Supercomputers are becoming more heterogeneous. They are composed by several machines with different computation capabilities and different kinds and families of accelerators, such as GPUs or Intel Xeon Phi coprocessors. Programming these machines is a hard task, that requires a deep study of the architectural details, in order to exploit efficiently each computational unit.
In this paper, we present an extension of a GPU-CPU heterogeneous programming model, to include support for Intel Xeon Phi coprocessors. This contribution extends the previous model and its implementation, by taking advantage of both the GPU communication model and the CPU execution model of the original approach, to derive a new approach for the Xeon Phi. Our experimental results show that using our approach, the programming effort needed for changing the kind of target devices is highly reduced for several study cases. For example, using our model to program a Mandelbrot benchmark, the 97% of the application code is reused between a GPU implementation and a Xeon Phi implementation.
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