论文标题
猛禽：狂热的吞吐量计算
RAPTOR: Ravenous Throughput Computing
论文作者
论文摘要
我们描述了激进运动任务覆盖（Raptor）的设计，实现和性能。 RAPTOR可以在HPC平台上执行异构任务（即具有任意持续时间的功能和可执行文件），提供高通量和高资源利用率。 Raptor支持DOE国家虚拟生物技术实验室的高吞吐量虚拟筛查要求，以寻找Covid-19的治疗解决方案。猛禽已在$> 8000 $的计算节点上使用，以维持1.44亿/小时的对接命中率，并筛选$ \ sim $ \ sim $ 10 $^{11} $ ligands。据我们所知，执行任务的吞吐量率和汇总数量的倍数比文献中先前报道的大两个。猛禽代表着在筛选的库的规模方面改善计算药物发现的重要进展，并有可能快速生成训练数据以服务于最后一代对接替代模型。
We describe the design, implementation and performance of the RADICAL-Pilot task overlay (RAPTOR). RAPTOR enables the execution of heterogeneous tasks -- i.e., functions and executables with arbitrary duration -- on HPC platforms, providing high throughput and high resource utilization. RAPTOR supports the high throughput virtual screening requirements of DOE's National Virtual Biotechnology Laboratory effort to find therapeutic solutions for COVID-19. RAPTOR has been used on $>8000$ compute nodes to sustain 144M/hour docking hits, and to screen $\sim$10$^{11}$ ligands. To the best of our knowledge, both the throughput rate and aggregated number of executed tasks are a factor of two greater than previously reported in literature. RAPTOR represents important progress towards improvement of computational drug discovery, in terms of size of libraries screened, and for the possibility of generating training data fast enough to serve the last generation of docking surrogate models.
