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 development

This 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.

National Science Foundation (NSF)
Division of Advanced CyberInfrastructure (ACI)
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Alejandro Suarez
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University of North Dakota
Grand Forks
United States
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