MRI Collaborative: Development of a Data-Intensive Scalable Computing Instrument for High Performance Computing

提案编号：13- 38078 PI（s）：Chen，Yong; Gropp，William D.;作者声明：Smith，Philip W. Sun，Xian-He; Zhuang，YuInstitution：Texas Tech University职称：MRI协作/开发：面向高性能计算的数据密集型可扩展计算仪器项目建议：该项目旨在开发DICSI，一种全面的计算仪器，以弥补现有以计算为中心的HPC仪器对数据密集型应用的局限性，支持HPC系统设计，计算化学，生物技术和大气科学的五个大型研究项目。基于引入应用感知和并行执行范式概念的研究，该项目解决了研究原型和工程解决方案之间的巨大差距。对未来的应用程序，算法和仪器设计的影响是预期的，因为该仪器可以在支持数据密集型科学方面开辟新的研究领域，并可能重塑国家计算设施和一些机构采用的HPC仪器。除了传统的HPC计算节点外，DISCI还有一组专门设计的数据节点。数据节点提供原位数据处理，以减少数据移动和数据访问延迟，并在必要时作为“胖”计算节点进行动态配置，而计算节点的功能与传统仪器相同。这些数据节点与计算节点协同工作，共同为数据密集型HPC提供最佳系统性能。基于软硬件协同开发的原则，该仪器由两部分组成：DISCI系统架构和DISCI运行时软件。该系统架构构建了一个以数据为中心的HPC工具。运行时软件扩展了MPI（消息传递接口）和MPI-IO库，以支持数据节点及其相关的原位处理。该仪器将促进和促进化学动力学模拟、湍流模拟、大气数据同化和天气预报、计算生物学以及专业人员和高级人员进行的计算机系统等领域的研究活动。更广泛的影响：这个发展项目将使学术部门，跨学科单位，组织和多组织合作，以整合他们的发展，教育和推广工作。为了吸引代表性不足的学生进入DISCI发展，该机构将与各组织的机构项目协调。所获得的经验将被整合到本科和研究生课程和夏季定向培训，让学生参与发展。该教育计划专注于支持数据密集型HPC，并培养具有广泛包容性和全球竞争力的科学人才。该项目预计将为未来的国家HPC仪器提供途径，以支持数据密集型科学。此外，它可能对构建exascale HPC仪器产生直接影响。

Proposal #: 13-38078PI(s): Chen, Yong; Gropp, William D.; Smith, Philip W.; Sun, Xian-He; Zhuang, YuInstitution: Texas Tech University Title: MRI Collab/Dev.: Data Intensive Scalable Computing Instrument for High Performance ComputingProject Proposed:This project, developing DICSI, an all-around computing instrument that compensates the limitations of existing computing-centric HPC instruments toward data-intensive applications, supports five large research projects in HPC system design, computational chemistry, biotechnology, and atmospheric science. Based on research introducing the application-aware and decoupled-execution paradigm concept, the project addresses the big gap between research prototypes and the engineering solution. Impact on future applications, algorithms, and instruments design is expected since the instrument could open up new research areas in supporting data-intensive sciences and possibly reshape HPC instruments adopted in National Computing Facilities and some institutions. In addition to the conventional HPC compute nodes, DISCI has a set of specially designed data nodes. The data nodes offer in-situ data processing to reduce data movement and data-access delay and dynamic provisioning as 'fat' compute nodes when necessary, while the functionality of compute nodes remains the same as in conventional instrumentation. These data nodes work with compute nodes in concert and together they provide an optimum system performance for data-intensive HPC. Based on a hardware-software co-development principle, the instrument consists of two components: the DISCI system architecture and the DISCI runtime software. The system architecture builds an HPC instrument with a data-centric view. The runtime software extends the MPI (Message Passing Interface) and MPI-IO library to support data nodes and their associated in-situ processing. The instrument will enable and foster research activities in the areas of chemical dynamics simulation, simulations of turbulent flows, atmospheric data assimilation and weather forecasting, computational biology, and computer systems that PIs and senior personnel conduct. Broader Impacts: This development project will enable academic departments, cross-disciplinary units, organizations, and multi-organization collaborations to integrate their development, education, and outreach efforts. To attract underrepresented students into the DISCI development, the institution will coordinate with institutional projects at respective organizations. The experience gained will be integrated into undergraduate and graduate courses and summer orientation trainings to get students involved in the development. The education plan concentrates on supporting data-intensive HPC and training a broadly inclusive and globally competitive science workforce. The project is expected to provide the pathway to future national HPC instruments to support data-intensive sciences. Furthermore, it could have a direct impact on building exascale HPC instruments.