Operating System Concepts – 10th Edition
Hamzeh Khazaei
Department of Electrical Engineering and Computer Science
Week 3: Threads & Concurrency
Feb 1, 2021
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EECS 3221:
OPERATING SYSTEM FUNDAMENTALS
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Chapter 4: Threads
! Overview
! Multicore Programming
! Multithreading Models
! Thread Libraries
! Implicit Threading
! Threading Issues
! Operating System Examples
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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
! One process can use at most one core, while multi-threads could use all
available cores or CPUs.
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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:
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Multicore Programming
! Types of parallelism
! Data parallelism – distributes distinctive chunk of data across multiple cores,
same operation on each 4Any example?
! 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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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
! MacOSX ! iOS
! Android Operating System Concepts – 10th Edition
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Operating System Concepts – 10th Edition
User and Kernel Threads
Q: Do we need to have a relationship between user threads and kernel threads?
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Multithreading Models
! Relationship between user threads and kernel threads: ! 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
! Many threads at kernel may result in poor performance (overhead)
! Number of threads per process sometimes restricted due to overhead
! Examples
! Windows
! Linux
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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 ! Windows with the ThreadFiber package
! Otherwise not very common
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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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Question?
! Why a user thread must always be mapped to a specific kernel thread?
! Users’ programs make their own “threads” and simulate context-switches to switch between them. However, these threads aren’t kernel threads. Each user thread can’t actually run on its own, and the only way for a user thread to run is if a kernel thread is actually told to execute the code contained in a user thread.
! In other words, the reason that user threads have to be associated with kernel threads is that by itself a user thread is just a bunch of data in a user program. Kernel threads are the real threads in the system, so for a user thread to make progress the user program has to have its scheduler take a user thread and then run it on a 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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Lambda Expression in Java
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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
4i.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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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 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
! Blockisin“^{}”:
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
4Programmers can create additional serial queues within program
! concurrent – removed in FIFO order but several may be removed at a time
4Four 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 symbol
! 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.
! Asignalhandlerisusedtoprocesssignals
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
4i.e. pthread_testcancel()
4Then 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
! Similartostaticdata
! 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
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End of Chapter 4 Any Question?
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