2013 IEEE 7th International Conference on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS) 2013
DOI: 10.1109/idaacs.2013.6663019
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Hardware models for automated partitioning and mapping in multi-core systems

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Cited by 5 publications
(3 citation statements)
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“…Concerning the transformation criteria, we have just five methods which are based on the metamodel such as [18], [20], [21], [24] and our proposed method. Thus, for the transformation automation, we notice some methods; use their algorithm [39] like [21], [23] and [26] while other methods transform their models relying on a transformation language; for instance, QVT for [18] and [24] or ATL in our proposal. However, we have other methods that transform their models manually relying on Human language like [14], [16] and [20].…”
Section: Discussionmentioning
confidence: 99%
“…Concerning the transformation criteria, we have just five methods which are based on the metamodel such as [18], [20], [21], [24] and our proposed method. Thus, for the transformation automation, we notice some methods; use their algorithm [39] like [21], [23] and [26] while other methods transform their models relying on a transformation language; for instance, QVT for [18] and [24] or ATL in our proposal. However, we have other methods that transform their models manually relying on Human language like [14], [16] and [20].…”
Section: Discussionmentioning
confidence: 99%
“…Finally the LGP aprroach provides all system's runnables distributed among several ProcessPrototypes (user defined or automatically generated) as well as explicit CPC model elements, that can be combined and transmitted to a mapping plugin [14] for further information augmentation and finally to a code generator in order to apply the partitioning to the software and run it in parallel on a multicore system. The partitioning mechanism processes runnables (nodes) with respect to their dependencies (orderings) and execution cycles and utilizes multicore architectures by efficient parallelism and load balancing such that execution times and energy consumption can be lowered and high performance application development can be facilitated.…”
Section: Listing 1 Pseudocode For Partitioning Algorithmmentioning
confidence: 99%
“…Our previous publication [5] is dealing with the partitioning and mapping for heterogeneous multicore systems using a hardware model. It outlines a 2 http://blog.yakindu.org/category/damos-2/ pragmatic approach for partitioning and mapping of data flow graphs with a simple algorithm.…”
Section: Related Workmentioning
confidence: 99%