We focus on large parallel hybrid architectures based on a combination of general processors (eg Intel Xeon) and accelerators (Nvidia GPU). Using with efficiency these hybrid clusters for high performance computing is central in our work. The heterogeneity of computing resources in hybrid clusters leads to many issues when we want to use large scientific applications on it. Two main issues were addressed in this thesis. The first one concerns the sharing of accelerators for MPI applications and the second one focuses on programming and concurrent execution of application between CPUs and accelerators. Hybrid architectures are very heterogeneous: for each cluster, the ratio between the number of accelerators and the number of CPU cores can be different. Thus, we first propose a concept of accelerator virtualization, which allows applications to view an architecture in which the number of accelerators is not related to the number of physical accelerators. An execution model based on the sharing of accelerators is proposed. We also propose extensions to the programming model based on MPI + threads to address the problem of concurrent execution between CPUs and accelerators. We propose a system based on two types of threads (CPU and accelerator threads) to implement hybrid calculations simultaneously exploiting the CPU and accelerators model. In both cases, the deployment and the execution of code on hybrid resources is critical. Consequently, we propose two software libraries, called S_GPU 1 and S_GPU 2, designed to deploy and perform calculations on the hybrid hardware. S_GPU 1 deals with virtualization and S_GPU 2 allows concurrent operations on CPUs and accelerators. To observe the deployment and the execution of code on complex hybrid architectures, we integrated trace mechanisms for analyzing the progress of the programs using our libraries. The validation of our proposals has been carried out on two large scientific applications: BigDFT (ab-initio simulation) and SPECFEM3D (simulation of seismic waves).
