Skip to main content

Partitioned Global Address Space Languages

De Wael, Mattias, Marr, Stefan, De Fraine, Bruno, Van Cutsem, Tom, De Meuter, Wolfgang (2015) Partitioned Global Address Space Languages. ACM Computing Surveys, 47 (4). 62:1-62:27. ISSN 0360-0300. (doi:10.1145/2716320) (KAR id:63827)

PDF Author's Accepted Manuscript
Language: English
Download (505kB)
[thumbnail of acm-csur-de-wael-et-al-partitioned-global-address-space-languages.pdf]
This file may not be suitable for users of assistive technology.
Request an accessible format
Official URL:


The Partitioned Global Address Space (PGAS) model is a parallel programming model that aims to improve programmer productivity while at the same time aiming for high performance. The main premise of PGAS is that a globally shared address space improves productivity, but that a distinction between local and remote data accesses is required to allow performance optimizations and to support scalability on large-scale parallel architectures. To this end, PGAS preserves the global address space while embracing awareness of non-uniform communication costs. Today, about a dozen languages exist that adhere to the PGAS model. This survey proposes a definition and a taxonomy along four axes: how parallelism is introduced, how the address space is partitioned, how data is distributed among the partitions and finally how data is accessed across partitions. Our taxonomy reveals that today's PGAS languages focus on distributing regular data and distinguish only between local and remote data access cost, whereas the distribution of irregular data and the adoption of richer data access cost models remain open challenges.

Item Type: Article
DOI/Identification number: 10.1145/2716320
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Stefan Marr
Date Deposited: 06 Oct 2017 15:02 UTC
Last Modified: 09 Dec 2022 00:05 UTC
Resource URI: (The current URI for this page, for reference purposes)
Marr, Stefan:
  • Depositors only (login required):


Downloads per month over past year