利用OpenMP和OpenACC加速异构HPC节点中的分子对接Mini-App
Exploiting OpenMP & OpenACC to Accelerate a Molecular Docking Mini-App in Heterogeneous HPC Nodes
摘要：
在药物发现中，分子对接是负责估计分子与对接位点相互作用时的位置的任务。该任务通常用于在该过程的早期阶段对大型分子库进行筛选。考虑到候选分子的数量和应用的复杂性，这项任务通常使用高性能计算（HPC）平台来执行。在现代HPC系统中，异构平台相对于同构平台提供更好的吞吐量。在这项工作中，我们将分子对接应用程序移植并优化到异构系统中，使用一个或多个GPU加速器，利用混合的OpenMP和OpenACC方法。我们证明，我们的方法有一个更好的利用节点相比，纯CPU/GPU数据分割方法，达到吞吐量提高高达36%，同时考虑到相同的计算节点。
英文摘要：
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. 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 36% while considering the same computing node.
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