Task parallelism

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Task Parallelism is a form of parallelization of computer code. It is meant to distribute computing across multiple processors in parallel computing environments. It contrasts to Data Parallelism as another form of parallelism.

1 Description
2 Example
3 References
4 See also

[edit] Description

In a multiprocessor system executing a single set of instructions (SIMD), task parallelism is achieved when each processor performs a different task on same or different data. For instance, if we are 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 runtime of the execution. The tasks can be assigned using conditional statements as described below.Most programs fall somewhere on the continuum between Task parallelism and Data parallelism.

[edit] 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 a SIMD 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 CPU's. 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.

[edit] References

Quinn Michael J, Parallel Programming in C with MPI and OpenMP McGraw-Hill Inc. 2004. ISBN 0-07-058201-7

[edit] See also

Data Parallelism

Topics in Parallel Computing

v • d • e

General	High-performance computing
Parallelism	Data parallelism • Task parallelism
Theory	Speedup • Amdahl's law • Flynn's Taxonomy • Cost efficiency • Gustafson's Law • Karp-Flatt Metric
Elements	Process • Thread • Fiber • Parallel Random Access Machine
Coordination	Multiprocessing • Multitasking • Memory coherency • Cache coherency • Barrier • Synchronization • Distributed computing • Grid computing
Programming	Programming model • Implicit parallelism • Explicit parallelism
Hardware	Computer cluster • Beowulf • Symmetric multiprocessing • Non-Uniform Memory Access • Cache only memory architecture • Asymmetric multiprocessing • Simultaneous multithreading • Shared memory • Distributed memory • Massively parallel processing • Superscalar processing • Vector processing • Supercomputer
Software	Distributed shared memory • Application checkpointing
APIs	Pthreads • OpenMP • Message Passing Interface (MPI)
Problems	Embarrassingly parallel • Grand Challenge