MRI: Acquisition of FlashTAIL - An All-NVMe Flash Storage Instrument for the Talon Artificial Intelligence & Machine Learning Cloud

这项重大研究仪器（MRI）奖支持收购名为FlashTAIL的数据存储仪器，该仪器将由北达科他州大学（UND）管理。该仪器将为使用图形处理单元（GPU）计算加速器的高性能计算环境提供高速数据传输，并专注于高级人工智能（AI）和机器学习（ML）应用。UND已承诺在未来五年内进行重大投资，以聘请新的教师，并形成一个计算研究人员集群，专门从事与大数据，人工智能和ML相关的领域。FlashTAIL将允许这些研究人员和UND的其他教师在大学部门的广泛范围内发展数据科学，人工智能和ML研究能力。参与研究人员提供的课程将吸引来自集群教师有合作关系的部门的学生。课程培训将结合由FlashTAIL仪器增强的GPU计算资源，为学生提供在尖端AI计算生态系统中工作的机会。通过这些途径，FlashTAIL将提高北达科他州研究的竞争力，促进多样化技术支持的区域劳动力的出现，并协助大学教师努力解决无人驾驶飞机系统（UAS），大数据和人工智能，农村健康，机器学习工作流通常需要使用大型数据集来训练预测算法，作为强大AI的基础。如果没有实现有效的数据管道，算法训练过程可能会从计算受限（计算边界）问题转变为较慢的输入/输出（I/O边界）问题。这种性能状态被称为“数据饥饿”，发生在处理器必须等待数据管道在ML训练继续之前提供更多数据时。训练过程越慢，可以完成的总体计算工作就越少，这使得高级AI解决方案更难以实现。收购FlashTAIL将通过装备UND的OpenStack云系统Talon，DataDirect Networks AI 400存储设备能够以40 GB/s -20 GB/s的并发高输入/输出每秒操作数（IOPS）数据速率将大型数据集流式传输到Talon的两个HPE Apollo 6500 NVLink GPU计算节点中的每一个（每个节点8个Nvidia Tesla smx 2 V100 GPU卡）。这将使UND能够支持需要大型数据集来正确训练预测ML算法的教师研究。这将提高UND研究开发计算机视觉AI应用程序的能力，这些应用程序用于大规模，大数据集的UAS收集的数据，用于野生动物调查，建筑和基础设施分析等项目，以及小规模，大数据集显微镜-该奖项由NSF办公室的MRI和数据项目管理并共同资助。电气、通信和网络系统（ECCS）部门内的高级网络基础设施（OAC）以及电子、光子和磁器件（EPMD）计划。该奖项反映了NSF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力进行评估而被认为值得支持。审查标准。

This Major Research Instrumentation (MRI) award supports the acquisition of a data storage instrument named FlashTAIL that will be managed by University of North Dakota (UND). This instrument will enable high speed data transfers for high-performance computing environments that use Graphical Processing Unit (GPU) computational accelerators and specialize in advanced Artificial Intelligence (AI) and Machine Learning (ML) applications. UND has committed to significant investments over the next five years to hire new faculty and form a cluster of computational researchers with specializations in areas relevant to Big Data, AI, and ML. FlashTAIL will allow these researchers and other faculty at UND to grow Data Science, AI, and ML research capabilities across a wide swath of university departments. Courses offered by the participating researchers will attract students from departments to which cluster faculty have collaborative relationships. Course training will incorporate the GPU computing resources enhanced by the FlashTAIL instrument, providing students with the opportunity to work within a cutting-edge AI computing ecosystem. Through these avenues, FlashTAIL will improve the competitiveness of North Dakota research, contribute to the emergence of a diverse tech-enabled regional workforce, and assist university faculty in their efforts to address the Grand Challenge objectives regarding unmanned aircraft systems (UAS), big data and AI, rural health, and energy sustainability.ML workflows often require the use of large datasets to train predictive algorithms that serve as the basis for robust AI. If an efficient data pipeline is not implemented, the algorithm training process can turn from a computationally restricted (compute bound) problem into a slower input/output (I/O bound) problem. This state of performance is known as "data starvation" and occurs when processors must wait for the data pipeline to deliver more data before the ML training can continue. The slower the training process, the less overall computational work can be completed, making advanced AI solutions more difficult to implement. Acquisition of FlashTAIL will serve to mitigate the impact of "data starvation" on ML workflows by equipping UND's OpenStack cloud system, Talon, with the DataDirect Networks AI400 storage instrument capable of streaming large datasets at a concurrent high input/output operations per second (IOPS) data rate of 40GB/s - 20GB/s to each of Talon's two HPE Apollo 6500 NVLink GPU-enabled compute nodes (8 x Nvidia Tesla smx2 V100 GPU cards per node). This will allow UND to support faculty research that requires large datasets to properly train predictive ML algorithms. This will improve the ability of UND research to develop computer vision AI applications for large-scale, large-dataset UAS-collected data for projects such as wildlife surveys and building and infrastructure analysis, and as well as small-scale, large-dataset microscopy-collected data for evaluating 3D biointerfaces for biological tissue developmentThis award is managed by and jointly funded through the MRI and Data programs within the NSF Office of Advanced Cyberinfrastructure (OAC) and the Electronics, Photonics and Magnetic Devices (EPMD) program within the Division of Electrical, Communications and Cyber Systems (ECCS).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.