Embarrassingly parallel

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 separate the problem into a number of parallel tasks.[1] This is often the case where there is little or no dependency or need for communication between those parallel tasks, or for results between them.[2]

Thus, these are different from distributed computing problems that need communication between tasks, especially communication of intermediate results. They are easy to perform on server farms which lack the special infrastructure used in a true supercomputer cluster. They are thus well suited to large, Internet-based volunteer computing platforms such as BOINC, and do not suffer 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.

Etymology

"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]

Examples

Some examples of embarrassingly parallel problems include:

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.[16] Similar R packages include "future", "parallel" and others.

See also

References
  1. Herlihy, Maurice; Shavit, Nir (2012). The Art of Multiprocessor Programming, Revised Reprint (revised ed.). Elsevier. p. 14. ISBN 9780123977953. Retrieved 28 February 2016. Some computational problems are "embarrassingly parallel": they can easily be divided into components that can be executed concurrently.
  2. Section 1.4.4 of: Foster, Ian (1995). Designing and Building Parallel Programs. Addison–Wesley. ISBN 9780201575941. Archived from the original on 2011-03-01.
  3. Alan Chalmers; Erik Reinhard; Tim Davis (21 March 2011). Practical Parallel Rendering. CRC Press. ISBN 978-1-4398-6380-0.
  4. Matloff, Norman (2011). The Art of R Programming: A Tour of Statistical Software Design, p.347. No Starch. ISBN 9781593274108.
  5. Leykin, Anton; Verschelde, Jan; Zhuang, Yan (2006). "Parallel Homotopy Algorithms to Solve Polynomial Systems". Mathematical Software - ICMS 2006. pp. 225–234. doi:10.1007/11832225_22. ISBN 978-3-540-38084-9. {{cite book}}: |journal= ignored (help)
  6. Moler, Cleve (1986). Heath, Michael T. (ed.). Matrix Computation on Distributed Memory Multiprocessors. ISBN 978-0898712094. {{cite book}}: |journal= ignored (help)
  7. The Intel hypercube part 2 reposted on Cleve's Corner blog on The MathWorks website
  8. Kepner, Jeremy (2009). Parallel MATLAB for Multicore and Multinode Computers, p.12. SIAM. ISBN 9780898716733.
  9. Erricos John Kontoghiorghes (21 December 2005). Handbook of Parallel Computing and Statistics. CRC Press. ISBN 978-1-4200-2868-3.
  10. Yuefan Deng (2013). Applied Parallel Computing. World Scientific. ISBN 978-981-4307-60-4.
  11. Josefsson, Simon; Percival, Colin (August 2016). "The scrypt Password-Based Key Derivation Function". tools.ietf.org. doi:10.17487/RFC7914. Retrieved 2016-12-12.
  12. Mathog, DR (22 September 2003). "Parallel BLAST on split databases". Bioinformatics. 19 (14): 1865–6. doi:10.1093/bioinformatics/btg250. PMID 14512366.
  13. How we made our face recognizer 25 times faster (developer blog post)
  14. Shigeyoshi Tsutsui; Pierre Collet (5 December 2013). Massively Parallel Evolutionary Computation on GPGPUs. Springer Science & Business Media. ISBN 978-3-642-37959-8.
  15. Youssef Hamadi; Lakhdar Sais (5 April 2018). Handbook of Parallel Constraint Reasoning. Springer. ISBN 978-3-319-63516-3.
  16. Simple Network of Workstations (SNOW) package
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