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Parallel and Distributed Processing Symposium, International (2013)
Cambridge, MA, USA USA
May 20, 2013 to May 24, 2013
ISSN: 1530-2075
ISBN: 978-1-4673-6066-1
pp: 138-149
As new heterogeneous systems and hardware accelerators appear, high performance computers can reach a higher level of computational power. Nevertheless, this does not come for free: the more heterogeneity the system presents, the more complex becomes the programming task in terms of resource management. OmpSs is a task-based programming model and framework focused on the runtime exploitation of parallelism from annotated sequential applications. This paper presents a set of extensions to this framework: we show how the application programmer can expose different specialized versions of tasks (i.e. pieces of specific code targeted and optimized for a particular architecture) and how the system can choose between these versions at run time to obtain the best performance achievable for the given application. From the results obtained in a multi-GPU system, we prove that our proposal gives flexibility to application's source code and can potentially increase application's performance.
Runtime, Graphics processing units, Programming, Computer architecture, Reliability, Proposals, Kernel, scheduling techniques, multi-gpu management, heterogeneous architectures, parallel programming models

J. Planas, R. M. Badia, E. Ayguade and J. Labarta, "Self-Adaptive OmpSs Tasks in Heterogeneous Environments," 2013 IEEE 27th International Symposium on Parallel and Distributed Processing (IPDPS 2013)(IPDPS), Boston, MA, 2013, pp. 138-149.
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