Heterogeneous systems with different types of compute devices are common nowadays in the field of High Performance Computing (HPC). This heterogeneity is not limited to compute devices, but also includes cluster nodes with different hardware configurations leading to asymmetric cluster architectures. In such a hierarchical system OpenCL is not sufficient any more. Support is required to distribute the work efficiently onto the non-identical cluster nodes. Different behavior of the individual compute devices with respect to execution time and energy consumption has to be taken into account to meet the demands of the user. Our framework provides a transparent view on the different compute devices alleviating the programmer to deal with the hardware architecture and device execution behavior explicitly. Besides efficiency considerations, the device-sensitive feature includes in addition handling of device failures and appropriate recovery actions. Experiments show that our framework succeeds in distributing the work onto compute devices efficiently.

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