{"title":"基于fpga的charmm -势能最小化加速","authors":"Bharat Sukhwani, M. Herbordt","doi":"10.1145/1646461.1646462","DOIUrl":null,"url":null,"abstract":"Energy minimization is an important step in molecular modeling, with applications in molecular docking and in mapping binding sites. Minimization involves repeated evaluation of various bonded and non-bonded energies of a protein complex. It is a computationally expensive process, with runtimes typically being many hours on a desktop system. In the current article, we present acceleration of the energy evaluation phase of minimization using Field Programmable Gate Arrays. We project a multiple orders-of-magnitude speed-up over a single CPU core and a factor of 8 speed-up over our previous acceleration using an NVIDIA Tesla 1060 GPU.","PeriodicalId":59014,"journal":{"name":"高性能计算技术","volume":"99 1","pages":"1-10"},"PeriodicalIF":0.0000,"publicationDate":"2009-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"FPGA-based acceleration of CHARMM-potential minimization\",\"authors\":\"Bharat Sukhwani, M. Herbordt\",\"doi\":\"10.1145/1646461.1646462\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Energy minimization is an important step in molecular modeling, with applications in molecular docking and in mapping binding sites. Minimization involves repeated evaluation of various bonded and non-bonded energies of a protein complex. It is a computationally expensive process, with runtimes typically being many hours on a desktop system. In the current article, we present acceleration of the energy evaluation phase of minimization using Field Programmable Gate Arrays. We project a multiple orders-of-magnitude speed-up over a single CPU core and a factor of 8 speed-up over our previous acceleration using an NVIDIA Tesla 1060 GPU.\",\"PeriodicalId\":59014,\"journal\":{\"name\":\"高性能计算技术\",\"volume\":\"99 1\",\"pages\":\"1-10\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-11-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"高性能计算技术\",\"FirstCategoryId\":\"1093\",\"ListUrlMain\":\"https://doi.org/10.1145/1646461.1646462\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"高性能计算技术","FirstCategoryId":"1093","ListUrlMain":"https://doi.org/10.1145/1646461.1646462","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
摘要
能量最小化是分子建模的一个重要步骤,在分子对接和定位结合位点方面有着广泛的应用。最小化涉及对蛋白质复合物的各种键能和非键能的重复评估。这是一个计算成本很高的过程,在桌面系统上的运行时间通常长达数小时。在这篇文章中,我们提出了使用现场可编程门阵列加速最小化的能量评估阶段。我们计划在单个CPU核心上实现多个数量级的加速,并且比之前使用NVIDIA Tesla 1060 GPU的加速提高8倍。
FPGA-based acceleration of CHARMM-potential minimization
Energy minimization is an important step in molecular modeling, with applications in molecular docking and in mapping binding sites. Minimization involves repeated evaluation of various bonded and non-bonded energies of a protein complex. It is a computationally expensive process, with runtimes typically being many hours on a desktop system. In the current article, we present acceleration of the energy evaluation phase of minimization using Field Programmable Gate Arrays. We project a multiple orders-of-magnitude speed-up over a single CPU core and a factor of 8 speed-up over our previous acceleration using an NVIDIA Tesla 1060 GPU.