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Scientific codes are usually highly parallelised and executed on heterogeneous architectures. Nowadays, it is common to use graphics accelerators (GPUs) to speed up data-parallel algorithms, and in the meantime, the already existing CPUs can help in this work. Distributing the jobs between systems is always a difficult problem, especially when the processing units have different runtime environments and hardware architectures. There are several attempts for static and dynamic load balancing, but most of these are ...