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Rapid development of diverse computer architectures and hardware accelerators caused that designing parallel systems faces new problems resulting from their heterogeneity. Our implementation of a parallel system called KernelHive allows to efficiently run applications in a heterogeneous environment consisting of multiple collections of nodes with different types of computing devices. The execution engine of the system is open for optimizer implementations, focusing on various criteria. In this paper, we propose a new optimizer for KernelHive, that utilizes distributed databases and performs data prefetching to optimize the execution time of applications, which process large input data. Employing a versatile data management scheme, which allows combining various distributed data providers, we propose using NoSQL databases for our purposes. We support our solution with results of experiments with our OpenCL implementation of a regular expression matching application.

Dynamic Data Management Among Multiple Databases for Optimization of Parallel Computations in Heterogeneous HPC Systems