Task parallelism

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Task parallelism (also known as function parallelism and control parallelism) is a form of parallelization of computer code across multiple processors in parallel computing environments. Task parallelism focuses on distributing tasks—concurrently performed by processes or threads—across different processors. In contrast to data parallelism which involves running the same task on different components of data, task parallelism is distinguished by running many different tasks at the same time on the same data. [1] A common type of task parallelism is pipelining which consists of moving a single set of data through a series of separate tasks where each task can execute independently of the others.

Contents

Description

In a multiprocessor system, task parallelism is achieved when each processor executes a different thread (or process) on the same or different data. The threads may execute the same or different code. In the general case, different execution threads communicate with one another as they work, but this is not a requirement. Communication usually takes place by passing data from one thread to the next as part of a workflow. [2]

As a simple example, if a system is running code on a 2-processor system (CPUs "a" & "b") in a parallel environment and we wish to do tasks "A" and "B", it is possible to tell CPU "a" to do task "A" and CPU "b" to do task "B" simultaneously, thereby reducing the run time of the execution. The tasks can be assigned using conditional statements as described below.

Task parallelism emphasizes the distributed (parallelized) nature of the processing (i.e. threads), as opposed to the data (data parallelism). Most real programs fall somewhere on a continuum between task parallelism and data parallelism. [3]

Thread-level parallelism (TLP) is the parallelism inherent in an application that runs multiple threads at once. This type of parallelism is found largely in applications written for commercial servers such as databases. By running many threads at once, these applications are able to tolerate the high amounts of I/O and memory system latency their workloads can incur - while one thread is delayed waiting for a memory or disk access, other threads can do useful work.

The exploitation of thread-level parallelism has also begun to make inroads into the desktop market with the advent of multi-core microprocessors. This has occurred because, for various reasons, it has become increasingly impractical to increase either the clock speed or instructions per clock of a single core. If this trend continues, new applications will have to be designed to utilize multiple threads in order to benefit from the increase in potential computing power. This contrasts with previous microprocessor innovations in which existing code was automatically sped up by running it on a newer/faster computer.

Example

The pseudocode below illustrates task parallelism:

program: ... if CPU = "a" then     do task "A" else if CPU="b" then     do task "B" end if ... end program

The goal of the program is to do some net total task ("A+B"). If we write the code as above and launch it on a 2-processor system, then the runtime environment will execute it as follows.

Code executed by CPU "a":

program: ... do task "A" ... end program

Code executed by CPU "b":

program: ... do task "B" ... end program

This concept can now be generalized to any number of processors.

Language support

Task parallelism can be supported in general-purposes languages either built-in facilities or libraries. Notable examples include:

Examples of fine-grained task-parallel languages can be found in the realm of Hardware Description Languages like Verilog and VHDL.

See also

Related Research Articles

Central processing unit Central component of any computer system which executes input/output, arithmetical, and logical operations

A central processing unit (CPU), also called a central processor or main processor, is the electronic circuitry within a computer that executes instructions that make up a computer program. The CPU performs basic arithmetic, logic, controlling, and input/output (I/O) operations specified by the instructions in the program. The computer industry used the term "central processing unit" as early as 1955. Traditionally, the term "CPU" refers to a processor, more specifically to its processing unit and control unit (CU), distinguishing these core elements of a computer from external components such as main memory and I/O circuitry.

Computer multitasking Concurrent execution of multiple processes

In computing, multitasking is the concurrent execution of multiple tasks over a certain period of time. New tasks can interrupt already started ones before they finish, instead of waiting for them to end. As a result, a computer executes segments of multiple tasks in an interleaved manner, while the tasks share common processing resources such as central processing units (CPUs) and main memory. Multitasking automatically interrupts the running program, saving its state and loading the saved state of another program and transferring control to it. This "context switch" may be initiated at fixed time intervals, or the running program may be coded to signal to the supervisory software when it can be interrupted.

