Mixed Parallel Programming Models Using Parallel Tasks

Author(s):  
Joerg Duemmler ◽  
Thomas Rauber ◽  
Gudula Ruenger

Parallel programming models using parallel tasks have shown to be successful for increasing scalability on medium-size homogeneous parallel systems. Several investigations have shown that these programming models can be extended to hierarchical and heterogeneous systems which will dominate in the future. In this chapter, the authors discuss parallel programming models with parallel tasks and describe these programming models in the context of other approaches for mixed task and data parallelism. They discuss compiler-based as well as library-based approaches for task programming and present extensions to the model which allow a flexible combination of parallel tasks and an optimization of the resulting communication structure.

2010 ◽  
Vol 18 (3-4) ◽  
pp. 203-217 ◽  
Author(s):  
Zoran Budimlić ◽  
Michael Burke ◽  
Vincent Cavé ◽  
Kathleen Knobe ◽  
Geoff Lowney ◽  
...  

We introduce the Concurrent Collections (CnC) programming model. CnC supports flexible combinations of task and data parallelism while retaining determinism. CnC is implicitly parallel, with the user providing high-level operations along with semantic ordering constraints that together form a CnC graph. We formally describe the execution semantics of CnC and prove that the model guarantees deterministic computation. We evaluate the performance of CnC implementations on several applications and show that CnC offers performance and scalability equivalent to or better than that offered by lower-level parallel programming models.


2018 ◽  
Vol 84 ◽  
pp. 22-31 ◽  
Author(s):  
Adrián Castelló ◽  
Rafael Mayo ◽  
Kevin Sala ◽  
Vicenç Beltran ◽  
Pavan Balaji ◽  
...  

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