Parallel computing, a problem which is able to be trivially divided into parallelized tasks
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] This is due to minimal or no dependency upon communication between the parallel tasks, or for results between them.[2]
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.[3] Some forms of password cracking are another embarrassingly parallel task that is easily distributed on central processing units, CPU cores, or clusters.
"Embarrassingly" is used here to refer to parallelization problems which are "embarrassingly easy".[4] 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."[5] The term is first found in the literature in a 1986 book on multiprocessors by MATLAB's creator Cleve Moler,[6] who claims to have invented the term.[7]
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."[8]
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.[16] Similar R packages include "future", "parallel" and others.
^Herlihy, Maurice; Shavit, Nir (2012). The Art of Multiprocessor Programming, Revised Reprint (revised ed.). Elsevier. p. 14. ISBN9780123977953. Retrieved 28 February 2016. Some computational problems are "embarrassingly parallel": they can easily be divided into components that can be executed concurrently.
^Matloff, Norman (2011). The Art of R Programming: A Tour of Statistical Software Design, p.347. No Starch. ISBN9781593274108.
^Leykin, Anton; Verschelde, Jan; Zhuang, Yan (2006). "Parallel Homotopy Algorithms to Solve Polynomial Systems". Mathematical Software - ICMS 2006. Lecture Notes in Computer Science. Vol. 4151. pp. 225–234. doi:10.1007/11832225_22. ISBN978-3-540-38084-9.
^Moler, Cleve (1986). "Matrix Computation on Distributed Memory Multiprocessors". In Heath, Michael T. (ed.). Hypercube Multiprocessors. Society for Industrial and Applied Mathematics, Philadelphia. ISBN978-0898712094.