embarrassingly parallel

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    In parallel computing, an embarrassingly parallel workload or problem (also called embarrassingly parallelizable, perfectly parallel, delightfully parallel or pleasingly parallel) is one where little or no effort is needed to split the problem into a number of parallel tasks. This is due to minimal or no dependency upon communication between the parallel tasks, or for results between them.
    These differ from distributed computing problems, which need communication between tasks, especially communication of intermediate results. They are easier to perform on server farms which lack the special infrastructure used in a true supercomputer cluster. They are well-suited to large, Internet-based volunteer computing platforms such as BOINC, and suffer less from parallel slowdown. The opposite of embarrassingly parallel problems are inherently serial problems, which cannot be parallelized at all.
    A common example of an embarrassingly parallel problem is 3D video rendering handled by a graphics processing unit, where each frame (forward method) or pixel (ray tracing method) can be handled with no interdependency. Some forms of password cracking are another embarrassingly parallel task that is easily distributed on central processing units, CPU cores, or clusters.


    Etymology


    "Embarrassingly" is used here to refer to parallelization problems which are "embarrassingly easy". The term may imply embarrassment on the part of developers or compilers: "Because so many important problems remain unsolved mainly due to their intrinsic computational complexity, it would be embarrassing not to develop parallel implementations of polynomial homotopy continuation methods." The term is first found in the literature in a 1986 book on multiprocessors by MATLAB's creator Cleve Moler, who claims to have invented the term.
    An alternative term, pleasingly parallel, has gained some use, perhaps to avoid the negative connotations of embarrassment in favor of a positive reflection on the parallelizability of the problems: "Of course, there is nothing embarrassing about these programs at all."


    Examples


    A trivial example involves serving static data. It would take very little effort to have many processing units produce the same set of bits. Indeed, the famous Hello World problem could easily be parallelized with few programming considerations or computational costs.
    Some examples of embarrassingly parallel problems include:

    Monte Carlo analysis
    Distributed relational database queries using distributed set processing.
    Numerical integration
    Bulk processing of unrelated files of similar nature in general, such as photo gallery resizing and conversion.
    The Mandelbrot set, Perlin noise and similar images, where each point is calculated independently.
    Rendering of computer graphics. In computer animation, each frame or pixel may be rendered independently (see parallel rendering).
    Some brute-force searches in cryptography. Notable real-world examples include distributed.net and proof-of-work systems used in cryptocurrency.
    BLAST searches in bioinformatics with split databases.
    Large scale facial recognition systems that compare thousands of arbitrary acquired faces (e.g., a security or surveillance video via closed-circuit television) with similarly large number of previously stored faces (e.g., a rogues gallery or similar watch list).
    Computer simulations comparing many independent scenarios.
    Genetic algorithms.
    Ensemble calculations of numerical weather prediction.
    Event simulation and reconstruction in particle physics.
    The marching squares algorithm.
    Sieving step of the quadratic sieve and the number field sieve.
    Tree growth step of the random forest machine learning technique.
    Discrete Fourier transform where each harmonic is independently calculated.
    Convolutional neural networks running on GPUs.
    Parallel search in constraint programming


    Implementations


    In R (programming language) – The Simple Network of Workstations (SNOW) package implements a simple mechanism for using a set of workstations or a Beowulf cluster for embarrassingly parallel computations. Similar R packages include "future", "parallel" and others.


    See also


    Amdahl's law defines value P, which would be almost or exactly equal to 1 for embarrassingly parallel problems.
    Cellular automaton
    Connection Machine
    CUDA framework
    Manycore processor
    Map (parallel pattern)
    Massively parallel
    Multiprocessing
    Parallel computing
    Process-oriented programming
    Shared-nothing architecture (SN)
    Symmetric multiprocessing (SMP)
    Vector processor


    References




    External links



    Embarrassingly Parallel Computations, Engineering a Beowulf-style Compute Cluster
    "Star-P: High Productivity Parallel Computing"

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Embarrassingly parallel - Wikipedia

In parallel computing, an embarrassingly parallel workload or problem (also called embarrassingly parallelizable, perfectly parallel, delightfully parallel or pleasingly parallel) is one where little or no effort is needed to split the problem into a number of parallel tasks. [1]

What is Embarrassingly Parallel? - Computer Hope

Jul 18, 2024 · A computing task is embarrassingly parallel (also called pleasingly parallel or perfectly parallel) if it can naturally divide into individual parallel processing tasks. Many image processing tasks, such as raytracing and raycasting of 3D images, are embarrassingly parallel.

Embarrassingly Parallel Algorithms Explained - freeCodeCamp.org

Nov 25, 2019 · Unlike distributed computing problems that need communication between tasks—especially on intermediate results, embarrassingly parallel algorithms are easy to perform on server farms that lack the special infrastructure used in a true supercomputer cluster.

Embarrassingly Parallel Programs - Computer Science: Indiana …

Use a separate pseudo-random number generator on each process. Each process must use a different seed. This is embarrassingly parallel. The choice of seeds used at each process is important. Simply using the process id or time of the day can yield to bad distributions. One can use the /dev/random device driver in Linux to get good seeds.

idioms - What is "embarrassing" about an embarrassingly parallel ...

Apr 16, 2017 · In computer science, a problem that is obviously decomposable into many identical but separate subtasks is called embarrassingly parallel. An example is a cryptographic brute force attack, in whic...

Exploring Embarrassingly Parallel and Its Role in Simplified ...

Jan 31, 2025 · That's how Embarrassingly Parallel computing works: it divides a big problem into many smaller independent tasks, all of which can be executed simultaneously. In computing, this simple yet strong approach is growing to revolutionise and improve the efficiency of solving complex problems in much less time and much more scalable solutions.

Embarrassingly Parallel Algorithms Explained – TheLinuxCode

Nov 11, 2024 · Embarrassingly parallel algorithms are the holy grail of concurrent programming. As the name suggests, they can be easily split into multiple fully independent sub-tasks that require no synchronization or communication between threads.

Embarrassingly Parallel - IDA Research

Feb 20, 2019 · 1. Embarrassingly Parallel problems are everywhere 2. It is a mindset .. a way of thinking about problem solving 3. Plenty of platforms 4. Sometimes it is a matter of mapping the problem into a format that a parallel platform can process 5. Many real life examples show it is worth the effort!

concurrency - Solving embarassingly parallel problems using Python ...

Mar 3, 2015 · Embarassingly parallel problems typically consist of three basic parts: Read input data (from a file, database, tcp connection, etc.). Run calculations on the input data, where each calculation is independent of any other calculation. Write results of calculations (to a file, database, tcp connection, etc.).

Outline: Embarrassingly (aka Naturally) Parallel Problems

COMP4300/8300 L6: Embarrassingly Parallel Problems 2024 • 15 Parallelization of MonteCarlo Integration each iteration is independent of each other (thus naturally parallel)!