Chapter 4: Threads & Concurrency

multithreaded

• Most modern applications are —

• Threads run within application

• Update display

• Fetch data

• Spell checking

• Answer a network request

Multiple tasks with the application can be implemented by separate threads (4)

Kernels

• Process creation is heavy-weight while thread creation is light-weight

• Can simplify code, increase efficiency

• — are generally multithreaded

responsiveness

may allow continued execution if part of process is blocked, especially important for user interfaces

resources 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

Multicore or multiprocessor systems

puts pressure on programmers

• Dividing activities

• Balance

• Data splitting

• Data dependency

• Testing and debugging

challenges include (5)

parallelism

implies a system can perform more than one task simultaneously

concurrency

supports more than one task making progress

Single processor / core, scheduler providing it

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

Amdahl’s Law

Identifies performance gains from adding additional cores to an application that has both serial and parallel components

• serial portion

• processing cores

• S is —

• N —

• 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

adding additional cores

Serial portion of an application has disproportionate effect on performance gained by —

But does the law take into account contemporary multicore systems?

user threads

management done by user-level threads library

• POSIX Pthreads

• Windows threads

• Java threads

Three primary thread libraries

Kernel threads

Supported by the Kernel

• Windows

• Linux

• Mac OS X

• iOS

• Android

Examples – virtually all general-purpose operating systems, including: (5)

• Many-to-One

• One-to-One

• Many-to-Many

3 Multithreading Models

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

• Solaris Green Threads

• GNU Portable Threads

Few systems currently use this model (2)

One-to-One

• windows and linux

• 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

• 2 examples

Many-to-Many Model

• ThreadFiber

• 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 — package

• Otherwise not very common

Two-level Model

Similar to M:M, except that it allows a user thread to be bound to kernel thread

Thread library

provides programmer with API for creating and managing threads

• Library entirely in user space

• Kernel-level library supported by the OS

Two primary ways of implementing

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)

Java threads

• are managed by the JVM

• Typically implemented using the threads model provided by underlying OS

• Thread class

• Runnable interface

Java threads may be created by:

• Extending —

• Implementing the —

• Standard practice is to implement the second

Java Executor Framework

Executor interface

Rather than explicitly creating threads, Java also allows thread creation around the

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

• Thread Pools

• Fork-Join

• OpenMP

• Grand Central Dispatch

• Intel Threading Building Blocks

Five methods explored

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 it

• static ExecutorService newSingleThreadExecutor ()

• static ExecutorService newFixedThreadPool (int size)

• static ExecutorService newCachedThreadPool()

Java Thread Pools: Three factory methods for creating thread pools in Executors class:

Fork-Join Parallelism

Multiple threads (tasks) are forked, and then joined

ForkJoinTask

an abstract base class

RecursiveTask and RecursiveAction

2 classes extend ForkJoinTask

RecursiveTask

returns a result (via the return value from the compute() method)

RecursiveAction

does not return a result

OpenMP

• Set of compiler directives and an API for C, C++, FORTRAN

• Provides support for parallel programming in shared-memory environments

parallel regions

Identifies — blocks of code that can run in parallel

• #pragma omp parallel

• Create as many threads as there are cores

• Run the for loop in parallel

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

serial

main queue

— blocks removed in FIFO order, queue is per process, called —

• Programmers can create additional serial queues within program

concurrent

removed in FIFO order but several may be removed at a time

• QOS_CLASS_USER_INTERACTIVE

• QOS_CLASS_USER_INITIATED

• QOS_CLASS_USER_UTILITY

• QOS_CLASS_USER_BACKGROUND

Four system wide queues divided by quality of service:

closure

For the Swift language a task is defined as a — similar to a block, minus the caret

It is submitted to the queue using the dispatch_async() function

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

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

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

Signals

are used in UNIX systems to notify a process that a particular event has occurred.

signal handler

A — 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

default handler

Every signal has — that kernel runs when handling signa

User-defined signal handler

— can override default

• For single-threaded, signal delivered to process

multi-threaded

Where should a signal be delivered for —?

• 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

Thread Cancellation

Terminating a thread before it has finished

target thread

Thread to be canceled is —

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

• Invoking thread cancellation requests cancellation, but actual cancellation depends on thread state

• If thread has cancellation disabled, cancellation remains pending until thread enables it

• cancellation point

• cleanup handler

• Default type is deferred

  • Cancellation only occurs when thread reaches —

    • i.e., pthread_testcancel()

    • Then — is invoked

• On Linux systems, thread cancellation is handled through signals

interrupt() method

• Deferred cancellation uses the —, which sets the interrupted status of a thread.

• A thread can then check to see if it has been interrupted

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

Scheduler Activations

Both M:M and Two-level models require communication to maintain the appropriate number of kernel threads allocated to the application

lightweight process (LWP)

Typically use an intermediate data structure between user and kernel threads —

• 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?

upcalls

upcall handler

Scheduler activations provide — a communication mechanism from the kernel to the — in the thread library

• This communication allows an application to maintain the correct number kernel threads

Windows Threads

Linux Threads

Operating System Examples 2

Windows API

— primary API for Windows applications

• Implements the one-to-one mapping, kernel-level

thread

Each — 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)

context

The register set, stacks, and private storage area are known as the — of the thread

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

tasks

Linux refers to them as — rather than threads

clone() system call

• Thread creation is done through —

• it 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)