Process (computing) particular execution of a computer program

In computing, a process is the instance of a computer program that is being executed by one or many threads. It contains the program code and its activity. Depending on the operating system (OS), a process may be made up of multiple threads of execution that execute instructions concurrently.

Thread (computing) smallest sequence of programmed instructions that can be managed independently by a scheduler

In computer science, a thread of execution is the smallest sequence of programmed instructions that can be managed independently by a scheduler, which is typically a part of the operating system. The implementation of threads and processes differs between operating systems, but in most cases a thread is a component of a process. Multiple threads can exist within one process, executing concurrently and sharing resources such as memory, while different processes do not share these resources. In particular, the threads of a process share its executable code and the values of its dynamically allocated variables and non-thread-local global variables at any given time.

Superscalar processor CPU that implements instruction-level parallelism within a single processor

A superscalar processor is a CPU that implements a form of parallelism called instruction-level parallelism within a single processor. In contrast to a scalar processor that can execute at most one single instruction per clock cycle, a superscalar processor can execute more than one instruction during a clock cycle by simultaneously dispatching multiple instructions to different execution units on the processor. It therefore allows for more throughput than would otherwise be possible at a given clock rate. Each execution unit is not a separate processor, but an execution resource within a single CPU such as an arithmetic logic unit.

SIMD class of parallel computers in Flynns taxonomy, with multiple processing elements that perform the same operation on multiple data points simultaneously

Single instruction, multiple data (SIMD) is a class of parallel computers in Flynn's taxonomy. It describes computers with multiple processing elements that perform the same operation on multiple data points simultaneously. Such machines exploit data level parallelism, but not concurrency: there are simultaneous (parallel) computations, but only a single process (instruction) at a given moment. SIMD is particularly applicable to common tasks such as adjusting the contrast in a digital image or adjusting the volume of digital audio. Most modern CPU designs include SIMD instructions to improve the performance of multimedia use. SIMD is not to be confused with SIMT, which utilizes threads.

Multiprocessing is the use of two or more central processing units (CPUs) within a single computer system. The term also refers to the ability of a system to support more than one processor or the ability to allocate tasks between them. There are many variations on this basic theme, and the definition of multiprocessing can vary with context, mostly as a function of how CPUs are defined.

Parallel computing programming paradigm in which many calculations or the execution of processes are carried out simultaneously

Parallel computing is a type of computation in which many calculations or the execution of processes are carried out simultaneously. Large problems can often be divided into smaller ones, which can then be solved at the same time. There are several different forms of parallel computing: bit-level, instruction-level, data, and task parallelism. Parallelism has long been employed in high-performance computing, but it's gaining broader interest due to the physical constraints preventing frequency scaling. As power consumption by computers has become a concern in recent years, parallel computing has become the dominant paradigm in computer architecture, mainly in the form of multi-core processors.

Instruction-level parallelism ability of computer instructions to be executed simultaneously with correct results

Instruction-level parallelism (ILP) is a measure of how many of the instructions in a computer program can be executed simultaneously.

Simultaneous multithreading (SMT) is a technique for improving the overall efficiency of superscalar CPUs with hardware multithreading. SMT permits multiple independent threads of execution to better utilize the resources provided by modern processor architectures.

OpenMP open standard for parallelizing

The application programming interface (API) OpenMP supports multi-platform shared-memory multiprocessing programming in C, C++, and Fortran, on many platforms, instruction-set architectures and operating systems, including Solaris, AIX, HP-UX, Linux, macOS, and Windows. It consists of a set of compiler directives, library routines, and environment variables that influence run-time behavior.

In concurrent programming, concurrent accesses to shared resources can lead to unexpected or erroneous behavior, so parts of the program where the shared resource is accessed need to be protected in ways that avoid the concurrent access. This protected section is the critical section or critical region. It cannot be executed by more than one process at a time. Typically, the critical section accesses a shared resource, such as a data structure, a peripheral device, or a network connection, that would not operate correctly in the context of multiple concurrent accesses.

