Task parallelism

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—concretely performed by processes or threads—across different processors. It contrasts to data parallelism as another form of parallelism.

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. Communication usually takes place by passing data from one thread to the next as part of a workflow.

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.

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.

In an SPMD system, both CPUs will execute the code.
In a parallel environment, both will have access to the same data.
The "if" clause differentiates between the CPUs. CPU "a" will read true on the "if" and CPU "b" will read true on the "else if", thus having their own task.
Now, both CPU's execute separate code blocks simultaneously, performing different tasks simultaneously.

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-parallel languages

Examples of (fine-grained) task-parallel languages can be found in the realm of Hardware Description Languages like Verilog and VHDL, which can also be considered as representing a "code static" software paradigm where the program has a static structure and the data is changing - as against a "data static" model where the data is not changing (or changing slowly) and the processing (applied methods) change (e.g. database search).

General-purposes languages

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

C++ (Intel): Threading Building Blocks
C, C++, Objective-C (Apple): Grand Central Dispatch
D: tasks and fibers
Go: goroutines
Java: Java concurrency
.NET: Task Parallel Library

Notes

References

Quinn Michael J, Parallel Programming in C with MPI and OpenMP McGraw-Hill Inc. 2004. ISBN 0-07-058201-7
D. Kevin Cameron coined terms "data static" and "code static".

Parallel computing

General	Distributed computing Cloud computing High-performance computing

Levels	Bit Instruction Task Data Memory Loop Pipeline

Multithreading	Temporal Simultaneous Preemptive Cooperative

Theory	PRAM model Analysis of parallel algorithms Amdahl's law Gustafson's law Cost efficiency Karp–Flatt metric Slowdown Speedup

Elements	Process Thread Fiber Instruction window

Coordination	Multiprocessing Memory coherency Cache coherency Cache invalidation Barrier Synchronization Application checkpointing

Programming	Stream Processing Dataflow programming Models Implicit parallelism Explicit parallelism Concurrency Non-blocking algorithm

Hardware	Flynn's taxonomy SISD SIMD MISD MIMD Dataflow architecture Pipelined processor Superscalar processor Vector processor Multiprocessor symmetric asymmetric Memory shared distributed distributed shared UMA NUMA COMA Massively parallel computer Computer cluster Grid computer

APIs	Ateji PX Boost.Thread Charm++ Cilk Coarray Fortran CUDA Dryad C++ AMP Global Arrays MPI OpenMP OpenCL OpenHMPP OpenACC TPL PLINQ PVM POSIX Threads RaftLib UPC TBB

Problems	Embarrassingly parallel Software lockout Scalability Race condition Deadlock Livelock Starvation Deterministic algorithm Parallel slowdown

Category: parallel computing Media related to parallel computing at Wikimedia Commons

This article is issued from Wikipedia - version of the 7/17/2016. The text is available under the Creative Commons Attribution/Share Alike but additional terms may apply for the media files.