Programmation haute performance pour architectures hybrides

Abstract : Clusters of multicore/GPU nodes connected with a fast network offer very high therotical peak performances, reaching tens of TeraFlops. Unfortunately, the efficient programing of such architectures remains challenging because of their complexity and the diversity of the existing programming models. The purpose of this thesis is to improve the programmability of dense scientific applications on hybrid architectures in three ways: reducing the execution times, processing larger data sets and reducing the programming effort. We propose DSTEP, a directive-based programming model expressing both data and computation distribution. A large set of distribution types are unified in a "dstep distribute" directive and the replication of some distributed elements can be expressed using a "halo". The "dstep gridify" directive expresses both the computation distribution and the schedule constraints of loop iterations. We define a distribution model and demonstrate the correctness of the code transformation from the sequential domain to the parallel domain. From the distribution model, we derive a generic compilation scheme transforming DSTEP annotated input programs into parallel hybrid ones. We have implemented such a tool as a compiler integrated to the PIPS compilation workbench together with a library offering the runtime functionality, especially the communication. Our solution is validated on scientific programs from the NAS Parallel Benchmarks and the PolyBenchs as well as on an industrial signal procesing application.
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Rachid Habel. Programmation haute performance pour architectures hybrides. Calcul parallèle, distribué et partagé [cs.DC]. Ecole Nationale Supérieure des Mines de Paris, 2014. Français. ⟨NNT : 2014ENMP0025⟩. ⟨tel-01101782v2⟩

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