In computer science, partitioned global address space (PGAS) is a parallel programming model paradigm. PGAS is typified by communication operations involving a global memory address space abstraction that is logically partitioned, where a portion is local to each process, thread, or processing element.[1][2] The novelty of PGAS is that the portions of the shared memory space may have an affinity for a particular process, thereby exploiting locality of reference in order to improve performance. A PGAS memory model is featured in various parallel programming languages and libraries, including: Coarray Fortran, Unified Parallel C, Split-C, Fortress, Chapel, X10, UPC++, Coarray C++, Global Arrays, DASH and SHMEM. The PGAS paradigm is now an integrated part of the Fortran language, as of Fortran 2008 which standardized coarrays.

The various languages and libraries offering a PGAS memory model differ widely in other details, such as the base programming language and the mechanisms used to express parallelism. Many PGAS systems combine the advantages of a SPMD programming style for distributed memory systems (as employed by MPI) with the data referencing semantics of shared memory systems. In contrast to message passing, PGAS programming models frequently offer one-sided communication operations such as Remote Memory Access (RMA), whereby one processing element may directly access memory with affinity to a different (potentially remote) process, without explicit semantic involvement by the passive target process. PGAS offers more efficiency and scalability than traditional shared-memory approaches with a flat address space, because hardware-specific data locality can be explicitly exposed in the semantic partitioning of the address space.

A variant of the PGAS paradigm, asynchronous partitioned global address space (APGAS) augments the programming model with facilities for both local and remote asynchronous task creation.[3] Two programming languages that use this model are Chapel and X10.

Examples

See also

References

  1. ↑ Almasi, George. "PGAS (Partitioned Global Address Space) Languages.", Encyclopedia of Parallel Computing, Springer, (2011): 1539-1545. https://doi.org/10.1007/978-0-387-09766-4_210
  2. ↑ Cristian Coarfă; Yuri Dotsenko; John Mellor-Crummey, "An Evaluation of Global Address Space Languages: Co-Array Fortran and Unified Parallel C"
  3. ↑ Tim Stitt, "An Introduction to the Partitioned Global Address Space (PGAS) Programming Model"
  4. ↑ Numrich, R.W., Reid, J., Co-array Fortran for parallel programming. ACM SIGPLAN Fortran Forum 17(2), 1–31 (1998).
  5. ↑ J. Reid: Coarrays in the Next Fortran Standard. SIGPLAN Fortran Forum 29(2), 10–27 (July 2010)
  6. ↑ GCC wiki, Coarray support in gfortran as specified in the Fortran 2008 standard
  7. ↑ W. Chen, D. Bonachea, J. Duell, P. Husbands, C. Iancu, K. Yelick. A Performance Analysis of the Berkeley UPC Compiler 17th Annual International Conference on Supercomputing (ICS), 2003. https://doi.org/10.1145/782814.782825
  8. ↑ Tarek El-Ghazawi, William Carlson, Thomas Sterling, and Katherine Yelick. UPC: distributed shared memory programming. John Wiley & Sons, 2005.
  9. ↑ UPC Consortium, UPC Language and Library Specifications, v1.3, Lawrence Berkeley National Lab Tech Report LBNL-6623E, Nov 2013. https://doi.org/10.2172/1134233
  10. ↑ Bradford L. Chamberlain, Chapel, Programming Models for Parallel Computing, edited by Pavan Balaji, MIT Press, November 2015.
  11. ↑ John Bachan, Scott B. Baden, Steven Hofmeyr, Mathias Jacquelin, Amir Kamil, Dan Bonachea, Paul H. Hargrove, Hadia Ahmed. "UPC++: A High-Performance Communication Framework for Asynchronous Computation", In 33rd IEEE International Parallel & Distributed Processing Symposium (IPDPS'19), May 20–24, 2019. https://doi.org/10.25344/S4V88H
  12. ↑ T. A. Johnson: Coarray C++. Proceedings of the 7th International Conference on PGAS Programming Models. pp. 54–66. PGAS’13 (2013),
  13. ↑ Nieplocha, Jaroslaw; Harrison, Robert J.; Littlefield, Richard J. (1996). Global arrays: A nonuniform memory access programming model for high-performance computers. The Journal of Supercomputing. 10 (2): 169–189.
  14. ↑ K. Furlinger, C. Glass, A. Knupfer, J. Tao, D. Hunich, et al. DASH: Data Structures and Algorithms with Support for Hierarchical Locality. Euro-Par Parallel Processing Workshops (2014).
  15. ↑ P. Charles, C. Grothoff, V. Saraswat, C. Donawa, A. Kielstra, et al. X10: an object-oriented approach to nonuniform cluster computing. Proceedings of the 20th Annual ACM SIGPLAN Conference on Object-Oriented Programming, Systems, Languages, and Applications (OOPSLA’05) (2005).
  16. ↑ Katherine Yelick, Paul Hilfinger, Susan Graham, Dan Bonachea, Jimmy Su, Amir Kamil, Kaushik Datta, Phillip Colella, and Tong Wen, "Parallel Languages and Compilers: Perspective from the Titanium Experience", The International Journal Of High Performance Computing Applications, August 1, 2007, 21(3):266-290
  17. ↑ Katherine Yelick, Susan Graham, Paul Hilfinger, Dan Bonachea, Jimmy Su, Amir Kamil, Kaushik Datta, Phillip Colella, Tong Wen, "Titanium", Encyclopedia of Parallel Computing, edited by David Padua, (Springer: 2011) Pages: 2049-2055
  18. ↑ Culler, D. E., Dusseau, A., Goldstein, S. C., Krishnamurthy, A., Lumetta, S., Von Eicken, T., & Yelick, K. Parallel programming in Split-C. In Supercomputing'93: Proceedings of the 1993 ACM/IEEE conference on Supercomputing (pp. 262-273). IEEE.
  19. ↑ Bonachea D, Hargrove P.GASNet-EX: A High-Performance, Portable Communication Library for Exascale Proceedings of Languages and Compilers for Parallel Computing (LCPC'18). Oct 2018. https://doi.org/10.25344/S4QP4W
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