Chapter 4: Threads PDF

Chapter 4: Threads Overview Multicore Programming Multithreading Models Thread Libraries Implicit Threading Threading Issues Operating System Examples 1 Objectives To introduce the notion of a thread—a fundamental unit of CPU utilization that forms t...

Chapter 4: Threads Overview Multicore Programming Multithreading Models Thread Libraries Implicit Threading Threading Issues Operating System Examples 1 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 2 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 3 Multithreaded Server Architecture 4 Single and Multithreaded Processes 5 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 6 Concurrency vs. Parallelism Concurrent execution on single-core system: Parallelism on a multi-core system: 7 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 As # of threads grows, so does architectural support for threading CPUs have cores as well as hardware threads Consider Oracle SPARC T4 with 8 cores, and 8 hardware threads per core 8 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? 9 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 10 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 11 User level threads Some advantages Compared to kernel threads, user threads can be created more quickly and are simpler to control We can run them on any operating system Thread switching doesn’t require kernel-mode privileges Some limitations The operating system kernel and threads don’t communicate well Regardless of whether a process contains one thread or multiple threads, it receives a single time slice during the scheduling Each thread must decide when to give up control to another thread Non-blocking systems calls are necessary. Otherwise, even if the process still has runnable threads, it will be halted in the kernel. 12 Kernel threads Some advantages They permit the scheduling of many instances of the same process across various CPUs Multithreading is possible for kernel procedures When a thread is halted, the kernel can schedule another thread for the same process Some limitations They are sluggish and ineffective in contrast to user threads because the kernel must schedule and manage both processes and threads Because each thread needs a whole thread control block to keep track of other threads, there is a substantial amount of overhead The kernel’s complexity increases Transferring control from one thread in a process to another thread in the same process necessitates a mode switch to kernel mode 13 Multithreading Models Many-to-One One-to-One Many-to-Many 14 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 15 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 16 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 17 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 18 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 19 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 29 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: (QueueUserWorkItem(&PoolFunction, NULL, 0)) 30 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