Chapter 2: Process Management
2.2. Threads & Concurrency
GV: Nguyễn Thị Thanh Vân
Operating System Concepts – 10th 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 designing 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
Design 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 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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Concurrency vs. Parallelism
Concurrent execution on single-core system:
Parallelism on a multi-core system:
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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Multithreading Models
One-to-One Many-to-One Many-to-Many
Each user-level thread maps to Many user-level threads Allows many user level threads
kernel thread mapped to single kernel thread to be mapped to many kernel
Creating a user-level thread One thread blocking causes all threads
creates a kernel thread to block Allows the operating system to
More concurrency than many- Multiple threads may not run in create a sufficient number of
to-one parallel on muticore system kernel threads
because only one may be in Windows with the ThreadFiber
Number of threads per process kernel at a time
sometimes restricted due to package
overhead Few systems currently use this Otherwise not very common
model
Examples
Examples:
• Windows
• Solaris Green Threads
• Linux
• GNU Portable Threads
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Two-level Model
Similar to M:M, except that it allows a user thread to be bound to
kernel thread
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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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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
• Standard practice is to implement Runnable interface
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Java Threads
Implementing Runnable interface:
Creating a thread:
Waiting on a thread:
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Java Executor Framework
Rather than explicitly creating threads, Java also allows thread creation
around the Executor interface:
The Executor is used as follows:
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Java Executor Framework
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Java Executor Framework (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
Five methods explored
• Thread Pools
• Fork-Join
• OpenMP
• Grand Central Dispatch
• Intel Threading Building Blocks
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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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Java Thread Pools
Three factory methods for creating thread pools in Executors class:
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Java Thread Pools (Cont.)
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Fork-Join Parallelism
Multiple threads (tasks) are forked, and then joined.
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Fork-Join Parallelism
General algorithm for fork-join strategy:
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Fork-Join Parallelism
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Fork-Join Parallelism in Java
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Fork-Join Parallelism in Java
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Fork-Join Parallelism in Java
The ForkJoinTask is an abstract base class
RecursiveTask and RecursiveAction classes extend
ForkJoinTask
RecursiveTask returns a result (via the return value from the
compute() method)
RecursiveAction does not return a result
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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
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Run the Loop in Parallel
Run the for loop in parallel
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Grand Central Dispatch
Apple technology for macOS and iOS operating systems
Extensions to C, C++ and Objective-C languages, API, and run-time
library
Allows identification of parallel sections
Manages most of the details of threading
Block is in “^{ }” :
ˆ{ printf("I am a block"); }
Blocks placed in dispatch queue
• Assigned to available thread in thread pool when removed from
queue
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Grand Central Dispatch
Two types of dispatch queues:
• serial – blocks removed in FIFO order, queue is per process,
called main queue
Programmers can create additional serial queues within
program
• concurrent – removed in FIFO order but several may be removed
at a time
Four system wide queues divided by quality of service:
o QOS_CLASS_USER_INTERACTIVE
o QOS_CLASS_USER_INITIATED
o QOS_CLASS_USER_UTILITY
o QOS_CLASS_USER_BACKGROUND
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Grand Central Dispatch
For the Swift language a task is defined as a closure – similar to a
block, minus the caret
Closures are submitted to the queue using the dispatch_async()
function:
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Intel Threading Building Blocks (TBB)
Template library for designing parallel C++ programs
A serial version of a simple for loop
The same for loop written using TBB with parallel_for statement:
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Threading Issues
Semantics of fork() and exec() system calls
Signal handling
• Synchronous and asynchronous
Thread cancellation of target thread
• Asynchronous or deferred
Thread-local storage
Scheduler Activations
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Semantics of fork() and exec()
Does fork()duplicate only the calling thread or all threads?
• Some UNIXes have two versions of fork
exec() usually works as normal – replace the running process
including all threads
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Signal Handling
Signals are used in UNIX systems to notify a process that a particular
event has occurred.
A signal handler is used to process signals
1. Signal is generated by particular event
2. Signal is delivered to a process
3. Signal is handled by one of two signal handlers:
1. default
2. user-defined
Every signal has default handler that kernel runs when handling
signal
• User-defined signal handler can override default
• For single-threaded, signal delivered to process
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Signal Handling (Cont.)
Where should a signal be delivered for multi-threaded?
• Deliver the signal to the thread to which the signal applies
• Deliver the signal to every thread in the process
• Deliver the signal to certain threads in the process
• Assign a specific thread to receive all signals for the process
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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:
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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()
Then cleanup handler is invoked
On Linux systems, thread cancellation is handled through signals
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Thread Cancellation in Java
Deferred cancellation uses the interrupt() method, which sets the
interrupted status of a thread.
A thread can then check to see if it has been interrupted:
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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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Scheduler Activations
Both M:M and Two-level models require
communication to maintain the appropriate
number of kernel threads allocated to the
application
Typically use an intermediate data structure
between user and kernel threads – lightweight
process (LWP)
• Appears to be a virtual processor on which
process can schedule user thread to run
• Each LWP attached to kernel thread
• How many LWPs to create?
Scheduler activations provide upcalls - a
communication mechanism from the kernel to the
upcall handler in the thread library
This communication allows an application to
maintain the correct number kernel threads
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Operating System Examples
Windows Threads
Linux Threads
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Windows Threads
Windows API – primary API for Windows applications
Implements the one-to-one mapping, kernel-level
Each thread contains
• A thread id
• Register set representing state of processor
• Separate user and kernel stacks for when thread runs in user mode
or kernel mode
• Private data storage area used by run-time libraries and dynamic
link libraries (DLLs)
The register set, stacks, and private storage area are known as the
context of the thread
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Windows Threads (Cont.)
The primary data structures of a thread include:
• ETHREAD (executive thread block) – includes pointer to process
to which thread belongs and to KTHREAD, in kernel space
• KTHREAD (kernel thread block) – scheduling and synchronization
info, kernel-mode stack, pointer to TEB, in kernel space
• TEB (thread environment block) – thread id, user-mode stack,
thread-local storage, in user space
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Windows Threads Data Structures
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Linux Threads
Linux refers to them as tasks rather than threads
Thread creation is done through clone() system call
clone() allows a child task to share the address space of the
parent task (process)
• Flags control behavior
struct task_struct points to process data structures (shared or
unique)
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End of Chapter 2.2
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