MRI: Acquisition of FASTER - Fostering Accelerated Sciences Transformation Education and Research

该项目资助了一种可组合的高性能数据分析和计算仪器，名为FASTER（促进加速科学转化、教育和研究）。FASTER将在依赖人工智能和机器学习（AI/ML）技术、大数据实践和高性能计算（HPC）技术的科学领域实现变革性进步。FASTER平台通过利用一种可以动态分配资源以支持工作流的技术，消除了研究计算中的重大瓶颈。它将支持来自德州农工大学系统及其合作机构的研究人员。30%的FASTER计算资源也将由美国国家科学基金会（NSF） XSEDE（极端科学与工程发现环境）项目分配给全国的研究人员。FASTER的可组合接口允许它同时支持研究计算中的新兴和传统工作负载。受益于FASTER的变革性研究项目将包括人工智能/机器学习模型的开发、网络安全、健康人口信息学、基因组学、生物信息学、计算机辅助药物设计、农业科学、生命科学、石油和天然气模拟、新材料设计、气候建模、多尺度模拟、量子计算架构、生物医学成像、地球科学和量子化学。除了支持广泛的研究领域外，该项目还有助于代码开发、教育和NSF几个大想法的劳动力发展目标。FASTER采用了创新的液态可组合软硬件方法，结合了尖端技术，如最先进的cpu和gpu、基于NVMe（非易失性内存Express）的存储和高速互连。FASTER上的工作流将能够动态集成分解的gpu和NVMe，以组成单个节点，从而允许它们超越传统硬件限制进行扩展。可组合和可配置的技术将允许研究人员有效地利用资源，使更多的科学。从管理资源中收集的最佳实践将与社区共享。fast将协调三管齐下的努力，以培训、教育和推广为重点，有效地扩大对计算机的参与。FASTER将利用现有的努力来促进STEM（科学、技术、工程和数学），并在K-12、大学和专业水平上扩大对计算机的参与，从而在全国范围内产生变革性的影响。fast活动旨在扩大传统上代表性不足的群体对计算机和STEM的参与，特别是在少数族裔服务机构。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The project funds the acquisition of a composable high-performance data-analysis and computing instrument, named FASTER (Fostering Accelerated Scientific Transformations, Education, and Research). FASTER will enable transformative advances in scientific fields that rely on artificial intelligence and machine learning (AI/ML) techniques, big data practices, and high-performance computing (HPC) technologies. The FASTER platform removes significant bottlenecks in research computing by leveraging a technology that can dynamically allocate resources to support workflows. It will support researchers from across the Texas A&M University System and their collaborating institutions. Thirty percent of FASTER’s computing resources will also be allocated to researchers nationwide by the National Science Foundation (NSF) XSEDE (Extreme Science and Engineering Discovery Environment) program. FASTER’s composable interface allows it to simultaneously support both emerging and traditional workloads in research computing. Transformative research projects benefiting from FASTER will include the development of AI/ML models, cybersecurity, health population informatics, genomics, bioinformatics, computer-aided drug design, agricultural sciences, life sciences, oil and gas simulations, de novo materials design, climate modeling, multi-scale simulations, quantum computing architectures, biomedical imaging, geosciences, and quantum chemistry. In addition to supporting a wide-range of fields of research, the project contributes to code development, education, and the workforce development goals of several NSF Big Ideas.FASTER adopts the innovative Liqid composable software-hardware approach combined with cutting-edge technologies such as state of the art CPUs and GPUs, NVMe (Non-Volatile Memory Express) based storage, and thigh speed interconnect. Workflows on FASTER will be able to dynamically integrate disaggregated GPUs and NVMe to compose a single node, allowing them to scale beyond traditional hardware limits. The composable and configurable techniques will allow researchers to use resources efficiently, enabling more science. Best practices gathered from managing the resource will be shared with the community. FASTER will coordinate a three-pronged effort to effectively broaden participation in computing by focusing on training, education and outreach. FASTER will leverage existing efforts that promote STEM (Science, Technology, Engineering and Mathematics) and broaden participation in computing at the K-12, collegiate, and professional levels to have a transformative impact nationally. FASTER activities are designed to expand the participation of traditionally underrepresented groups in computing and STEM, particularly at minority-serving institutions.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.