Partitioned global address space

In

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

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

Fortran 2008^[6]

C programming language

Chapel^[10] a parallel language originally developed by Cray under the DARPA HPCS project
UPC++,^{Remote Procedure Call
(RPC)}
Coarray C++^[12] a C++ library developed by Cray, providing a close analog to Fortran coarray functionality
Global Arrays^[13] a library supporting parallel scientific computing on distributed arrays
DASH ^[14] a C++ template library for distributed data structures with support for hierarchical locality
SHMEM a family of libraries supporting parallel scientific computing on distributed arrays
X10^[15] a parallel language developed by IBM under the DARPA HPCS project
Fortress a parallel language developed by Sun Microsystems under the DARPA HPCS project
Titanium ^[16]^[17] an explicitly parallel dialect of Java developed at UC Berkeley to support scientific high-performance computing on large-scale multiprocessors
Split-C^{C programming language
that supports efficient access to a global address space}
The
manycore network on a chip processor with scratchpad memory
addressable between cores.

External links

Official website
An Introduction to the Partitioned Global Address Space Model
Programming in the Partitioned Global Address Space Model Archived 2010-06-12 at the Wayback Machine (2003)
GASNet Communication System - provides a software infrastructure for PGAS languages over high-performance networks^[19]

References

^ 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
^ Cristian Coarfă; Yuri Dotsenko; John Mellor-Crummey, "An Evaluation of Global Address Space Languages: Co-Array Fortran and Unified Parallel C"
^ Tim Stitt, "An Introduction to the Partitioned Global Address Space (PGAS) Programming Model"
^ Numrich, R.W., Reid, J., Co-array Fortran for parallel programming. ACM SIGPLAN Fortran Forum 17(2), 1–31 (1998).
^ J. Reid: Coarrays in the Next Fortran Standard. SIGPLAN Fortran Forum 29(2), 10–27 (July 2010)
^ GCC wiki, Coarray support in gfortran as specified in the Fortran 2008 standard
^ 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
^ Tarek El-Ghazawi, William Carlson, Thomas Sterling, and Katherine Yelick. UPC: distributed shared memory programming. John Wiley & Sons, 2005.
^ 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
^ Bradford L. Chamberlain, Chapel, Programming Models for Parallel Computing, edited by Pavan Balaji, MIT Press, November 2015.
^ 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
^ T. A. Johnson: Coarray C++. Proceedings of the 7th International Conference on PGAS Programming Models. pp. 54–66. PGAS’13 (2013),
^ 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.
^ 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).
^ 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).
^ 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
^ 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
^ 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.
^ 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

[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

[3]

[6]

[10]

[12]

[13]

[14]

[15]

[16]

[17]

[19]

v t e Parallel computing
General	Distributed computing Parallel computing Massively parallel Cloud computing High-performance computing Multiprocessing Manycore processor GPGPU Computer network Systolic array
Levels	Bit Instruction Thread Task Data Memory Loop Pipeline
Multithreading	Temporal Simultaneous (SMT) Simultaneous and heterogenous Speculative (SpMT) Preemptive Cooperative Clustered multi-thread (CMT) Hardware scout
Theory	PRAM model PEM model Analysis of parallel algorithms Amdahl's law Gustafson's law Cost efficiency Karp–Flatt metric Slowdown Speedup
Elements	Process Thread Fiber Instruction window Array
Coordination	Multiprocessing Memory coherence Cache coherence Cache invalidation Barrier Synchronization Application checkpointing
Programming	Stream processing Dataflow programming Models Implicit parallelism Explicit parallelism Concurrency Non-blocking algorithm
Hardware	Flynn's taxonomy SISD SIMD Array processing (SIMT) Pipelined processing Associative processing MISD MIMD Dataflow architecture Pipelined processor Superscalar processor Vector processor Multiprocessor symmetric asymmetric Memory shared distributed distributed shared UMA NUMA COMA Massively parallel computer Computer cluster Beowulf cluster Grid computer Hardware acceleration
APIs	Ateji PX Boost Chapel HPX Charm++ Cilk Coarray Fortran CUDA Dryad C++ AMP Global Arrays GPUOpen MPI OpenMP OpenCL OpenHMPP OpenACC Parallel Extensions PVM pthreads RaftLib ROCm UPC TBB ZPL
Problems	Automatic parallelization Deadlock Deterministic algorithm Embarrassingly parallel Parallel slowdown Race condition Software lockout Scalability Starvation
Category: Parallel computing

Examples

See also

External links

References