Chapter 4: Threads & Concurrency PDF

Chapter 4: Threads & Concurrency Operating System Concepts – 10th Edition Silberschatz, Galvin and Gagne ©2018  Outline § Overview § Multicore Programming § Multithreading Models...

Chapter 4: Threads & Concurrency 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 Operating System Concepts – 10th Edition 4.2 Silberschatz, Galvin and Gagne ©2018  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 § Designing multithreaded applications using the Pthreads, Java, and Windows threading APIs Operating System Concepts – 10th Edition 4.3 Silberschatz, Galvin and Gagne ©2018  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 Operating System Concepts – 10th Edition 4.4 Silberschatz, Galvin and Gagne ©2018  Single and Multithreaded Processes Operating System Concepts – 10th Edition 4.5 Silberschatz, Galvin and Gagne ©2018  Multithreaded Server Architecture Operating System Concepts – 10th Edition 4.6 Silberschatz, Galvin and Gagne ©2018  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 Operating System Concepts – 10th Edition 4.7 Silberschatz, Galvin and Gagne ©2018  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 Operating System Concepts – 10th Edition 4.8 Silberschatz, Galvin and Gagne ©2018  Concurrency vs. Parallelism § Concurrent execution on single-core system: § Parallelism on a multi-core system: Operating System Concepts – 10th Edition 4.9 Silberschatz, Galvin and Gagne ©2018  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 Operating System Concepts – 10th Edition 4.10 Silberschatz, Galvin and Gagne ©2018  Data and Task Parallelism Operating System Concepts – 10th Edition 4.11 Silberschatz, Galvin and Gagne ©2018  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? Operating System Concepts – 10th Edition 4.12 Silberschatz, Galvin and Gagne ©2018  Amdahl’s Law Operating System Concepts – 10th Edition 4.13 Silberschatz, Galvin and Gagne ©2018  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 Operating System Concepts – 10th Edition 4.14 Silberschatz, Galvin and Gagne ©2018  User and Kernel Threads Operating System Concepts – 10th Edition 4.15 Silberschatz, Galvin and Gagne ©2018  Multithreading Models § Many-to-One § One-to-One § Many-to-Many Operating System Concepts – 10th Edition 4.16 Silberschatz, Galvin and Gagne ©2018  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 multicore system because only one may be in kernel at a time § Few systems currently use this model § Examples: Solaris Green Threads GNU Portable Threads Operating System Concepts – 10th Edition 4.17 Silberschatz, Galvin and Gagne ©2018  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 Operating System Concepts – 10th Edition 4.18 Silberschatz, Galvin and Gagne ©2018  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 Operating System Concepts – 10th Edition 4.19 Silberschatz, Galvin and Gagne ©2018  Two-level Model § Similar to M:M, except that it allows a user thread to be bound to kernel thread Operating System Concepts – 10th Edition 4.20 Silberschatz, Galvin and Gagne ©2018  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 Operating System Concepts – 10th Edition 4.21 Silberschatz, Galvin and Gagne ©2018  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) Operating System Concepts – 10th Edition 4.22 Silberschatz, Galvin and Gagne ©2018  Pthreads Example Operating System Concepts – 10th Edition 4.23 Silberschatz, Galvin and Gagne ©2018  Pthreads Example (Cont.) Operating System Concepts – 10th Edition 4.24 Silberschatz, Galvin and Gagne ©2018  Pthreads Code for Joining 10 Threads Operating System Concepts – 10th Edition 4.25 Silberschatz, Galvin and Gagne ©2018  Windows Multithreaded C Program Operating System Concepts – 10th Edition 4.26 Silberschatz, Galvin and Gagne ©2018  Windows Multithreaded C Program (Cont.) Operating System Concepts – 10th Edition 4.27 Silberschatz, Galvin and Gagne ©2018  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 Operating System Concepts – 10th Edition 4.28 Silberschatz, Galvin and Gagne ©2018  Java Threads Implementing Runnable interface: Creating a thread: Waiting on a thread: Operating System Concepts – 10th Edition 4.29 Silberschatz, Galvin and Gagne ©2018  Java Executor Framework § Rather than explicitly creating threads, Java also allows thread creation around the Executor interface: § The Executor is used as follows: Operating System Concepts – 10th Edition 4.30 Silberschatz, Galvin and Gagne ©2018  Java Executor Framework Operating System Concepts – 10th Edition 4.31 Silberschatz, Galvin and Gagne ©2018  Java Executor Framework (Cont.) Operating System Concepts – 10th Edition 4.32 