Embarrassingly Parallel

What is Embarrassingly Parallel?

A computing task is said to be embarrassingly parallel when it can be naturally and easily divided into several different processing tasks that are performed in parallel with little or no dependency or communication.

Understanding Embarrassingly Parallel

Understanding Embarrassingly Parallel

In parallel computing, where multiple tasks are run at the same time, there may be some problems or workloads that are embarrassingly parallel.

Ideally, the term ‘embarrassingly’ in the etymology has nothing to do with any kind of embarrassment.

It, in fact, refers to ‘overabundance’ of the parallelization problems, which are, however, ‘embarrassingly easy.’

Typically, in parallel computing, an embarrassingly parallel problem or workload refers to the one that can be separated into a number of parallel tasks with very little or no effort.

This is the case often when there is very little or no need for or dependency on any sort of communication between the parallel tasks as well as the results obtained from them.

Embarrassingly parallel is also known by different other terms and phrases such as:

Ideally, it refers to a different form of distributed computing problems since they do not need any communications between the different tasks or even with the intermediate results.

3D image processing, such as ray tracing and ray casting of images, is the most common example of an embarrassingly parallel computing task.

In this case, there are different sections of one single image that are computed separately without needing any interaction with the others involved.

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A proper breakdown of the process will make things very clear to you.

These tasks are very easy to perform, especially on a server farm that does not have the special infrastructure used in a real supercomputer cluster.

Embarrassingly Parallel Algorithms

Embarrassingly parallel algorithms, irrespective of the nomenclature, can be either sequential algorithm or parallel algorithm in nature.

It is for this reason that they are so useful for large, distributed platforms based on the internet and are not subject to the customary parallel slowdown effects.

Embarrassingly parallel algorithms, also known as naturally parallel algorithms, have the following features:

In an ideal case scenario, in these algorithms the calculation of the tasks or sub-solutions is totally independent due to specific reasons such as:

These computations become almost embarrassingly parallel if they need some initial or final communication.

On the other hand, in a hybrid or a variable scenario, these solutions or tasks are collected in a shared data structure and also need some coordination in the end.

However, in a non-obvious termination condition, it may need some intermediate coordination while something is found.

Keeping track of the pool of tasks needs some additional overhead because all sub-problems are not known at the beginning.

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The algorithms of Monte Carlo simulations are a bit different. They approximate a function by means of random selections in computations.

And, in the case of the pseudorandom numbers, there are different techniques that are followed according to the set of rules such as:

There is also another specific technique followed in which the linear generator is converted so that each process produces only its share of random numbers. This makes controlling them much easier, but it needs more communication.

Embarrassingly Parallel Examples

One of the most common examples of embarrassingly parallel computing is 3D video or image rendering. It is done by the Graphics Processing Unit with no interdependency and by two methods, namely the forward method and the ray tracing method.

Another good example is a few types of password cracking that are distributed easily among the CPU cores or clusters.

Some other implementations and examples of embarrassingly parallel computing, both classic and otherwise, are:

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Some classic examples of embarrassingly parallel computing are:

Embarrassingly Parallel vs Parallel


Embarrassingly parallel computing is very important to expedite the process of computing different problems.

Now, after reading this article, you know that it is the process by which a specific task is easily and naturally divided and assigned to different cores for processing and has nothing ‘embarrassing’ in it as such.