Embarrassingly parallel
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In parallel computing, an embarrassingly parallel workload (or embarrassingly parallel problem) is one for which little or no effort is required to separate the problem into a number of parallel tasks. This is often the case where there exists no dependency (or communication) between those parallel tasks.[1]
Embarrassingly parallel problems are ideally suited to distributed computing and are also easy to perform on server farms which do not have any of the special infrastructure used in a true supercomputer cluster. They are thus well suited to large, internet based distributed platforms such as BOINC.
A common example of an embarrassingly parallel problem lies within graphics processing units (GPUs) for tasks such as 3D projection, where each pixel on the screen may be rendered independently.
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[edit] Examples
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Some examples of embarrassingly parallel problems include:
- The Mandelbrot set and other fractal calculations, where each point can be calculated independently.
- Rendering of computer graphics. In ray tracing, each pixel may be rendered independently. In computer animation, each frame may be rendered independently (see parallel rendering).
- Brute force searches in cryptography. A notable real-world example is distributed.net.
- BLAST searches in bioinformatics.
- Large scale face recognition that involves comparing thousands of input faces with similarly large number of faces.[2]
- Computer simulations comparing many independent scenarios, such as climate models.
- Genetic algorithms and other evolutionary computation metaheuristics.
- Ensemble calculations of numerical weather prediction.
- Event simulation and reconstruction in particle physics.
[edit] See also
[edit] References
- ^ Designing and Building Parallel Programs, by Ian Foster. Addison-Wesley (ISBN 9780201575941), 1995. Section 1.4.4
- ^ http://lbrandy.com/blog/2008/10/how-we-made-our-face-recognizer-25-times-faster/
[edit] External links
- Embarrassingly parallel, Parallel algorithms
- Embarrassingly Parallel Computations, Engineering a Beowulf-style Compute Cluster
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