GPU-Accelerated Parallel Computer for Drug Discovery Applications

用于药物发现应用的 GPU 加速并行计算机

• 批准号: 8826249
• 负责人: Jarrod Anson Smith
• 金额: $ 22.49万
• 依托单位: VANDERBILT UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-03-01 至 2017-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8826249
• 关键词: Adoption; Architecture; Area; BCL1 Oncogene; Biological Assay; Biology; Biomedical Research; Chemicals; Code; Communities; Complex; Computer Simulation; Computer software; Computers; Consult; Crystallography; Data; Education; Fee-for-Service Plans; Funding; Institutes; Ligands; Minor; Modeling; Molecular Models; Performance; Phase; Price; Production; Qualifying; Quantitative Structure-Activity Relationship; Research; Role; Running; Scientist; Services; System; Techniques; Therapeutic; Time; United States National Institutes of Health; Universities; Work; base; chemical synthesis; computer science; cost; drug discovery; experience; high throughput screening; interest; molecular modeling; parallel computer; programs; research and development; screening; structural biology; virtual

 DESCRIPTION: We request funds to purchase a GPU-accelerated parallel computer to provide financially sustainable support for ongoing drug discovery efforts at Vanderbilt University. The proposed system consists of 20 Dell R720 compute nodes each equipped with dual nVidia K20 Tesla GPUs, 64GB of RAM, and dual 6-core Intel Xeon 2620 CPUs. The system will be made available to the Vanderbilt drug discovery research community through our Computational Structural and Chemical Biology Core (CSCBC), which offers a comprehensive menu of state-of-the-art molecular modeling techniques using either a self-service or fee-for-service consulting model. Expertise in these areas is provided by highly qualified staff scientists and PIs and in the Center for Structural Biology (CSB) and the Vanderbilt Institute for Chemical Biology (VICB). Fundamental to our approach is the recognition that successful application of computer modeling is dependent upon obtaining a sufficient quality and quantity of experimental data as inputs. The CSCBC therefore works closely, in a supporting role, with complimentary experimental cores such as the Crystallography, NMR, EPR, and Cryo-EM facilities sponsored by the CSB, and the High Throughput Screening and Chemical Synthesis cores sponsored by the VICB. For several years, GPUs have promised to dramatically accelerate software applications that can be organized to run on a simplified but highly parallel architecture, and do so at a relatively low cost when compared with traditional, more complex CPUs which are easier to program but also relatively more expensive per unit of raw computing power. Redesigning and reprogramming compute codes so that they can take advantage of GPU architectures remains an active area in computer science. However, many codes have begun to move out of a research and development phase into a "production" phase with broad adoption by end-users who are eager to leverage the price/performance advantage offered by GPUs. Virtual screening with quantitative structure activity relationships (QSAR) is one such mature application that has the potential to dramatically increase the number of active compounds that are identified in NIH-funded drug discovery projects at Vanderbilt. BCL:CHEMINFO is a GPU-accelerated ligand-based virtual screening application that can enrich the number of active hits in high-throughput screening assays by 30x-60x. BCL:CHEMINFO runs between 150x-300x faster on GPUs vs. CPUs, but it only costs about 2x to purchase GPU-equipped computers. Building the proposed GPU-accelerated computer to support drug discovery projects will significantly decrease the costs and time associated with discovering new molecules of therapeutic interest. The proposed system will be maintained at the Advanced Computing Center for Research and Education (ACCRE), which has a large 24/7 on-call staff with extensive experience managing clusters of CPU and GPU- based computers in support of biomedical research. The technical support at ACCRE will be largely covered by institutional matching funds, further reducing costs to the major and minor users on this proposal.

 描述：我们请求资金购买一台 GPU 加速的并行计算机，为范德比尔特大学正在进行的药物发现工作提供财务上可持续的支持。拟议的系统由 20 个 Dell R720 计算节点组成，每个节点均配备双 nVidia K20 Tesla GPU、64GB RAM 和双 6 核 Intel Xeon 2620 CPU。该系统将通过我们的计算结构和化学生物学核心 (CSCBC) 向范德比尔特药物发现研究界提供，该核心使用自助服务或按服务收费的咨询模式提供最先进的分子建模技术的综合菜单。这些领域的专业知识由高素质的科学家和 PI 提供， 结构生物学中心 (CSB) 和范德比尔特化学生物学研究所 (VICB)。我们方法的基础是认识到计算机建模的成功应用取决于获得足够质量和数量的实验数据作为输入。因此，CSCBC 与 CSB 赞助的晶体学、NMR、EPR 和冷冻电镜设施等免费实验核心以及 VICB 赞助的高通量筛选和化学合成核心密切合作，发挥支持作用。多年来，GPU 一直致力于显着加速软件应用程序的运行速度，这些应用程序可以组织在简化但高度并行的架构上运行，并且与传统的、更复杂的 CPU 相比，其成本相对较低，传统的 CPU 更容易编程，但每单位原始计算能力的成本也相对较高。重新设计和重新编程计算代码，使其能够利用 GPU 架构仍然是计算机科学中的一个活跃领域。然而，许多代码已经开始从研发阶段进入“生产”阶段，并被渴望利用 GPU 提供的性价比优势的最终用户广泛采用。具有定量结构活性关系 (QSAR) 的虚拟筛选就是这样一种成熟的应用，它有可能显着增加范德比尔特国家卫生研究院 (NIH) 资助的药物发现项目中鉴定的活性化合物的数量。 BCL:CHEMINFO 是一款 GPU 加速的基于配体的虚拟筛选应用程序，可将高通量筛选测定中的活性命中数增加 30 倍至 60 倍。 BCL：CHEMINFO 在 GPU 上的运行速度比 CPU 快 150 倍到 300 倍，但购买配备 GPU 的计算机的成本仅为大约 2 倍。构建拟议的 GPU 加速计算机来支持药物发现项目将显着降低与发现具有治疗意义的新分子相关的成本和时间。拟议的系统将由研究和教育高级计算中心 (ACCRE) 维护，该中心拥有大量 24/7 待命的工作人员，他们在管理基于 CPU 和 GPU 的计算机集群以支持生物医学研究方面拥有丰富的经验。 ACCRE 的技术支持将主要由机构配套资金承担，进一步降低主要和次要用户在该提案中的成本。

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