In the jargon of parallel computing, an embarrassingly parallel workload (or embarrassingly parallel problem) is one for which no particular effort is needed to segment the problem into a very large number of parallel tasks, and there is no essential dependency (or communication) between those parallel tasks.

In other words, each step can be computed independently from every other step, thus each step could be made to run on a separate processor to achieve quicker results.

Examples of embarrassingly parallel problems include:

• distributed rendering of non-real-time computer graphics. In ray tracing, each pixel may be rendered independently. In computer animation, each frame may be rendered independently.
• brute force searches in cryptography. A notable real-world example is distributed.net.
• BLAST searches in bioinformatics.
• Computer simulations comparing many independent scenarios, such as climate models.

Embarrassingly parallel problems are ideally suited to distributed computing over the Internet (eg. SETI@home), and are also easy to perform on server farms which do not have any of the special infrastructure used in a true supercomputer cluster.

Embarrassingly parallel problems lie at one end of the spectrum of parallelization, the degree to which a computational problem can be readily divided amongst processors.

See also

• Amdahl's law

Parallel computing | Distributed systems