Molecular docking aims to predict possible drug candidates for many diseases, and it is computationally intensive. Particularly, in simulating the ligand-receptor binding process, the binding pocket of the receptor is divided into subcubes, and when the ligand is docked into all cubes, there are many molecular docking tasks, which are extremely time-consuming. In this study, we propose a heterogeneous parallel scheme of molecular docking for the binding process of ligand to receptor to accelerate simulating. The parallel scheme includes two layers of parallelism, a coarse-grained layer of parallelism implemented in the message-passing interface (MPI) and a fine-grained layer of parallelism focused on the graphics processing unit (GPU). At the coarse-grain layer of parallelism, a docking task inside one lattice is assigned to one unique MPI process, and a grouped master-slave mode is used to allocate and schedule the tasks. Meanwhile, at the fine-gained layer of parallelism, GPU accelerators undertake the computationally intensive computing of scoring functions and related conformation spatial transformations in a single docking task. The results of the experiments for the ligand-receptor binding process show that on a multicore server with GPUs the parallel program has achieved a speedup ratio as high as 45 times in flexible docking and as high as 54.5 times in semiflexible docking, and on a distributed memory system, the docking time for flexible docking and that for semiflexible docking gradually decrease as the number of nodes used in the parallel program gradually increases. The scalability of the parallel program is also verified in multiple nodes on a distributed memory system and is approximately linear.
For vision-based measurement, there are few research or professional tools for local contour positional errors of flexible automotive rubber strips. To support the automatic measurement of contour positional errors, a novel local contour registration and measurement method based on shape descriptors is proposed. In this method, a shape descriptor is proposed to find correspondence between a reference local contour and a desired local contour. First, a shape descriptor that includes the shape representation and restrictions of the local contour is extracted from the reference contour. Second, several tolerable shape descriptors for a desired actual local contour are constructed by adding some loosening factors to the ideal descriptor, and an angular similarity-based searching strategy is used to find the best actual local contour. Finally, from the matched local point sets, a quantitative calculation step provides the desired deviation values. This method is implemented in a sealing strip cross section measurement system, and numerous cross-sectional profiles are tested. The experimental results verify the stability and effectiveness of the proposed method. Important progress toward the automatic measurement of flexible products is demonstrated.
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