Sparse and unstructured computations are widely used in Scientific and Engineering Applications. Such problem inherent in sparse and unstructured computations is called irregular problem. In this paper, we propose some extensions to OpenMP directives, aiming at efficient irregular OpenMP codes to be executed in parallel. These OpenMP directives include scheduling for irregular loops, inspector/executor for parallelizing irregular reduction, and eliminating ordered loops. We also introduce implementation strategies with respect to these extensions
Reductions represent a common algorithmic pattern in many scientific applications. OpenMP* has alway...
OpenMP is attracting wide-spread interest because of its easy-to-use parallel programming model for ...
This paper presents a set of proposals for the OpenMP shared--memory programming model oriented tow...
In prior work, we have proposed techniques to extend the ease of shared-memory parallel programming ...
In prior work, we have proposed techniques to extend the ease of shared-memory parallel programming ...
Many scientific applications involve array operations that are sparse in nature, ie array elements d...
In previous work, we have proposed techniques to extend the ease of shared-memory parallel programmi...
In this paper, some automatic parallelization and opti-mization techniques for irregular scientific ...
OpenMP has emerged as an important model and language extension for shared-memory parallel programmi...
Abstract—OpenMP has been very successful in exploiting structured parallelism in applications. With ...
Abstract. Nowadays shared memory HPC platforms expose a large number of cores organized in a hierarc...
this article we investigate the trade-off between time and space efficiency in scheduling and execut...
This paper advances the state-of-the-art in programming models for exploiting task-level parallelism...
OpenMP has established itself as the de facto standard for parallel programming on shared-memory pla...
The OpenMP task directive makes it possible to efficiently parallelize irregular applications, with ...
Reductions represent a common algorithmic pattern in many scientific applications. OpenMP* has alway...
OpenMP is attracting wide-spread interest because of its easy-to-use parallel programming model for ...
This paper presents a set of proposals for the OpenMP shared--memory programming model oriented tow...
In prior work, we have proposed techniques to extend the ease of shared-memory parallel programming ...
In prior work, we have proposed techniques to extend the ease of shared-memory parallel programming ...
Many scientific applications involve array operations that are sparse in nature, ie array elements d...
In previous work, we have proposed techniques to extend the ease of shared-memory parallel programmi...
In this paper, some automatic parallelization and opti-mization techniques for irregular scientific ...
OpenMP has emerged as an important model and language extension for shared-memory parallel programmi...
Abstract—OpenMP has been very successful in exploiting structured parallelism in applications. With ...
Abstract. Nowadays shared memory HPC platforms expose a large number of cores organized in a hierarc...
this article we investigate the trade-off between time and space efficiency in scheduling and execut...
This paper advances the state-of-the-art in programming models for exploiting task-level parallelism...
OpenMP has established itself as the de facto standard for parallel programming on shared-memory pla...
The OpenMP task directive makes it possible to efficiently parallelize irregular applications, with ...
Reductions represent a common algorithmic pattern in many scientific applications. OpenMP* has alway...
OpenMP is attracting wide-spread interest because of its easy-to-use parallel programming model for ...
This paper presents a set of proposals for the OpenMP shared--memory programming model oriented tow...