GPU-Accelerated Parallel Computer for Drug Discovery Applications

 描述：我们申请资金购买一台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政上可持续的支持。推荐的系统由20个Dell R720计算节点组成，每个节点都配备双NVIDIA K20特斯拉GPU、64 GB 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)提供给Vanderbilt药物发现研究界，该核心使用自助服务或收费咨询模式提供最先进的分子建模技术的全面菜单。这些领域的专业知识由高素质的科学家和专业人员提供，并在 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依赖于获得足够质量和数量的实验数据作为输入。因此，CSCBC密切合作，发挥辅助作用，提供免费的实验核心，如由CSB赞助的结晶学、核磁共振、EPR和冷冻-EM设备，以及由VICB赞助的高通量筛选和化学合成核心。多年来，GPU承诺大幅加速那些可以组织起来在简化但高度并行的体系结构上运行的软件应用程序，并且与传统的、更复杂的CPU相比，这样做的成本相对较低。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，以便它们能够利用GPU架构的优势，仍然是计算机科学中一个活跃的领域。然而，许多代码已经开始从研究和开发阶段进入“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，它有可能显著增加范德比尔特NIH资助的药物发现项目中确定的活性化合物的数量。BCL：CHEMINFO是一个基于GPU加速的基于配体的虚拟筛查应用程序，可以将高通量筛查分析中的活跃命中率提高30倍-60倍。BCL：CHEMINFO在GPU上的运行速度比在CPU上快150-300倍，但购买配备GPU的计算机只需2倍左右。建造建议的GPU加速计算机来支持药物发现项目，将大大减少与发现新的治疗感兴趣的分子相关的成本和时间。拟议的系统将在高级计算研究和教育中心(ACCRE)维护，该中心有大量24/7随叫随到的工作人员，他们拥有丰富的管理CPU和基于GPU的计算机集群的经验，以支持生物医学研究。ACCRE的技术支持将主要由机构配对基金支付，从而进一步降低这项提案的主要用户和次要用户的费用。

 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.