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September 12, 2019

IDAS: ‘Automagic’ HPC With Training Wheels

Elizabeth Leake

High-performance computing (HPC) for research is notorious for having steep barriers to entry. For this reason, high-tech disciplines were early adopters, have used the most cycles and typically drove hardware and software purchasing decisions. But as more fields engage with data-intensive research and artificial intelligence (AI) workflows, data-center hardware and software landscapes are changing to meet a larger and more diverse community of practice.

The University of Iowa’s (UI) Interactive Data Analytics Service (IDAS) is an HPC resource that supports large-scale and collaborative data analytics workflows involving RStudio for R and Jupyter Notebook for Python (but not limited to Python). Applications via the IDAS interface look and feel like they do on a regular workstation, but with access to thousands of times the computing power. IDAS has its own HPC and graphics processing units (GPUs) and allows users to perform interactive data analysis tasks with applications used for machine learning and AI. In the future, the UI Research Services development team will assist with custom environments. There are plans to add a feature that allows researchers to access Iowa’s Argon supercomputer if more power is needed, or to purchase commercial cloud if locally-hosted HPC isn’t enough.

While RStudio for classroom use is available, RStudio commercial licensing terms changed and pricing increased midstream as IDAS was being developed. Therefore, until a more affordable and dynamic license distribution method is available, IDAS employs the free RStudio classroom license. In the future, a remote desktop feature will be added.

IDAS especially appeals to those who may not have used HPC in the past, but whose data-intensive research would benefit from advanced computational capabilities. Such disciplines are often referred to as, “the long tail of science,” and many have begun to engage with AI methodologies which make their work more computationally-intensive than ever. While they aren’t always the biggest compute consumers, their collective needs represent critical mass that IDAS accommodates quite well.

Experimentation within IDAS is safe with associated storage options that are appropriate for most classifications of data. Sensitive data should be evaluated for IDAS consideration on a case-by-case basis. “If you aren’t sure about your data’s classification, you can arrange a consultation with our compliance specialist,” said Research Services Director Danny Tang.

You can read the rest of the story at HPCwire: