GPU-Accelerated Parallel Computer for Life Sciences Research

用于生命科学研究的 GPU 加速并行计算机

• 批准号: 10415306
• 负责人: Jarrod Anson Smith
• 金额: $ 60万
• 依托单位: VANDERBILT UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10415306
• 关键词: Academic Medical Centers; Antibody Therapy; Architecture; Area; Arithmetic; Award; Biological Sciences; Biomedical Research; Code; Computer Systems; Computer software; Computers; Cryoelectron Microscopy; Data Science; Development; Disease; Drug Design; Education; Electron Microscopy; Equipment; Faculty; Funding; Image Analysis; Infrastructure; Institution; Investments; Libraries; Ligands; Machine Learning; Minor; Performance; Price; Research; Research Support; Running; Software Tools; Statistical Models; Structure; System; Systems Biology; Translating; United States National Institutes of Health; Universities; base; cost; cost effective; cryogenics; drug discovery; electron tomography; experience; high dimensionality; instrument; molecular dynamics; operation; parallel architecture; parallel computer; personalized medicine; programs; small molecule; structural biology; tool

Project Summary/Abstract We request funds to purchase a high-performance, GPU-accelerated parallel computer to provide financially sustainable support for computationally intensive NIH-funded biomedical research at Vanderbilt University (VU) and Vanderbilt University Medical Center (VUMC). The proposed system consists of 20 Exxact “TensorEX” compute nodes, each equipped with four nVidia A6000 “Ampere” GPUs, 512GB of RAM, dual 32- core AMD EPYC CPUs, an 8TB nVME SSD for scratch I/O, and a dual-port Mellanox 25Gbps network interface. GPUs have achieved wide acceptance and application in the high-performance technical computing (HPTC) and data science fields. Their simplified but highly parallel architectures yield processors that can do a staggering number of fundamental arithmetic operations simultaneously, at a tiny fraction of the complexity and therefore cost compared to a similar capability in a traditional CPU-based architecture. The intrinsic ~100- fold price/performance advantage has driven the authors of many important HPTC codes and libraries to modify their software to run on GPUs. There are now mature GPU implementations of many critical computationally- intensive software tools which are key to accelerating the pace of discovery in our research programs. The proposed computer system will support research software that is well-known to perform best when running on GPU coprocessors. Molecular dynamics (MD) codes such as AMBER and GROMACS are in high demand, to support our structure-based drug design (SBDD) and personalized structural biology (PSB) efforts. Cryogenic electron microscopy (Cryo-EM) and cryogenic electron tomography (Cryo-ET) image analysis tools such as RELION leverage the massive investments our institution has recently made and continues to make in these areas. Machine learning applications for ligand-based drug discovery, and high-dimensional statistical modeling tools for systems biology are examples of GPU-accelerated tools that are both developed and applied at Vanderbilt. Research in these areas (and more) would be significantly enhanced, accelerated, and more cost-effective with access to the proposed instrument. That will translate into direct positive impacts on the development of small molecule and antibody-based therapeutics, advances in personalized medicine, and critical advancements in our understanding of disease-related mechanisms at atomic through cellular scales. 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, including equipment from previous S10 awards. This faculty-driven approach to computing has been extremely successful, making ACCRE a critical component of the VU/VUMC research enterprise. Technical support at ACCRE is subsidized by institutional funds, further reducing costs and barriers for access to this infrastructure by the major and minor users on this proposal.

项目摘要/摘要 我们要求资金购买高性能的GPU加速平行计算机以提供 范德比尔特的财务可持续支持对计算密集的NIH资助生物医学研究 大学（VU）和范德比尔特大学医学中心（VUMC）。提出的系统由20个exxact组成 “ tensorex”计算节点，每个节点配备了四个NVIDIA A6000“ Ampere” GPU，RAM 512GB，Dual 32-- 核心AMD EPYC CPU，用于划痕I/O的8TB NVME SSD和双端口Mellanox 25Gbps网络接口。 GPU在高性能技术计算中已广泛接受和应用 （HPTC）和数据科学领域。他们简化但高度平行的体系结构产生可以做的处理器 仅仅是复杂性的很小一部分，大量的基本算术操作数量惊人 因此，与传统基于CPU的体系结构相似的功能相比，成本相比。内在〜100- 折叠价格/性能优势使许多重要的HPTC代码和库的作者修改 他们在GPU上运行的软件。现在有许多关键计算的成熟GPU实现 密集的软件工具是在我们的研究计划中加速发现空间的关键。 拟议的计算机系统将支持众所周知的研究软件，该软件在 在GPU协处理器上运行。分子动力学（MD）代码（例如琥珀色和gromacs） 需求，以支持我们基于结构的药物设计（SBDD）和个性化结构生物学（PSB）的工作。 低温电子显微镜（冷冻-EM）和低温电子断层扫描（Cryo-ET）图像分析工具 例如依赖利用我们的机构最近进行的大规模投资，并继续进行 这些区域。基于配体的药物发现和高维统计的机器学习应用 系统生物学建模工具是开发和应用的GPU加速工具的示例 在范德比尔特。这些领域（以及更多）的研究将得到显着增强，加速以及更多 具有成本效益，可以访问拟议的仪器。这将转化为直接的积极影响 开发小分子和基于抗体的疗法，个性化医学的进步以及 在我们对原子量表上与疾病相关机制的理解中的关键进步。 拟议的系统将维护在高级研究中心， 教育（Accre），拥有24/7大型的call员工，拥有丰富的CPU群集的经验 基于GPU的计算机支持生物医学研究，包括先前S10奖项的设备。 这种以教师为导向的计算方法非常成功，使其成为关键的组成部分 VU/VUMC研究企业的。 Accre的技术支持由机构资金补贴，进一步 在此提案中，降低了主要用户和次要用户进入此基础设施的成本和障碍。