Finding a novel drug is a very long and complex procedure. Using computer simulations, it is possible to accelerate the preliminary phases by performing a virtual screening that filters a large set of drug candidates to a manageable number. This paper presents the implementations and comparative analysis of two GPU-optimized implementations of a virtual screening algorithm targeting novel GPU architectures. This work focuses on the analysis of parallel computation patterns and their mapping onto the target architecture. The first method adopts a traditional approach that spreads the computation for a single molecule across the entire GPU. The second uses a novel batched approach that exploits the parallel architecture of the GPU to evaluate more molecules in parallel. Experimental results showed a different behavior depending on the size of the database to be screened, either reaching a performance plateau sooner or having a more extended initial transient period to achieve a higher throughput (up to 5x), which is more suitable for extreme-scale virtual screening campaigns.

寻找新药是一个非常漫长且复杂的过程。使用计算机模拟，可以通过执行虚拟筛选来加速初步阶段，将大量候选药物筛选到可管理的数量。本文介绍了针对新型 GPU 架构的虚拟筛选算法的两种 GPU 优化实现的实现和比较分析。这项工作的重点是分析并行计算模式及其到目标架构的映射。第一种方法采用传统方法，将单个分子的计算分布在整个 GPU 上。第二种使用新颖的批处理方法，利用 GPU 的并行架构来并行评估更多分子。实验结果显示，根据要筛选的数据库的大小，会出现不同的行为，要么更快地达到性能平台，要么具有更长的初始瞬态期，以实现更高的吞吐量（高达 5 倍），这更适合极端规模虚拟筛选活动。