“…This satisfiability reasoning is present, for example, in problems from Systems Biology, such as genomic reversal distance, 1 classification models, 2 polymerase chain reaction experiments, 3 and gene regulatory networks (GRN) inference. [4][5][6][7][8] A common characteristic of these problems is that the input size of the modeled MHS and HSP instances is often large, making the retrieval of the exact solutions impracticable for traditional algorithms. Moreover, the MHS enumeration problem adds two major difficulties in comparison to the traditional HSP: (i) an MHS solution H must be a hitting set (similarly with HSP), but no other subset of H is allowed to be a hitting set, and (ii) we are not only concerned to enumerate MHSs of minimum size but as well all MHSs from a minimum size where solutions exist to a certain maximum size k. These difficulties lead to a considerably larger search space, and thus, enumerating MHSs of any size even for small instances is often impracticable for exact algorithms.With the advent of new accelerator technologies such as Many Integrated Core (MIC) architectures, 9 hybrid heterogeneous platforms composed of CPUs, GPUs, and MICs became a common occurrence in research centers.…”