{"title":"High-throughput drug discovery on the Fujitsu A64FX architecture","authors":"Filippo Barbari, F. Ficarelli, Daniele Cesarini","doi":"10.1145/3636480.3637095","DOIUrl":null,"url":null,"abstract":"High-performance computational kernels that optimally exploit modern vector-capable processors are critical in running large-scale drug discovery campaigns efficiently and promptly compatible with the constraints posed by urgent computing needs. Yet, state-of-the-art virtual screening workflows focus either on the broadness of features provided to the drug researcher or performance on high-throughput accelerators, leaving the task of deploying efficient CPU kernels to the compiler. We ported the key parts of the LiGen drug discovery pipeline, based on molecular docking, to the Fujitsu A64FX platform and leveraged its vector processing capabilities via an industry-proven retargetable SIMD programming model. By rethinking and optimizing key geometrical docking algorithms to leverage SVE instructions, we are able to provide efficient, high throughput execution on SVE-capable platforms.","PeriodicalId":120904,"journal":{"name":"Proceedings of the International Conference on High Performance Computing in Asia-Pacific Region Workshops","volume":"4 11","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the International Conference on High Performance Computing in Asia-Pacific Region Workshops","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3636480.3637095","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
High-performance computational kernels that optimally exploit modern vector-capable processors are critical in running large-scale drug discovery campaigns efficiently and promptly compatible with the constraints posed by urgent computing needs. Yet, state-of-the-art virtual screening workflows focus either on the broadness of features provided to the drug researcher or performance on high-throughput accelerators, leaving the task of deploying efficient CPU kernels to the compiler. We ported the key parts of the LiGen drug discovery pipeline, based on molecular docking, to the Fujitsu A64FX platform and leveraged its vector processing capabilities via an industry-proven retargetable SIMD programming model. By rethinking and optimizing key geometrical docking algorithms to leverage SVE instructions, we are able to provide efficient, high throughput execution on SVE-capable platforms.
高性能计算内核可优化利用现代矢量处理器,这对于高效、及时地运行大规模药物发现活动,并与紧迫的计算需求所带来的限制相匹配至关重要。然而,最先进的虚拟筛选工作流程要么侧重于为药物研究人员提供广泛的功能,要么侧重于高通量加速器上的性能,而将部署高效 CPU 内核的任务留给了编译器。我们将基于分子对接的 LiGen 药物发现流水线的关键部分移植到富士通 A64FX 平台,并通过业界公认的可重目标 SIMD 编程模型利用其矢量处理能力。通过重新思考和优化关键的几何对接算法以利用 SVE 指令,我们能够在支持 SVE 的平台上提供高效、高吞吐量的执行。