A shared environment parallel logic programming system on distributed memory architectures

2005 ◽  
pp. 371-380 ◽  
Author(s):  
Sergio A. Delgado-Rannauro ◽  
Michel Dorochevsky ◽  
Kees Schuerman ◽  
André Véron ◽  
Jiyang Xu
Keyword(s):  
Logic Programming ◽  
Distributed Memory ◽  
Shared Environment ◽  
Programming System ◽  
Parallel Logic Programming ◽  
Memory Architectures

Performance of an OR-parallel logic programming system

1988 ◽  
Vol 17 (1) ◽  
pp. 59-92 ◽  
Author(s):  
Peter A. Tinker
Keyword(s):  
Logic Programming ◽  
Programming System ◽  
Parallel Logic Programming

scholarly journals Designing a high performance parallel logic programming system

1987 ◽  
Vol 15 (1) ◽  
pp. 43-52 ◽  
Author(s):  
M. V. Hermenegildo ◽  
R. A. Warren
Keyword(s):  
Logic Programming ◽  
High Performance ◽  
Programming System ◽  
Parallel Logic Programming

The parallel logic programming system in the FGCS project and its future directions

1999 ◽  
Vol 25 (13-14) ◽  
pp. 1601-1633
Author(s):  
Shunichi Uchida ◽  
Akira Aiba ◽  
Kazuaki Rokusawa ◽  
Takashi Chikayama ◽  
Ryuzo Hasegawa
Keyword(s):  
Logic Programming ◽  
Programming System ◽  
Future Directions ◽  
Parallel Logic Programming

scholarly journals A team-based scheduling model for interfacing or-parallel prolog engines

2014 ◽  
Vol 11 (4) ◽  
pp. 1435-1454 ◽  
Author(s):  
João Santos ◽  
Ricardo Rocha
Keyword(s):  
Logic Programming ◽  
Programming Languages ◽  
Distributed Memory ◽  
The Past ◽  
Scheduling Model ◽  
Simultaneous Evaluation ◽  
Clean Interface ◽  
Layered Approach ◽  
Prolog Programs ◽  
Memory Architectures

Logic Programming languages, such as Prolog, offer a great potential for the exploitation of implicit parallelism. One of the most noticeable sources of implicit parallelism in Prolog programs is or-parallelism. Or-parallelism arises from the simultaneous evaluation of a subgoal call against the clauses that match that call. Nowadays, multicores and clusters of multicores are becoming the norm and, although, many parallel Prolog systems have been developed in the past, to the best of our knowledge, none of them was specially designed to explore the combination of shared and distributed memory architectures. Conceptually, an or-parallel Prolog system consists of two components: an or-parallel engine (i.e., a set of independent Prolog engines which we named a team of workers) and a scheduler. In this work, we propose a team-based scheduling model to efficiently exploit parallelism between different or-parallel engines running on top of clusters of multicores. Our proposal defines a layered approach where a second-level scheduler specifies a clean interface for scheduling work between the base or-parallel engines, thus enabling different scheduling combinations to be used for distributing work among workers inside a team and among teams.

Sign in / Sign up

Export Citation Format

Share Document