In computing, hardware acceleration is the use of computer hardware specially made to perform some functions more efficiently than is possible in software running on a general-purpose central processing unit (CPU). Any transformation of data or routine that can be computed, can be calculated purely in software running on a generic CPU, purely in custom-made hardware, or in some mix of both. An operation can be computed faster in application-specific hardware designed or programmed to compute the operation than specified in software and performed on a general-purpose computer processor. Each approach has advantages and disadvantages. The implementation of computing tasks in hardware to decrease latency and increase throughput is known as hardware acceleration.

Automatic parallelization, also auto parallelization, autoparallelization, or parallelization, the last one of which implies automation when used in context, refers to converting sequential code into multi-threaded or vectorized code in order to utilize multiple processors simultaneously in a shared-memory multiprocessor (SMP) machine. The goal of automatic parallelization is to relieve programmers from the hectic and error-prone manual parallelization process. Though the quality of automatic parallelization has improved in the past several decades, fully automatic parallelization of sequential programs by compilers remains a grand challenge due to its need for complex program analysis and the unknown factors during compilation.

Concurrent computing is a form of computing in which several computations are executed concurrently—during overlapping time periods—instead of sequentially, with one completing before the next starts.

BMDFM software

BMDFM is software that enables running an application in parallel on shared memory symmetric multiprocessors (SMP) using the multiple processors to speed up the execution of single applications. BMDFM automatically identifies and exploits parallelism due to the static and mainly DYNAMIC SCHEDULING of the dataflow instruction sequences derived from the formerly sequential program.

Multi-core processor Microprocessor with more than one processing unit

A multi-core processor is a computer processor integrated circuit with two or more separate processing units, called cores, each of which reads and executes program instructions, as if the computer had several processors. The instructions are ordinary CPU instructions but the single processor can run instructions on separate cores at the same time, increasing overall speed for programs that support multithreading or other parallel computing techniques. Manufacturers typically integrate the cores onto a single integrated circuit die or onto multiple dies in a single chip package. The microprocessors currently used in almost all personal computers are multi-core. A multi-core processor implements multiprocessing in a single physical package. Designers may couple cores in a multi-core device tightly or loosely. For example, cores may or may not share caches, and they may implement message passing or shared-memory inter-core communication methods. Common network topologies to interconnect cores include bus, ring, two-dimensional mesh, and crossbar. Homogeneous multi-core systems include only identical cores; heterogeneous multi-core systems have cores that are not identical. Just as with single-processor systems, cores in multi-core systems may implement architectures such as VLIW, superscalar, vector, or multithreading.

Intel C++ Compiler, also known as icc or icl, is a group of C and C++ compilers from Intel available for Windows, Mac, Linux, FreeBSD and Intel-based Android devices.

Data parallelism

Data parallelism is parallelization across multiple processors in parallel computing environments. It focuses on distributing the data across different nodes, which operate on the data in parallel. It can be applied on regular data structures like arrays and matrices by working on each element in parallel. It contrasts to task parallelism as another form of parallelism.

Multithreading (computer architecture) ability of a central processing unit (CPU) or a single core in a multi-core processor to execute multiple processes or threads concurrently

In computer architecture, multithreading is the ability of a central processing unit (CPU) to provide multiple threads of execution concurrently, supported by the operating system. This approach differs from multiprocessing. In a multithreaded application, the threads share the resources of a single or multiple cores, which include the computing units, the CPU caches, and the translation lookaside buffer (TLB).

References

  1. Reinders, James (10 September 2007). "Understanding task and data parallelism | ZDNet". ZDNet. Retrieved 8 May 2017.
  2. Quinn, Michael J. (2007). Parallel programming in C with MPI and openMP (Tata McGraw-Hill ed.). New Delhi: Tata McGraw-Hill Pub. ISBN   978-0070582019.
  3. Hicks, Michael. "Concurrency Basics" (PDF). University of Maryland: Department of Computer Science. Retrieved 8 May 2017.