Experience with Parallel Symbolic Applications in Orca
. Orca is a simple, imperative parallel programming language, based on a form of distributed shared memory called shared data-objects. This paper discusses the suitability of Orca for parallel symbolic programming. Orca was not designed specifically for this application area, and it lacks several fe...
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ftciteseerx:oai:CiteSeerX.psu:10.1.1.38.5489 2023-05-15T17:52:56+02:00 Experience with Parallel Symbolic Applications in Orca Henri Bal Koen Langendoen Raoul Bhoedjang The Pennsylvania State University CiteSeerX Archives 1995 application/postscript http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.38.5489 en eng http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.38.5489 Metadata may be used without restrictions as long as the oai identifier remains attached to it. ftp://ftp.lip6.fr/pub2/distributed_systems/amoeba/orca_papers/psls95.ps.gz text 1995 ftciteseerx 2016-09-18T00:19:20Z . Orca is a simple, imperative parallel programming language, based on a form of distributed shared memory called shared data-objects. This paper discusses the suitability of Orca for parallel symbolic programming. Orca was not designed specifically for this application area, and it lacks several features supported in many languages for symbolic parallel computing, such as futures, automatic load balancing, and automatic garbage collection. On the other hand, Orca does give high-level support for sharing global state. Also, its implementation automatically distributes shared data (stored in shared objects). We first give a comparison between Orca and two other models: imperative message-passing systems and functional languages. We do so by looking at several key issues in parallel programming and by studying how each of the three paradigms deals with these issues. Next, we describe our experiences with writing parallel symbolic applications in Orca. This work indicates that Or. Text Orca Unknown |
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description |
. Orca is a simple, imperative parallel programming language, based on a form of distributed shared memory called shared data-objects. This paper discusses the suitability of Orca for parallel symbolic programming. Orca was not designed specifically for this application area, and it lacks several features supported in many languages for symbolic parallel computing, such as futures, automatic load balancing, and automatic garbage collection. On the other hand, Orca does give high-level support for sharing global state. Also, its implementation automatically distributes shared data (stored in shared objects). We first give a comparison between Orca and two other models: imperative message-passing systems and functional languages. We do so by looking at several key issues in parallel programming and by studying how each of the three paradigms deals with these issues. Next, we describe our experiences with writing parallel symbolic applications in Orca. This work indicates that Or. |
author2 |
The Pennsylvania State University CiteSeerX Archives |
format |
Text |
author |
Henri Bal Koen Langendoen Raoul Bhoedjang |
spellingShingle |
Henri Bal Koen Langendoen Raoul Bhoedjang Experience with Parallel Symbolic Applications in Orca |
author_facet |
Henri Bal Koen Langendoen Raoul Bhoedjang |
author_sort |
Henri Bal |
title |
Experience with Parallel Symbolic Applications in Orca |
title_short |
Experience with Parallel Symbolic Applications in Orca |
title_full |
Experience with Parallel Symbolic Applications in Orca |
title_fullStr |
Experience with Parallel Symbolic Applications in Orca |
title_full_unstemmed |
Experience with Parallel Symbolic Applications in Orca |
title_sort |
experience with parallel symbolic applications in orca |
publishDate |
1995 |
url |
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.38.5489 |
genre |
Orca |
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Orca |
op_source |
ftp://ftp.lip6.fr/pub2/distributed_systems/amoeba/orca_papers/psls95.ps.gz |
op_relation |
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.38.5489 |
op_rights |
Metadata may be used without restrictions as long as the oai identifier remains attached to it. |
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1766160695822385152 |