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Chapter 4: Threads

Operating System Concepts – 9th Edition

Silberschatz, Galvin and Gagne ©2013

Chapter 4: Threads
 Overview  Multicore Programming  Multithreading Models  Thread Libraries  Implicit Threading  Threading Issues  Operating System Examples

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Objectives
 To introduce the notion of a thread—a fundamental unit of CPU

utilization that forms the basis of multithreaded computer systems
 To discuss the APIs for the Pthreads, Windows, and Java

thread libraries
 To explore several strategies that provide implicit threading  To examine issues related to multithreaded programming  To cover operating system support for threads in Windows and

Linux

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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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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 multiprocessor

architectures

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Multicore Programming
 Multicore or multiprocessor systems putting 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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Multicore Programming (Cont.)
 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 CPUs have cores as well as hardware threads Consider Oracle SPARC T4 with 8 cores, and 8 hardware threads per core

 As # of threads grows, so does architectural support for threading
 

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Concurrency vs. Parallelism


Concurrent execution on single-core system:



Parallelism on a multi-core system:

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Single and Multithreaded Processes

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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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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 Solaris Linux Tru64 UNIX Mac OS X

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Multithreading Models
 Many-to-One  One-to-One  Many-to-Many

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Many-to-One
 Many user-level threads mapped to

single kernel thread
 One thread blocking causes all to block  Multiple threads may not run in parallel

on muticore system because only one may be in kernel at a time
 Few systems currently use this model  Examples:
 

Solaris Green Threads GNU Portable Threads

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One-to-One
 Each user-level thread maps to kernel thread  Creating a user-level thread creates a kernel thread  More concurrency than many-to-one  Number of threads per process sometimes

restricted due to overhead
 Examples
  

Windows Linux Solaris 9 and later

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Many-to-Many Model
 Allows many user level threads to be

mapped to many kernel threads
 Allows the operating system to create

a sufficient number of kernel threads
 Solaris prior to version 9  Windows with the ThreadFiber

package

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Two-level Model
 Similar to M:M, except that it allows a user thread to be

bound to kernel thread
 Examples
   

IRIX HP-UX Tru64 UNIX Solaris 8 and earlier

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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 (Solaris, Linux, Mac OS X)

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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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Windows Multithreaded C Program

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Windows Multithreaded C Program (Cont.)

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Java Threads
 Java threads are managed by the JVM  Typically implemented using the threads model provided by

underlying OS
 Java threads may be created by:

 

Extending Thread class Implementing the Runnable interface

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Java Multithreaded Program

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Java Multithreaded Program (Cont.)

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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
 Three methods explored
  

Thread Pools OpenMP Grand Central Dispatch

 Other methods include Microsoft Threading Building Blocks

(TBB), java.util.concurrent package

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Thread Pools
 Create a number of threads in a pool where they await work  Advantages:
  

Usually slightly faster to service a request with an existing thread than create a new thread Allows the number of threads in the application(s) to be bound to the size of the pool Separating task to be performed from mechanics of creating task allows different strategies for running task


i.e.Tasks could be scheduled to run periodically

 Windows API supports thread pools:

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OpenMP
 

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 #pragma omp parallel for for(i=0;i

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