Silberschatz, Galvin and Gagne ©2018  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 Operating System Concepts – 10th Edition 4.33 Silberschatz, Galvin and Gagne ©2018  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 4 i.e,Tasks could be scheduled to run periodically § Windows API supports thread pools: Operating System Concepts – 10th Edition 4.34 Silberschatz, Galvin and Gagne ©2018  Java Thread Pools § Three factory methods for creating thread pools in Executors class: Operating System Concepts – 10th Edition 4.35 Silberschatz, Galvin and Gagne ©2018  Java Thread Pools (Cont.) Operating System Concepts – 10th Edition 4.36 Silberschatz, Galvin and Gagne ©2018  Fork-Join Parallelism § Multiple threads (tasks) are forked, and then joined. Operating System Concepts – 10th Edition 4.37 Silberschatz, Galvin and Gagne ©2018  Fork-Join Parallelism § General algorithm for fork-join strategy: Operating System Concepts – 10th Edition 4.38 Silberschatz, Galvin and Gagne ©2018  Fork-Join Parallelism Operating System Concepts – 10th Edition 4.39 Silberschatz, Galvin and Gagne ©2018  Fork-Join Parallelism in Java Operating System Concepts – 10th Edition 4.40 Silberschatz, Galvin and Gagne ©2018  Fork-Join Parallelism in Java Operating System Concepts – 10th Edition 4.41 Silberschatz, Galvin and Gagne ©2018  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 Operating System Concepts – 10th Edition 4.42 Silberschatz, Galvin and Gagne ©2018  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 Operating System Concepts – 10th Edition 4.43 Silberschatz, Galvin and Gagne ©2018  § Run the for loop in parallel Operating System Concepts – 10th Edition 4.44 Silberschatz, Galvin and Gagne ©2018  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 Operating System Concepts – 10th Edition 4.45 Silberschatz, Galvin and Gagne ©2018  Grand Central Dispatch § Two types of dispatch queues: serial – blocks removed in FIFO order, queue is per process, called main queue 4 Programmers can create additional serial queues within program concurrent – removed in FIFO order but several may be removed at a time 4 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 Operating System Concepts – 10th Edition 4.46 Silberschatz, Galvin and Gagne ©2018  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: Operating System Concepts – 10th Edition 4.47 Silberschatz, Galvin and Gagne ©2018  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: Operating System Concepts – 10th Edition 4.48 Silberschatz, Galvin and Gagne ©2018  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 Operating System Concepts – 10th Edition 4.49 Silberschatz, Galvin and Gagne ©2018  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 Operating System Concepts – 10th Edition 4.50 Silberschatz, Galvin and Gagne ©2018  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 Operating System Concepts – 10th Edition 4.51 Silberschatz, Galvin and Gagne ©2018  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 Operating System Concepts – 10th Edition 4.52 Silberschatz, Galvin and Gagne ©2018  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: Operating System Concepts – 10th Edition 4.53 Silberschatz, Galvin and Gagne ©2018  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 4 i.e., pthread_testcancel() 4 Then cleanup handler is invoked § On Linux systems, thread cancellation is handled through signals Operating System Concepts – 10th Edition 4.54 Silberschatz, Galvin and Gagne ©2018  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: Operating System Concepts – 10th Edition 4.55 Silberschatz, Galvin and Gagne ©2018  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 Operating System Concepts – 10th Edition 4.56 Silberschatz, Galvin and Gagne ©2018  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 Operating System Concepts – 10th Edition 4.57 Silberschatz, Galvin and Gagne ©2018  Operating System Examples § Windows Threads § Linux Threads Operating System Concepts – 10th Edition 4.58 Silberschatz, Galvin and Gagne ©2018  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 Operating System Concepts – 10th Edition 4.59 Silberschatz, Galvin and Gagne ©2018  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 Operating System Concepts – 10th Edition 4.60 Silberschatz, Galvin and Gagne ©2018  Windows Threads Data Structures Operating System Concepts – 10th Edition 4.61 Silberschatz, Galvin and Gagne ©2018  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) Operating System Concepts – 10th Edition 4.62 Silberschatz, Galvin and Gagne ©2018  End of Chapter 4 Operating System Concepts – 10th Edition Silberschatz, Galvin and Gagne ©2018