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Nowadays, large scale distributed systems gather thousands of nodes with hierarchical memory models. They are heterogeneous, volatile and geographically distributed. The efficient exploitation of such systems requires the conception and adaptation of appropriate numerical methods, the definition of new programming paradigms, new metrics for performance prediction, etc. The modern hybrid numerical methods are well adapted to this kind of systems. This is particularly because of their multi-level parallelism and fault tolerance property. However the programming of these methods for these architectures requires concurrent reuse of sequential and parallel code. But the currently existing numerical libraries aren't able to exploit the multi-level parallelism offered by theses methods. A few linear algebra numerical libraries make use of object oriented approach allowing modularity and extensibility. Nevertheless, those which offer modularity,sequential and parallel code reuse are almost non-existent. In this paper, we analyze the lacks in existing libraries and propose a design based on a component approach and the strict separation between computation operations, data management and communication control of an application. We present then an application of this design using YML scientific workflow environment (http://yml.prism.uvsq.fr/) jointly with the object oriented LAKe (Linear Algebra Kernel) library. Some numerical experiments on GRID5000 platform validate our approach and show its efficiency.