread_4.c
Then cleanup handler is invoked Thread Disable cancling
▪ On Linux systems, thread cancellation is handled through signals
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Thread-Local Storage
▪ Thread-local storage (TLS) allows each thread to have its own copy of data
▪ Useful when you do not have control over the thread creation process (i.e., when
using a thread pool)
▪ Different from local variables
• Local variables visible only during single function invocation
• TLS visible across function invocations
▪ Similar to static data
• TLS is unique to each thread
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Operating System Examples
▪ Windows Threads
▪ Linux Threads
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End of Chapter 4
Operating System Concepts – 10 th Edition Silberschatz, Galvin and Gagne ©2018
