Integrating Profiling into MDE Compilers

Aranega, Vincent; de Oliveira Rodrigues, Antonio Wendell; Etien, Anne; Guyomarch, Frédéric; Dekeyser, Jean-Luc

Document type :

Article dans une revue scientifique: Article original

DOI :

10.5121/ijsea.2014.5401

Title :

Integrating Profiling into MDE Compilers

Author(s) :

Aranega, Vincent [Auteur]

Contributions of the Data parallelism to real time [DART]
de Oliveira Rodrigues, Antonio Wendell [Auteur correspondant]
Contributions of the Data parallelism to real time [DART]
Etien, Anne [Auteur]

Analyses and Languages Constructs for Object-Oriented Application Evolution [RMOD]
Guyomarch, Frédéric [Auteur]

Dynamic Reconfigurable Massively Parallel Architectures and Languages [DREAMPAL]
Dekeyser, Jean-Luc [Auteur]

Dynamic Reconfigurable Massively Parallel Architectures and Languages [DREAMPAL]

Journal title :

International Journal of Software Engineering & Applications

Pages :

Publisher :

AIRCC Publishing Corporation

Publication date :

2014-07-28

ISSN :

0976-2221

English keyword(s) :

Performance Analysis
MDE
Profiling
Traceability

HAL domain(s) :

Informatique [cs]/Calcul parallèle, distribué et partagé [cs.DC]

English abstract : [en]

Scientific computation requires more and more performance in its algorithms. New massively parallel architectures suit well to these algorithms. They are known for offering high performance and power efficiency. Unfortunately, ...
Show more >Scientific computation requires more and more performance in its algorithms. New massively parallel architectures suit well to these algorithms. They are known for offering high performance and power efficiency. Unfortunately, as parallel programming for these architectures requires a complex distribution of tasks and data, developers find difficult to implement their applications effectively. Although approaches based on source-to-source intends to provide a low learning curve for parallel programming and take advantage of architecture features to create optimized applications, programming remains difficult for neophytes. This work aims at improving performance by returning to the high-level models, specific execution data from a profiling tool enhanced by smart advices computed by an analysis engine. In order to keep the link between execution and model, the process is based on a traceability mechanism. Once the model is automatically annotated, it can be re-factored aiming better performances on the re-generated code. Hence, this work allows keeping coherence between model and code without forgetting to harness the power of parallel architectures. To illustrate and clarify key points of this approach, we provide an experimental example in GPUs context. The example uses a transformation chain from UML-MARTE models to OpenCL code.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL

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