In drug discovery, molecular docking is the task in charge of estimating the position of a molecule when interacting with the docking site. This task is usually used to perform screening of a large library of molecules, in the early phase of the process. Given the amount of candidate molecules and the complexity of the application, this task is usually performed using high-performance computing (HPC) platforms. In modern HPC systems, heterogeneous platforms provide a better throughput with respect to homogeneous platforms. In this work, we ported and optimized a molecular docking application to a heterogeneous system, with one or more GPU accelerators, leveraging a hybrid OpenMP and OpenACC approach. The target application focuses on the virtual screening phases in the drug discovery process, and it is based on geometric transformations of the target ligands. We prove that our approach has a better exploitation of the node compared to pure CPU/GPU data splitting approaches, reaching a throughput improvement up to 25% while considering the same computing node.
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The GeoDock execution triggered an illegal access to the GPU memory when trying to transfer the private data structure.
When we enable the multi-threading with OpenMP, the CUDA managed memory fails. The manager tries to allocate the memory, from different threads, in the same area and returns a runtime error.
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This work has been partially funded by the EU H2020-FET-HPC program under the project ANTAREX—AutoTuning and Adaptivity appRoach for Energy efficient eXascale HPC systems (Grant Number 671623)
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Vitali, E., Gadioli, D., Palermo, G. et al. Exploiting OpenMP and OpenACC to accelerate a geometric approach to molecular docking in heterogeneous HPC nodes. J Supercomput 75, 3374–3396 (2019). https://doi.org/10.1007/s11227-019-02875-w
- Molecular docking
- High-performance computing